INFORMATIONAL, TRANSFORMATIONAL AND TRANSPORTATIONAL ADVERTISING STRATEGIES By Tao Deng A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Information and Media – Doctor of Philosophy 2019 ABSTRACT INFORMATIONAL, TRANSFORMATIONAL AND TRANSPORTATIONAL ADVERTISING STRATEGIES By Tao Deng Traditionally, advertising strategy can be categorized as either informational or transformational. Nonetheless, scholars have proposed a unique pathway to persuasion through narrative transportation but devote little attention to developing its application in advertising, despite its popularity in many forms of advertising today. This dissertation proposes an overlapping model of advertising strategies by adding transportational strategy as an additional strategy that is conceptually different from the informational and transformational strategies. Study 1 of this dissertation tested advertising evaluations of informational, transformational, and transportational strategies using fictional ads focused on one of the three advertising strategies for two medium involvement products. Findings of study 1 indicate that a transportational strategy yields more positive results than the other two strategies in terms of ad attitude, brand attitude and purchase intention. Study 2 further compared ads that focused on one particular strategy with ads that used a mix of different strategies. Findings indicate that ads with a focused strategy result in more positive outcomes than ads with mixed strategies in terms of ad and brand attitude and purchase intention. Finally, study 3 tested a transportation mediation framework using critical thoughts and affective responses as mediators in real-world video ads created by large global brands. Findings show that the addition of critical thoughts and affective responses as mediators significantly increases the model’s explanatory power. Keywords: informational, transformational, transportational, narrative transportation, advertising Copyright by TAO DENG 2019 ACKNOWLEDGEMENTS I would like to express my deepest appreciation to Dr. Dawn Pysarchik, my dissertation committee chair for her overwhelming support to my dissertation, my doctoral study, and my teaching at Michigan State University. Thank you, Dr. Dawn Pysarchik, for your sincere and selfless devotion to guide me through my PhD career, your insightful advice and wisdom in teaching, and the countless hours you spent in improving my dissertation research. I am tremendously fortunate to have had such a wonderful advisor. The past five years have been an unforgettable journey. I am grateful to my dissertation committee members, Dr. Patricia Huddleston, Dr. Hairong Li and Dr. Carrie Heeter, for their support, suggestions, and encouragement. I have learned so much from your teaching and constructive advice. I would also like to extend my gratitude to the exceptional faculty in the ADPR department. Special thanks to Dr. Saleem Alhabash, for the amazing mentorship in research and publication, and for guiding me through so many interesting research projects through the MAP Lab. To Dr. Anastasia Kononova, for her support and guidance in our research projects. And to Dr. Jingbo Meng and Dr. Wei Peng, for their support and trust in our research collaboration. Further, I owe a debt of gratitude to my mentor and Master thesis chair at Marquette University, Dr. Jean Grow, who provided enormous support during my academic career. Thank you for the encouragement and friendship. You are my inspiration. A special thank you to Jiaying Hu, my editor and lovely wife, who is always there when I need editing and food. You are the secret behind my success. Now, please proofread this. iv TABLE OF CONTENTS LIST OF TABLES ........................................................................................................................ vii LIST OF FIGURES ....................................................................................................................... ix KEY TO ABBREVIATIONS ......................................................................................................... x CHAPTER 1 ................................................................................................................................... 1 INTRODUCTION ....................................................................................................................... 1 CHAPTER 2 ................................................................................................................................... 5 LITERATURE REVIEW ............................................................................................................ 5 Emotion in Advertising ........................................................................................................... 5 The Affective and Cognitive Dichotomy ................................................................................ 7 The Concept of Advertising Strategy .................................................................................... 10 Informational and Transformational Strategies ..................................................................... 11 Analytical Persuasion and Narrative Persuasion ................................................................... 14 Narrative Transportation........................................................................................................ 17 Narrative Transportation in Advertising................................................................................ 19 Operational Definitions of the Three Strategies .................................................................... 22 Overlapping Model of Three Advertising Strategies ............................................................ 23 CHAPTER 3 ................................................................................................................................. 25 STUDY 1 – COMPARISON OF THE THREE STRATEGIES .............................................. 25 Methods ................................................................................................................................. 25 Design ............................................................................................................................... 25 Pretests .............................................................................................................................. 26 Participants ........................................................................................................................ 34 Procedures ......................................................................................................................... 36 Independent Variables ...................................................................................................... 36 Dependent Variables ......................................................................................................... 36 Control Variables .............................................................................................................. 37 Data Analysis .................................................................................................................... 37 Manipulation Checks ........................................................................................................ 38 Results ................................................................................................................................... 39 Hypothesis 1 to 3 .............................................................................................................. 39 Hypothesis 4...................................................................................................................... 40 Discussion .............................................................................................................................. 42 CHAPTER 4 ................................................................................................................................. 46 STUDY 2 – SINGLE STRATEGY VS. MIXED STRATEGY ............................................... 46 Methods ................................................................................................................................. 48 v Pretests .............................................................................................................................. 48 Participants ........................................................................................................................ 50 Procedures ......................................................................................................................... 53 Stimuli ............................................................................................................................... 53 Independent Variables ...................................................................................................... 54 Dependent Variables ......................................................................................................... 54 Control Variables .............................................................................................................. 55 Data Analysis .................................................................................................................... 56 Manipulation Checks ........................................................................................................ 56 Results ................................................................................................................................... 58 Hypothesis 1...................................................................................................................... 59 Hypothesis 2...................................................................................................................... 60 Research Question ............................................................................................................ 61 Discussion .............................................................................................................................. 62 CHAPTER 5 ................................................................................................................................. 66 STUDY 3 – TESTING MEDIATORS WITH REAL VIDEO ADS ........................................ 66 Methods ................................................................................................................................. 69 Pretest ................................................................................................................................ 69 Participants ........................................................................................................................ 70 Procedures ......................................................................................................................... 72 Stimuli ............................................................................................................................... 72 Independent Variable (IV) ................................................................................................ 73 Mediators (M) ................................................................................................................... 74 Dependent Variables (DV) ............................................................................................... 75 Control Variables .............................................................................................................. 75 Data Analysis .................................................................................................................... 76 Results ................................................................................................................................... 77 Hypothesis 1...................................................................................................................... 77 Hypothesis 2...................................................................................................................... 80 Hypothesis 3...................................................................................................................... 83 Discussion .............................................................................................................................. 86 CHAPTER 6 ................................................................................................................................. 90 GENERAL DISCUSSION AND CONCLUSIONS ................................................................. 90 Managerial Implications ........................................................................................................ 92 Limitations and Future Study ................................................................................................ 94 Conclusions ........................................................................................................................... 95 APPENDICES .............................................................................................................................. 96 Appendix A – Instrument for Study 1 ....................................................................................... 97 Appendix B – Instrument for Study 2 ..................................................................................... 104 Appendix C – Instrument for Study 3 ..................................................................................... 109 REFERENCES ........................................................................................................................... 114 vi LIST OF TABLES Table 1: Participants were randomly assigned to one of 12 possible condition orders. ............... 26 Table 2: Scales used in the study 1 pretest 1. ............................................................................... 27 Table 3: Perceived strategy used and dependent variable measures. ............................................ 30 Table 4: Informational and transformational scale items modified from Puto & Wells (1984). .. 32 Table 5: Study 1 pretest 2 pairwise comparisons of perceived strategy used in the conditions. .. 33 Table 6: Categorization of study 1 stimuli using Taylor’s Six Segment Message Strategy Wheel (Taylor, 1999). .............................................................................................................................. 34 Table 7: Demographics of the study 1 participants (N = 240). ..................................................... 35 Table 8: Descriptive results of the dependent variables of the two product categories, and one- sample t-test of the dependent variables averaged across the two product categories. ................ 40 Table 9: Cumulative percentages of responses scored above the mid-point values for the three dependent variables. ...................................................................................................................... 40 Table 10: Pairwise comparisons between advertising strategies. ................................................. 42 Table 11: Perceived levels of advertising strategy used in the study 2 pretest ads. ..................... 49 Table 12: Categorization of study 2’s pretest ad2 using Taylor’s Six Segment Message Strategy Wheel (Taylor, 1999). ................................................................................................................... 50 Table 13: Demographics of the study 2 participants (N = 280). ................................................... 52 Table 14: Differences of perceived advertising strategy used in the study 2 stimuli. .................. 57 Table 15: Descriptive results of study 2 (N = 280). ...................................................................... 59 Table 16: Pairwise comparisons between pairs of study conditions (N = 280). ........................... 60 Table 17: Perceived levels of transportational strategy used in the study 3 pretest ads. .............. 70 vii Table 18: Demographics of the study 3 participants (N = 200). ................................................... 71 Table 19: Critical thoughts mediation model results for the three study stimuli (N = 200). ........ 78 Table 20: Affective responses mediation model results for the three study stimuli (N = 200). ... 82 Table 21: Descriptive results of study 3 main study (N = 200). ................................................... 83 Table 22: Pairwise comparisons across the three ads in study 3 for ad attitude, brand attitude and purchase intention (N = 200). ....................................................................................................... 85 viii LIST OF FIGURES Figure 1: Nike 1992 magazine print ad featuring Michael Powell, holder of the long jump world record (Vintageadbrowser, n.d.). .................................................................................................... 2 Figure 2: The conceptualization of advertising strategy, advertising execution and advertising appeal, based on Mueller’s (2011) definitions. ............................................................................. 11 Figure 3: Taylor’s (1999) Six Segment Message Strategy Wheel model. .................................... 15 Figure 4: Overlapping model of advertising strategies. ................................................................ 24 Figure 5: Study 1 stimuli ads per condition. ................................................................................. 29 Figure 6: Estimated means of the dependent variables for the three conditions across the two product categories (NRunning shoe = 79, NHeadphone = 79). .................................................................. 41 Figure 7: Mean levels of perceived advertising strategy used in the study 1 stimuli when combining the two product categories. ......................................................................................... 45 Figure 8: Study 2 stimuli ads per condition. ................................................................................. 54 Figure 9: Mean levels of perceived advertising strategy used in the study 2 stimuli. .................. 58 Figure 10: Estimated means of the dependent variables for the four conditions (N = 280). ........ 60 Figure 11: Transportational process mediation framework. ......................................................... 68 Figure 12: Screenshots of study 3 stimuli. .................................................................................... 73 Figure 13: Mediation path model. ................................................................................................. 77 Figure 14: Estimated means of perceived transportational strategy, ad attitude, brand attitude and purchase intention for the three ads (N = 200). ............................................................................. 85 ix KEY TO ABBREVIATIONS INFO Informational Strategy TRSF Transformational Strategy TRSP Transportational Strategy x CHAPTER 1 INTRODUCTION In determining the “what to say” and “how to say it” their advertisements, marketers have historically applied informational or transformational strategies. Decisions are centered on if their goal is to present the facts of the products/services or to change consumer attitudes. Little do they know that there is a third way to persuasion—a unique pathway that is distinct from the traditional dual-processing models (e.g., the elaboration likelihood model and the heuristic- systematic model)—that is, transportation via narrative. By definition, narrative transportation persuades an individual by embedding him/her in a story world while evoking strong emotions with the use of a dramatic plot. The individual then becomes highly involved in the narrative and finds himself/herself accepting not only the explicit but also the implicit assertions presented in the story, no matter if it is factual or fictional. Strategies that employ a story in advertising are very common. Commercial brands have long been using inspirational stories to associate their brands/products with a certain value. For example, T-Mobile’s “Jake’s Dad” one-minute TV commercial tells a moving story about how Jake reconciles with his father at the event of his baby being born, conveying a subtle message about connecting people and families via T-Mobile service ([T-Mobile], 2019). Nike is known to have famous athletes tell their inspirational stories in its ads, which it has been using in early print and now in digital (see Figure 1; Vintageadbrowser, n.d.). Even political advertising has adopted the narrative approach. Recently, digital political advertising has moved away from the notorious negative advertising approach and started using personal stories to show a different side of a candidate. The so-called “empathy” political ads show how a candidate can empathize by presenting her/his own life struggles or share a touching story about her/his family oftentimes 1 with high emotional music and cinematic pictures (Wall Street Journal, 2018). One can even argue that the popular advertising forms—native advertising and product placement—are applications of narrative advertising that embed branded products into a narrative, be it a TV show, film or sponsorship. Figure 1: Nike 1992 magazine print ad featuring Michael Powell, holder of the long jump world record (Vintageadbrowser, n.d.). 2 Stories/narrative are no strangers to marketers. Commercial brands have long been using inspirational stories to associate their brands/products with a certain value(s). Yet, stories/narrative has not received much scholarly attention mainly because the researchers that coined narrative transportation explicitly denied its application to advertising. According to Green and Brock (2000), immersion into the story world is a necessary condition to be transported. As claimed by Green and Brock, the obvious persuasive intent of advertising is to interrupt the immersion process, thus, deterring narrative transportation from taking effect. Since this conceptual obstacle does not stop marketers from using a story in advertising, it should not discourage academic research on the phenomenon. Thus, in this dissertation, in addition to the two commonly known advertising strategies (informational and transformational), the author proposes a transportational strategy that uses narrative transportation to persuade. In advertising, informational and transformational represent two major types of message strategies. Informational strategy aims to provide meaningful facts about the product or service. It focuses on directly promoting the features, attributes, or benefits of the product itself (Cutler, Thomas, & Rao, 2000). Transformational strategy, in contrast, focuses on the affective elements such as feelings and experiences and seeks to move consumers emotionally to change their attitude or increase their product liking (Puto & Wells, 1984). The two strategies may not be mutually exclusive, however, an ad can contain both strategies. Previous studies have put significant efforts into identifying the typologies of both strategies and testing them in cross- cultural contexts (Chiou, 2002; Cutler et al., 2000; Golan & Zaidner, 2008). This dissertation benefits the existing research in informational and transformational advertising by introducing a unique form of persuasion that is also grounded in the emotional and experiential side of media consumption—narrative transportation. It refers to a distinct 3 mental process of being transported into a narrative world when the message receiver is immersed in the text and thus being persuaded (Green & Brock, 2000). The transportation process consists of an integrative combination of attention, imagery, and emotions (Green & Sestir, 2017). Despite being a distinct route to persuasion, narrative transportation has long been an exclusive concept in media narrative related studies and receives little attention in advertising research (Phillips & McQuarrie, 2010). Following the small but dedicated pool of research that examines narrative transportation in advertising (Dunlop, Wakefield, & Kashima, 2008; Escalas, 2004; Phillips & McQuarrie, 2010; Zheng, 2010), it is argued that if executed effectively, narrative transportation can provide positive results for advertisers. This dissertation, thereby, investigates the transportational effects of narrative transportation in advertising. The objectives of the research are to test the effectiveness of narrative transportation along with informational and transformational strategies regarding ad attitude, brand attitude and purchase intention outcomes. To achieve this goal, this dissertation first offers a review of relevant literature on the application of the three strategies (informational, transformational and transportational), and proposes a new overlapping model that combines different advertising approaches. Then, study 1 tests advertisements applying each of the three distinct strategies in print ads for two medium involvement products. Further, to test the advertising effects of a single strategy vs. an overlapping model, study 2 is designed to compare ads that are perceived to use one of the three advertising strategies with ads that are perceived to use multiple strategies. Finally, to further examine the advertising transportation process and to improve external validity, study 3 is designed to test potential mediators of transportation using real-life video ads that are perceived to use a transportational strategy. Study 3 also tests transportational strategy’s various effects at different levels (low, medium, and high). 4 CHAPTER 2 LITERATURE REVIEW Emotion in Advertising Humans are considered to be complex social beings. According to sociologist Jonathan Turner (2007), emotions are the driving force behind human social organization. All our social commitments, social structures, and social interactions are formed, guided and rejected by emotions. Humans are the “most emotional animals on earth” (Turner, 2007, p. 1). Thus, emotions play a vital role in all human affairs, including advertising. The study of emotion has long been a central topic in the psychology domain. Psychologists have found that emotions play an important role in determining human behaviors and actions (Carlson, Heth, Miller, Donahoe, & Martin, 2009). However, in the advertising field, emotion was not always a subject worthy of scholarly attention, at least in the earlier years. The first formal advertising model—the attention, interest, desire, and action model (AIDA) (Strong, 1925) introduced emotion as a construct of the affect/desire component. Following this conception, the advertising process begins with generating awareness (attention) and cognitive processing (interest), then leads to affect response (desire), and ends with a behavior (action). The widespread adoption of this model has led to a proliferation of “hierarchy of effects” models that dominated the advertising literature for decades (Lavidge & Steiner 1961). As a result, emotion has been widely conceptualized as post-cognitive, that is, to occur only after considerable cognitive operations have been completed. Thus, much of the research focus has been turned to the information-oriented decision-making models while the emotional side has been neglected (Holbrook & Hirschman, 1982). 5 Early studies on emotional appeals in advertising focused on the motivational processes (see Dichter, 1960 and Martineau, 1957), but were largely overshadowed by the information processing perspective that dominated the marketing domain at that time (Holbrook & Hirschman, 1982). The information processing theory holds that customer behavior is purposive and goal-oriented, that the customer is an active decision maker involved in searching, attending, perceiving and evaluating information to make brand choices (Bettman, 1979). In this regard, advertising serves an informational role that mainly supplies facts consumers can use to make informed purchase decisions. Emotion did not find its rightful place in advertising, and more generally in marketing, until the 1980s. The change of focus was led by social psychologist Robert Zajonc (1980) who argued that decisions can be made based as much on affective/emotional factors as on rational/factual processing. In his work, Zajonc (1980) shows evidence that indicates affective judgments can occur independently from and prior to cognitive operations. Thus, in his concluding remark, Zajonc (1980, p. 172) points out that “[i]f we stop to consider just how much variance in the course of our lives is controlled by cognitive processes and how much by affect, and how much the one and the other influence the important outcomes in our lives, we cannot but agree that affective phenomena deserve far more attention than they have received.” Since Zajonc’s work, emotion has gained renewed emphasis and is playing a crucial role in determining consumer responses to advertising (Batra & Ray, 1986; Edell & Burke, 1987; Holbrook & O’Shaughnessy, 1984; Holbrook & Batra, 1987). For example, in light of the reemergence of emotion in advertising research, Holbrook and O’Shaughnessy (1984) provide a conceptual framework to distinguish emotion from other motivational and affective constructs in the emotional process. Influential works from Batra and Ray (1986), Edell and Burke (1987), 6 and Holbrook and Batra (1987) have found emotions to be important mediators of cognitive and behavioral responses to advertising. Recent research in neuroscience and cognitive neuroscience have embraced emotion as an important construct to explain human behaviors (see Adolphs, Tranel, Damasio, & Damasio, 1995; Damasio, 1994; Davidson, 1993; and Davis, 1992, for example). Using fMRI, Bakalash and Riemer (2013) found that ad memorability is highest among ads that elicit a high level of emotional arousal as evident in participants’ self-report as well as the activations in the amygdala region of the brain (which is often associated with emotional memory formation). Using a face detection computer program, McDuff, Kaliouby, Cohn and Picard (2015) coded over 12,000 facial responses from over 1,200 people to 170 ads and found that ad liking is shown by eliciting positive expressions and is positively associated with purchase intent. Furthermore, Hamelin et al. (2017) applied program-controlled facial recognition to measure unconscious emotions evoked during exposure to safe driving PSAs. They found that high emotional PSAs tend to generate higher and more durable safe driving attitudes than low emotional ones (Hamelin, El Moujahid, & Thaichon, 2017). Based on these insights, researchers from various disciplines have recognized the important role emotion plays in rational thinking and human behavior. The same trend can also be seen in advertising research in recent years. The Affective and Cognitive Dichotomy The advertising process has long been viewed as an intermediate model, that is, for an ad to elicit response or change behavior it has to show some types of mental effect first, consciously or unconsciously. Among the advertising literature, there are two major intermediate advertising effects: the affection or “feeling” type, and the cognition or “thinking” type. These mediators can influence and change advertising response. 7 The advertising literature often makes a distinction between affective and cognitive responses and thus creates a false dichotomy. This dichotomy distinguishes the two processes and views them as independent and only differ in temporal activation. In fact, evidence from neuroscience indicates that complex cognitive–emotional brain functions are dynamically coalesced in a network of brain areas, none of which should be recognized as specifically or distinctively affective or cognitive (Pessoa, 2008). It seems that affection and cognition are interconnected as higher cognitive functions activate them simultaneously and interactively. To further review the empirical findings of the affective and cognitive responses to advertising, let us first go over two opposite classes of models: the rational-only cognitive information models and the affective-only pure affective models (Vakratsas & Ambler, 1999). Cognitive Information Models. This class of models is anchored by rational consumer choice that stems from classic economic models of human decision-making (see Simon, 1959). Standard economic models tend to maximize the impact of rational cognition on people’s decision-making behavior while neglecting the effects of emotions, essentially viewing humans as perfectly rational machines. In these models, advertising, thus, exists merely as information sources and exerts no power in terms of affecting consumer preference. Studies adopting the cognitive information models classify goods/attributes into search and experience categories (Nelson, 1970). If product attributes are readily available prior to purchase, the product is categorized as a search product; whereas if product attributes cannot be assessed until after purchase and use, then, the product is an experience product (Klein, 1998). Hoch and Ha (1986) propose that advertising works better for experience rather than search products especially when the product category is ambiguous. The rationale maintains that information about the experience 8 product provided by ads is not as readily available for the consumer to check as the information about a search product (e.g., price, attribute) (Nelson, 1974). The limitations of the cognitive information model are effectively illustrated by empirical findings from studies using psychophysiological measures. For example, Aaker, Stayman and Hagerty (1986) found that emotional responses as evidenced by skin responses is positively associated with ad liking. Hunt (1989) found that heart rate, electromyography, and cardiac pulse wave are linked to ad recall. Pure Affective Models. Unlike the cognitive information models, the pure affective approach ignores cognition but focuses on ad-elicited affective responses such as liking, feelings, and emotions (Aaker et al., 1986). Researchers adopting this approach argue that the effectiveness of advertising does not necessarily rely on the information it provides but can also be based on the emotion it evokes (Holbrook & Batra, 1987). For example, in their seminal work, Mitchell and Olson (1981) pointed out that beliefs about the product attributes are not the only mediators of attitude formation and change, attitude toward the ad also mediates brand attitudes and purchase intentions. In addition, Brown and Stayman (1992) also found robust relationships between ad attitude and brand attitude in a meta-analysis of 43 published studies. Discounting cognition, however, means that the pure affective models are hard to measure. Cognitive bias tends to emerge with traditional self-report methods that rely on cognitive processing to interpret questions and form responses. Various methods are developed to combat cognitive bias, such include a series of psychophysiological methods that collect heart rate, pulse, electrodermal activity, and facial muscle activation data. Despite the advancement in measurement, affective models are rather limited in explaining advertising effects (Vakratsas & Ambler, 1999) because, like many other stimuli, advertisements evoke both cognitive and 9 affective responses (Holbrook & O’Shaughnessy, 1984). Nevertheless, this line of research on emotion has demonstrated the importance of affective responses in advertising (Vakratsas & Ambler, 1999). The Concept of Advertising Strategy Recent studies investigating informational and transformational advertising have adopted labels such as informational/transformational strategies (Cui, Liu, Yang, & Wang, 2013; Lee, Taylor, & Chung, 2011), execution styles (Cutler et al., 2000), and appeals (Chiou, 2002; Hornik, Ofir, & Rachamim, 2016; Naylor, Kleiser, Baker, & Yorkston, 2008). Due to the subtle differences in the operationalizations of the above terms among advertising literature, it is important to distinguish the terminologies and clarify their conceptual differences. This dissertation adopts Mueller’s (2011) definition of advertising strategy, advertising execution, and advertising appeal. Advertising strategy concerns what the advertising message will say or communicate. Frazer (1983) defines advertising strategy as “a policy or guiding principle which specifies the general nature and character of messages to be designed. Advertising strategy states the means selected to achieve the desired audience effect over the term of the campaign” (p. 36). That is, the “what to say” element of the advertising “five W’s and a H” (what to say, when to say, where to say, say to whom and why, and how to say it). Advertising execution can be seen as the “how to say it” element. Thus, advertising strategy adopted for a specific campaign guides the execution, that is, the selection of advertising appeal and execution style for a specific ad (Kaynak, 1989). Advertising appeal is a broad concept that concerns the approaches advertisers use to motivate potential consumers to buy a product or change their attitude toward a product (e.g., humor, sex, patriotism; Belch & Belch, 10 1993). In this sense, the decision of choosing the advertising appeal for an ad is determined by the strategy adopted (Mueller, 2011). See Figure 2 for the conceptual connections of the three concepts. This dissertation investigates the effects of different advertising strategies, thus, the “what to say” aspect of the advertising. Following the above conceptual definitions, the author adopted the informational/transformational strategies terminology. Figure 2: The conceptualization of advertising strategy, advertising execution and advertising appeal, based on Mueller’s (2011) definitions. Advertising Strategy Advertising Execution Advertising Appeal The strategic decision of “what to say” in an ad. The decision of “how to say it”. The use of different Guided by the advertising strategy. appeals in an ad. Part of the execution decision. Informational and Transformational Strategies Advertising message strategies can be categorized into two major types: informational and transformational, depending on the amount of cognitive and affective elements being used (Puto & Wells, 1984). Informational strategies have been defined as providing factual and relevant information to consumers in a clear and logical way to aid brand/product evaluation and purchase decision (Puto & Wells, 1984). This type of strategy is typically product-oriented and focuses mainly on product attributes and features. Transformational strategies are based on the emotional and hedonic side of consumption that seeks to link the product or brand to a unique set of psychological characters (Puto & Wells, 1984). Transformational ads attempt to “transform” the use experience in an emotional way to achieve better evaluation. In other words, transformational strategies do not rely on product 11 attributes or benefits but seek to make the consumer feel good about a product or brand by associating the product/brand with positive emotions (Cutler et al., 2000). Furthermore, scholars argue that transformational ads not only form the emotional associations but also define and project the use experience (Deighton, 1988). According to Puto and Wells (1984), informational and transformational strategies are not mutually exclusive but can be used together, which means that an ad can contain certain levels of each strategy. The conceptualization of informational and transformational strategies is widely used in studying consumer response to advertising (see Naylor et al., 2008) and content analysis of differences in advertising across national markets (see Chiou, 2002; Cui et al., 2013; Cutler et al., 2000). In a field study of retail experience, Naylor and colleagues (2008) found that transformational advertising is effective toward enhancing hedonic and symbolic benefits but has no effect on functional benefits. Using an extended model of informational vs. transformational message strategy developed by Taylor (1999), Golan and Zaidner (2008) analyzed 360 viral ads and found that the majority of the viral ads use transformational strategies that include themes like humor and sexuality. Similarly, Lee et al. (2011) showed that financial service organizations moved away from transformational strategies in favor of informational strategies during the economic crisis between 2005 and 2009. The authors assert that the financial crisis prompted organizations to become more concerned with providing clear market information to reduce confusion and uncertainty (Lee et al., 2011). For the last two decades, scholars have developed similar models from the same theoretical vein of informational/transformational. Mueller (1987) defines a hard-sell approach as one that induces rational thinking, and a soft-sell approach as one that induces affective (feeling) reaction. Based on Rossiter and Percy’s (1987) definition of informational and 12 transformational advertising, Johar and Sirgy (1991) propose two general advertising approaches: utilitarian (functional) and value-expressive (image). The utilitarian approach highlights the functional features of the product/brand and is influenced by functional congruity. The value-expressive strategy involves building an image and is mediated by self-congruity (Johar & Sirgy, 1991). Other scholars have developed extended categorization systems of the informational and transformational dichotomy. For example, Moriarty’s (1987) ad appeal topology system groups identification, description, comparison and demonstration into a literal (informational) category, while association, metaphor, storytelling and aesthetic are classified into a symbolic (transformative) category. Applying this topology, Culter et al. (2000) found it useful to categorize advertisements from different media types. Another classification scheme developed by Laskey, Day and Crask (1989) has 11 categories with six informational and five transformational strategies. In a later study, Laskey, Fox and Crask (1995) found that a transformational strategy is superior to informational in persuasion but is relatively ineffective for key message comprehension. In an analysis of Taiwanese TV commercials, Chiou (2002) found that Laskey et al.’s typology is effective only in explaining purchase intention while Mueller’s (1987) soft-and hard-selling typology is effective in explaining ad liking and purchase intention. Finally, Taylor (1999) introduced a six-segment message strategy wheel as a comprehensive model for analyzing advertisements. The wheel distinguishes between informational (the transmission view) and transformational (the ritual view) with each side containing three segments: ration, acute need, and routine on the informational side, and ego, social, and sensory on the transformational side (Taylor, 1999). Taylor’s (1999) model had been tested in a number of content analysis studies (e.g., Lee et al., 2011 and Golan & Zaidner, 2008) 13 and was demonstrated to be a reliable measure for informational and transformational strategies in modern day advertising. The author employs Taylor’s (1999) model as a manipulation check for the study 1 and study 2’s main stimuli. See Figure 3 for an illustration of Taylor’s (1999) model. Analytical Persuasion and Narrative Persuasion The informational and transformational dichotomy resembles the analytical processing traditionally studied in the dual-process models of persuasion, namely the elaboration likelihood model (ELM; Petty & Cacioppo 1981) and the heuristic-systematic model (HSM; Chaiken 1980). In these theories, persuasion takes effect via two routes. The highly involved consumer engages in thoughtful elaboration of the message, and allocates more cognitive effort to seek out and process product-related information. Alternatively, the less motivated consumer will not focus on the central message but will be emotionally triggered by a variety of peripheral cues (Petty & Cacioppo 1983). Beside the central and peripheral routes, there is no other way to initiate persuasion, and the models do not distinguish stories from other messages. Several scholars argue otherwise. 14 Figure 3: Taylor’s (1999) Six Segment Message Strategy Wheel model. Items of greatest importance Ration Ego Transmission View (Informational) • Claim-based • Presenting info • Rational • Product-focused Acute need Social Routine Sensory Ritual View (Transformational) • Image-based • Sharing, communing • Emotional • Experience-focused Items of least importance Ration Based on the hierarchy of effects model. Consumers need a lot of information about product features, attributes, prices, warranties, etc. Consumers willing to spend much time to gather such information and actively seek alternative brands/products for comparison. Acute need The need of a product/solution overrides the need of information. Product categories that provide immediate solutions to needs that arise abruptly. Routine Product purchased in a habitual way. Few cognitive effects are needed to judge product attributes. Differences between alternative brands are negligible. Ego Social Products that are appealing to consumer’s ego needs. Consumers buy the product to make a statement of “who they are”. Consumers have strong emotional attachments to the brand or product category. Consumers buy the product in order to fit or pretend to fit in a certain social group, e.g., friends, family, neighbors, co-workers. Through using the product, consumers gain the acceptance of the social group and find belonging. The liking of the group’s members is important. Sensory Products that are appealing to the human’s five senses. Such as products that provide a temporary pleasure, a treat to the mouth, or an emotional lift. 15 Green and Brock (2000) assert that although the dual-process models provide a valid description of analytical persuasion, they fail to capture narrative persuasion. They maintain that the mainstream attitude-change theories (e.g., ELM and HSM) have failed to provide applicable mechanisms to explain narrative persuasion (Green & Brock, 2000). Narrative persuasion cannot be categorized into central or peripheral processing, but a distinctive route to initiate persuasion entirely (Green & Brock, 2000). Following this argument, Slater and Rouner (2002) notes that analytical persuasion and narrative persuasion differ regarding the role of involvement, specifically, the involvement of the receiver with a narrative. In analytical persuasion, the message receiver processes the information based on his/her relevant involvement with the product or brand. If the involvement is sufficiently high, centralized processing occurs, and the message will be scrutinized. Involvement thus exists as a motivational state in which the receiver is aware of the persuasion attempt. In narrative persuasion, processing depends more on the involvement with the message itself rather than the involvement with the product/brand. To be effective in narrative persuasion, the receiver must be engrossed in the message (Slater & Rouner, 2002). This type of involvement is rather a state or experience where the receiver feels caught up in the narrative itself and not on the logical argument or other extrinsic or peripheral cues. According to Green and Brock (2000), analytical “elaboration leads to attitude change via logical consideration and evaluation of arguments” (p. 702), whereas narrative persuasion via involvement with the message relies on unintentional affective responses more so than intentional cognitive processing. If one scrutinizes the message critically, persuasion is unlikely to occur. This distinctive way of processing leads to strong affective responses and reduced negative cognitive responses, which can cause beliefs, and attitude and intention changes (Green, Kass, Carrey, Herzig, Feeney, & Sabini, 2008). 16 Narrative Transportation Deighton, Romer, and McQueen (1989) first found that advertisements adopting an analytical approach are more likely to stimulate cognitive responses, whereas advertisements using narrative elicit affective responses. They concluded that a narrative story is capable of creating a fictional world that the story readers can lose themselves in. Gerrig (1993) was the first to introduce the concept of transportation response, a conceptual metaphor in the broad context of the psychology of reading. That is, using the literal experience of traveling as a metaphor for reading, Gerrig anticipates the construct of “transportation” as a state of detachment to the real world. In his description: “[the reader] is transported, by some means of transportation, as a result of performing certain actions. The traveler goes some distance from his or her world of origin, which makes some aspects of the world of origin inaccessible. The traveler returns to the world of origin, somewhat changed by the journey” (Gerrig 1993, pp. 10- 11). Gerrig makes it clear that a transportation response occurs when there is a clear separation of the real world and the narrative world, and will result in some degree of persuasion effect. Green and Brock (2000) built on Gerrig’s work and coined the notion of narrative transportation as a new path to persuasion that is not addressed by previous theories. To explain the persuasive effect of narrative transportation, Green and Brock (2000) draw on the suggestion advanced by Gilbert and colleagues (Gilbert, 1991; Gilbert, Tafarodi, & Malone, 1993). Gilbert (1991) suggests that “people believe in the ideas they comprehend, as quickly and automatically as they believe in the objects they see” (p. 107). In his view, acceptance of an idea is the first step in comprehension, rejection of the idea follows but requires more effort than acceptance. In addition, it requires cognitive effort to disbelieve and to reconcile a particular assertion. Green and Brock (2000) suggest that a transported individual will be less likely to engage in critical 17 analysis of any assertion presented in the narrative because of the high immersion into the story. Gerrig and Prentice (1991) demonstrate that false information, such as “mental illness is contagious,” acquired through fictional narrative means can be incorporated into real-world knowledge in a receiver’s mind. Furthermore, transportation via a story not only causes the reader to believe explicitly presented assertions but also other implied aspects of the story (Green & Brock, 2000). The more the receiver experiences narrative transportation, the less likely a counterargument will be generated and the more likely a change of belief will occur. Green and Brock (2000) constructed narrative transportation based on the relevant theories of persuasion. Scholars following Green and Brock’s definition of narrative transportation stress three essential elements of transportation: (1) a story being consumed and interpreted, (2) a story receiver is transported through empathy and mental imagery, (3) the experience of entering a story world and leaving the real-world behind (Green & Brock, 2000; Slater & Rouner, 2002). Van Laer and colleagues (2013) offer a condensed definition of narrative transportation as “the extent to which (1) a consumer empathizes with the story characters and (2) the story plot activates his or her imagination, which leads him or her to experience suspended reality during story reception” (pp. 799-800). Empathy and imagination provide an explanation for the necessary detachment from reality that narrative transportation requires to activate. Empathy refers to the affection developed for the story characters or the experience of feeling the same way as the characters in the story. Through imagination, a receiver generates vivid images of the story scenes, thereby making the story seem more like a real-world event. As an experiential response to a narrative, narrative transportation is similar to other concepts such as flow—a highly focused mental state of concentration on the current event 18 (Csikszentmihalyi, 1992), absorption (Tellegen & Atkinson, 1974), and immersion (Wang & Calder, 2006). Yet, the concept of transportation is particularly focused on the holistic experience of the narrative. As a mental state, flow can be experienced in a variety of general activities such as writing and listening, while narrative transportation requires a story to initiate empathy and imagination, which are not mandated in flow. Similarly, absorption is described as a personal trait or tendency to be immersed in an experience (Tellegen & Atkinson, 1974). Interestingly, immersion is the term considered by Gerrig (1993) to refer to the concept of transportation, but he ultimately set it aside in favor of transportation. Immersion has been associated with flow (Csikszentmihalyi, 1992) but is primarily a response to images and other visual cues, while narrative transportation is linked to a comprehensive story plot (Phillips & McQuarrie, 2010). Narrative Transportation in Advertising Scholarly attempts to apply narrative transportation in advertising research have faced a dilemma. That is, Green and Brock (2000) introduced a new concept to explain persuasion, yet they reject its application in advertising. In fact, Green and Brock (2000) make a clear distinction between rhetoric and narrative arguing that print ads are rhetorical text, not narrative, thus “transportation is unlikely” (p. 719). Advertisements, regardless of how well they are constructed, carry explicit persuasive intent that will lead to consumer resistance (Green & Brock, 2000). Thus, following this critical logic, the persuasion route via transportation will not occur with rhetorical text such as ads. The crucial element, a narrative, must be presented. According to Green and Brock (2000), a narrative should be intense drama filled with strong emotions. In fact, the two stories used in Green and Brock’s paper both include dead-or-alive situations: the first one involves a 19 public murder at a shopping mall, the second is about a boy and his dog adrift on the sea—both pondering eating each other. Observant readers may notice that the narrative in Green and Brock’s (2000) interpretation bears similar elements to the many dramatic ads we see day-to-day: the protagonist(s), a plot and a climax. In fact, a common form of advertising—problem and solution—features the exact elements (though some might lack the big reveal at the end). To date, only a handful of scholars have attempted to overcome the conceptual barrier set by Green and Brock and explore the possibility of using transportation to explain persuasion of consumers via advertising. Acknowledging Green and Brock’s (2000) limitation regarding advertising’s inability to transport, Wang and Calder (2006) focus on the transportation effects produced by the media context of where the ads appeared. They found that transportation positively affects ads that do not interrupt the magazine reading process and negatively impacts ads that are seen as intrusive. However, their study did not examine the persuasive effects of the ads themselves, thus, the study provided no insights as to how transportation could work in advertising. Escalas (2004, 2007) used a different approach by manipulating the ad copy and instructing the readers to construct a narrative by themselves. As Escalas (2004) terms it “self- referencing mental simulation”, one of the ad copies reads as (p. 48): Imagine yourself running through this park. Your feet feel remarkably light. You look down and see a pair of Westerly running shoes, on your feet. They weigh only 10 oz. You notice a spring in your step. Westerly running shoes provide strong support with their advanced stability system. Westerly’s cushioning system spreads shock, reducing injury. Imagine yourself in Westerly running shoes, to improve the comfort and quality of your morning run. . . . In both studies, Escalas (2004, 2007) found support that transportation through self- referencing mental simulation increases ad attitudes and brand evaluations. However, her manipulation of the ad copies is far from what Green and Brock (2000) conceived, that is, plot 20 and drama. In a qualitative work examining fashion ads, Phillips and McQuarrie (2010) argue that narrative transportation does occur among graphic fashion ads despite Green and Brock’s (2000) reasoning. Fashion ads, especially those containing grotesque imagery (visual representations of bizarreness) can lead to narrative transportation that, in turn, intensifies the brand experience (Phillips & McQuarrie, 2010). In a study of antismoking advertisements, Dunlop, Wakefield and Kashima (2008) use “transportability”—an individual characteristic measure—as a proxy for transportation experience. Whereas transportation is the process of being persuaded in a story, transportability is the generalized tendency about how likely an individual will be transported by a message (Dal Cin, Zanna, & Fong, 2004). Dunlop et al. (2008) found that transportability is positively related to the recall of narrative antismoking advertisements, and individuals high in transportability (thus, easier to be transported by narratives) are more likely to recall an anti-smoking ad and to perceive the ads as helpful in their smoking cessation attempts. Similarly, Stephenson and Palmgreen (2001) found that messages high in drama, emotion, and activity are more effective in generating antismoking intentions than messages that are not. These findings suggest that engagement with the plot of narrative ads might increase message effectiveness, rather than the cognitive elaboration of the message argument as proposed by traditional dual-processing models (e.g., ELM and HSM). Perhaps the most relevant study of narrative transportation in advertising is Seo, Choi and Yoon’s (2018) paper on narrative transportation in electronic word-of-mouth advertising. Using similar conceptualization of narrative transportation as Green and Brock (2000), the authors found positive relationship between narrative transportation in viral ads and intention to share the ads (Seo et al., 2018). Additionally, they found that advertising disclosure negatively moderated such relationship. 21 Although many studies have explored various aspects of narrative transportation in advertising, none has actually performed empirical tests on the transportation effects of narrative in advertising storytelling, as per Green and Brock’s (2000) original conceptual definition. Thus, this dissertation proposes that narrative transportation, containing drama, plot and climax will lead to higher emotional responses and transportation experiences compared to informational or transformational strategies. This dissertation is the first to the author’s knowledge to examine various consumer responses to pure informational advertising, emotional/transformational advertising and narrative transportation advertising. Operational Definitions of the Three Strategies Based on the above review of literature, the author offers the following operational definitions for the three advertising strategies. Informational advertising strategy is an advertising strategy that focuses on providing factual and rational information about product features, specifications, benefits, services, etc. to consumers in a clear and logical way to aid product and brand evaluation and purchase decision. Transformational advertising strategy is an advertising strategy that focuses on presenting the emotional and hedonic side of product consumption in order to appeal to consumers’ ego, social or sensory needs. Transformational ads communicate the product use experience in an emotional way to associate the product and brand with a set of psychological characteristics. Transportational advertising strategy is an advertising strategy that focuses on a story of one or more characters, or a slice-of-life representation of the character(s), always in a positive light. Through immersion into the story, a consumer engages, feels and transports without much 22 critical scrutiny of ad claims, which results in positive affective responses to the ad, product and brand. In our definitions of advertising strategies, we only focus on strategies that present an outcome in a positive light. We make no predictions about what would happen if an advertising strategy is used to produce a negative effect. Unlike some of the prevention advertising (e.g., smoking, drug or alcohol) and negative political advertising, which are not the focus of this study, the goal of most commercial advertisements is to elicit positive feelings toward the brand/product in order to promote consumption. In addition, MacInnis and Price (1987) found a positivity bias due to the imagery processing of ads, which causes consumers to focus on only favorable ad outcomes while they imagine the ad scenario. Thus, in the context of the current study, we examine advertising strategies that are presented only in a positive light. Overlapping Model of Three Advertising Strategies Informational and transformational advertising strategies may not be mutually exclusive but can coexist in one advertisement (Puto & Wells, 1984). Similarly, based on the previous literature review of transformational and narrative transportation strategies, it is suggested that these two strategies may overlap in conceptual definition due to the use of emotion elements to persuade. Thus, this dissertation proposes an overlapping model of three advertising strategies (see Figure 4). 23 Figure 4: Overlapping model of advertising strategies. Informational Strategy Transformational Strategy Transportational Strategy As seen in Figure 4, the three strategies may overlap each other, which means that they can be used together in one advertisement. But the degree varies according to how the advertiser emphasizes each strategy. Thus, it is possible to use a mixed approach that features two or three strategies to some degree or to use only one strategy. However, it is important to note that despite the intention of applying one or more than one of the strategies in the ad, it is ultimately the judgement of the consumer that determines how the ad will be perceived. 24 CHAPTER 3 STUDY 1 – COMPARISON OF THE THREE STRATEGIES Even though the three strategies may overlap with each other, they may still carry unique and distinctive characteristics and may result in differentiated effects. In study 1, the author examined the three strategies as distinct advertising strategies and tested their individual effects on ad and brand evaluations, and purchase intention. For the purpose of study 1, fictional advertisements were created based on the operational definitions of the three advertising strategies with the intent to test the strategies’ individual effects. Based on the above discussion, study 1 proposes the following hypotheses: H1: An informational strategy will have a positive effect on ad attitude (H1a), brand attitude (H1b), and purchase intention (H1c). H2: A transformational strategy will have a positive effect on ad attitude (H2a), brand attitude (H2b), and purchase intention (H2c). H3: A transportational strategy will have a positive effect on ad attitude (H3a), brand attitude (H3b), and purchase intention (H3c). H4: A transportational strategy will have the most positive effects on ad attitude (H4a), brand attitude (H4b), and purchase intention (H4c) compared to a transformational strategy, and followed by an informational strategy. Methods Design Study 1 used a 3 (informational “INFO” vs. transformational “TRSF” strategies vs. transportational strategy “TRSP”) x 2 (two product categories with fictional brand names) between subject, mixed factorial design with repeated measures on product categories. All 25 participants were exposed to both product categories. In the stimulus ad, the advertising strategy (the ad copy) was either INFO, TRSF or TRSP. A total of six ads were created, two for each of the three conditions: INFO, TRSF, and TRSP, using two product categories. To ensure each participant views ads from the different conditions, a total of 12 (two category orders multiplied by six condition combinations; see Table 1) possible combinations were created. Participants were randomly assigned to one of the 12 conditions where they were exposed to two ads from two different product categories. The order of the conditions was arranged so that all ads were evenly presented across all participants. Table 1: Participants were randomly assigned to one of 12 possible condition orders. Order Stimuli Order Stimuli 1 2 3 4 5 6 Ad1 (category 1, INFO) Ad2 (category 2, TRSF) Ad1 (category 1, INFO) Ad2 (category 2, TRSP) Ad1 (category 1, TRSF) Ad2 (category 2, INFO) Ad1 (category 1, TRSF) Ad2 (category 2, TRSP) Ad1 (category 1, TRSP) Ad2 (category 2, INFO) Ad1 (category 1, TRSP) Ad2 (category 2, TRSF) 7 8 9 10 11 12 Ad1 (category 2, INFO) Ad2 (category 1, TRSF) Ad1 (category 2, INFO) Ad2 (category 1, TRSP) Ad1 (category 2, TRSF) Ad2 (category 1, INFO) Ad1 (category 2, TRSF) Ad2 (category 1, TRSP) Ad1 (category 2, TRSP) Ad2 (category 1, INFO) Ad1 (category 2, TRSP) Ad2 (category 1, TRSF) Pretests Product categories. The choice of product categories was informed by the importance of selecting moderately involving products that are relevant to the general population, as highly involving products can confound outcomes especially when product involvement is not the focus of the study. The first pretest with a student sample (N = 55) was designed to determine the 26 product categories from the following list: running shoe, water bottle, sunglasses, headphone, backpack, energy drink, energy bar, and ice-cream. Each participant evaluated (1) their involvement with the product categories, (2) general attitudes toward the products, (3) use frequency, and (4) the amount of money they are willing to pay for each product. Two medium- involving products with similar scores in attitude, use frequency, and the amount of money willing to pay were selected for study 1. See Table 2 for the scales used in this pretest. Table 2: Scales used in the study 1 pretest 1. Scale Item and Description Cronbach’s α Product category involvement (seven-point semantic differential scale; adopted from Zaichkowsky, 1994) To me the product category is: M = .90** • • • • • • • • • • (1) Important/Unimportant (7) * (1) Boring/Interesting (7) (1) Relevant/Irrelevant (7) * (1) Exciting/Unexciting (7) * (1) Means nothing/Means a lot to me (7) (1) Appealing/Unappealing (7) * (1) Fascinating/Mundane (7) * (1) Worthless/Valuable (7) (1) Involving/Uninvolving (7) * (1) Not Needed/Needed (7) Attitude toward the product (seven-point semantic differential scale; adopted from MacKenzie & Lutz, 1989) What is your general attitude about the product category? M = .87** • • • (1) Negative/positive (7) (1) Bad/good (7) (1) Unfavorable/favorable (7) Use frequency (multiple choice) How often do you use the product? N/A • Daily • Several times a week • Once a week • Once every two weeks • Once every three weeks • Once a month • Other (please specify) Money willing to spend on the product (in U.S. dollars; sliding scale from $0 to $500) If you are going to purchase a (product), how much are you willing to pay? N/A * reverse coded item. ** represents the mean Cronbach’s α values for the two product categories in the pretest 1. 27 Across the eight product categories, running shoe and headphone displayed similar levels of attitude, use frequency and money willing to pay. Repeated ANOVA tests between the two product categories revealed that the product category showed no main effects toward involvement, attitude toward the product, use frequency, and the amount of money willing to pay. Thus, the two product categories did not differ significantly in the above-mentioned variables. In addition, the two product categories shared similar medium to high levels of involvement (MRunning shoe = 5.58, SD = 1.06; MHeadphone = 5.82, SD = 1.03). Based on the results of this pretest, running shoe and headphone were selected as the product categories for study 1. Stimuli. Using professional software, three fictional product ads were created for each of the two product categories (one for each advertising strategy: INFO, TRSF, TRSP) by the author who has experience in advertising graphic design. All ads applied the same visual images of the product and a person using the product. The brand name, product name, and the placements of the names and visuals were also made consistent across all ads. In the INFO ads, the slogan and ad copy emphasize the clear and attractive quality of the product with detailed specifications and features. In the TRSF ads, the slogan and ad copy emphasize the feeling and experience of consuming/using the product. In the TRSP ads, the ad copy contains a short story about the person using the product. See Figure 5 for the study stimuli. 28 Figure 5: Study 1 stimuli ads per condition. Informational running shoe ad Transformational running shoe ad Transportational running shoe ad Informational headphone ad Transformational headphone ad Transportational headphone ad 29 A second pretest using an independent sample of student participants (N = 30) was designed to test the stimuli. All participants rated each ad on (1) narrative transportation (TRSP), (2) ad attitude, (3) brand attitude, (4) purchase intention (see Table 3 for the items), and (5) informational (INFO) and transformational (TRSF) strategies (see Table 4). Table 3: Perceived strategy used and dependent variable measures. Scale Item and Description Cronbach’s α Narrative transportation (seven-point Likert scale anchored by not at all/very much; modified from Appel et al., 2015) Based on the ad that you just saw, please indicate your disagreement/agreement with each of the following statements using the scale from 1 = Not at all to 7 = Very much. M = .86* • • I could picture myself in the scene of the events described in the ad. I was mentally involved in the ad while reading it. I wanted to learn how the ad ended. • • The ad affected me emotionally. • While reading the ad I had a vivid image of the primary character. Ad attitude (seven-point semantic differential scale; adapted from MacKenzie & Lutz, 1989) Please rate the ad you just saw: (1) Negative/positive (7) (1) Unpleasant/Pleasant (7) (1) Bad/Good (7) (1) Unfavorable/Favorable (7) • • • • M = .88* Brand attitude (seven-point semantic differential scale; adapted from Mitchell & Olson, 1981) Please indicate your attitude toward the brand: M = .89* • • • • (1) Bad/Good (7) (1) Unpleasant/Pleasant (7) (1) Unfavorable/Favorable (7) (1) Negative/Positive (7) * represents the mean Cronbach’s α values for the two product categories in study 1 pretest 2. 30 Table 3 (cont’d). Scale Item and Description Cronbach’s α Purchase intention (seven-point Likert scale anchored by very unlikely/very likely; modified from Terlutter, Diehl, & Mueller, 2006). Based on the ad that you just saw, please rate each of the following statements using the scale from 1 = Very Unlikely to 7 = Very Likely. • Would you like to try the M = .87* product? • With price being irrelevant, could you imagine yourself buying this product? • With price being irrelevant, could you imagine this product to be one of your most likely choices when you next buy it? Present or absent in the ad: N/A • Ration • Acute need • Routine • Social • Sensory • Ego Informational/Transformational strategy (see Figure 3 for item definitions) * represents the mean Cronbach’s α values for the two product categories in study 1 pretest 2. 31 Table 4: Informational and transformational scale items modified from Puto & Wells (1984). Item # Description Scale 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 I learned something from this ad that I didn’t know before about this brand. INFO I would like to have an experience like the one shown in the ad. The ad did not seem to be addressing me directly. There is nothing special about this ad that makes it different from the others. TRSF TRSF INFO While I read this ad, I thought about how this brand might be useful to me. TRSF This ad did not teach me what to look for when buying this product. This ad was meaningful to me. This ad was very uninformative. This brand fits my lifestyle very well. I could really relate to the ad. Using this brand makes me feel good about myself. INFO TRSF INFO TRSF TRSF TRSF The company could provide evidence to support the claims made in the ad. INFO It’s hard to give a specific reason, but somehow this brand is not really for me. This ad did not really hold my attention. If I could change my lifestyle, I would make it less like the people who use this brand. TRSF TRSF TRSF I felt as though I were right there in the ad, experiencing the same thing. TRSF I can now accurately compare this brand with other competing brands on matters that are important to me. This ad did not remind me of any experiences or feelings I’ve had in my own life. I have more confidence in my ability to judge the merits of buying this brand now that I have seen this ad. It’s hard to put into words, but this ad leaves me with a good feeling about using this brand. INFO TRSF INFO TRSF Seven-point Likert scale anchored by strongly disagree (1) to strongly agree (7) Note: INFO Cronbach’s α = .71, TRSF Cronbach’s α = .86 (for study 1) 32 Results of the second pretest showed that INFO conditions were rated higher in perceived informational strategy than TRSF and TRSP conditions (see Table 5 for post hoc test results). Similarly, TRSF ads were higher in perceived transformational strategy than INFO and TRSP ads. TRSP ads were indeed rated higher in perceived transportational strategy compared to the INFO and TRSF ads. All conditions showed positive (larger than mid-point) ad attitudes, brand attitudes and purchase intentions. Thus, the stimuli were used for study 1. Table 5: Study 1 pretest 2 pairwise comparisons of perceived strategy used in the conditions. Running shoe (N = 30) Headphone (N = 30) INFO vs. TRSF INFO vs. TRSP TRSF vs. TRSP INFO vs. TRSF INFO vs. TRSP TRSF vs. TRSP Perceived Perceived Perceived Informational Transformational Transportational Strategy Strategy Strategy Mean Difference Mean Difference Mean Difference -.76** -.99** -.24 -.78** -.72* -.06 -.98*** -.24 -.76* -1.22*** -.53 -.69* 1-.09 -1.14* -1.06* -1.54 -1.07*** -1.22** Note: † p < .10, * p < .05, ** p < .01, *** p < .001 To further test the informational and transformational manipulation, one independent researcher who was not participating in the study rated the ads for the informational and transformational manipulation using Taylor’s Six Segment Message Strategy Wheel (Taylor, 1999). Specifically, each ad was coded in terms of six individual items: ration, acute need, routine, ego, social, and sensory. The first three items (the transmission/informational) were coded as either present (value = 1) or absent (value = 0). The last three items (the ritual/transformational) were coded as either present (value = -1) or absent (value = 0). See 33 Figure 3 for the definitions of the items. The final scores for each ad were calculated by summing up all six items’ scores. The ad was categorized as using an informational strategy if the final score was larger than zero. If the final score was less than zero, then the ad was categorized as adopting a transformational strategy. The independent researcher was briefed and trained on the application of Taylor’s six-segment model. The results indicated that the INFO ads adopted an informational strategy as evidenced by the presence of the item ration for both product categories (final total score = 1), while both the TRSF and TRSP ads were using a transformational strategy as evidenced by the presences of ego and sensory (final total scores = -2). See Table 6 for the results. Table 6: Categorization of study 1 stimuli using Taylor’s Six Segment Message Strategy Wheel (Taylor, 1999). Running shoe INFO TRSF TRSP INFO Headphone TRSF TRSP Participants Transmission Ritual (Informational) Strategy (Transformational) Strategy Ration Acute need Routine Ego Social Sensory 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 -1 -1 0 -1 -1 0 0 0 0 0 0 0 -1 -1 0 -1 -1 Study 1 participants (N = 240) were recruited from Amazon Mechanical Turk. The Mechanical Turk respondents participated in the study anonymously and received $.50 US dollar as compensation for completing the survey. The average completion time per participant was 11.8 minutes. A demographic breakdown of the participants can be seen in Table 7. 34 Table 7: Demographics of the study 1 participants (N = 240). Variables Sex Male Female Age 30-35 36-40 41-45 46-50 50+ Percent 38.6% 61.4% 14.5% 47.5% 13.7% 9.0% 17.1% Ethnicity 2.1% American Indian of Native 7.7% Asian American 38.0% Black/African American Latino 3.4% Two or more Ethnicities/Races 3.4% White/Caucasian Other 44.0% 1.3% 46.6% 42.7% 6.4% 3.4% .9% 15.6% 5.6% 36.4% 38.1% 4.3% 6.8% 35.0% 45.7% 11.1% 1.3% Marital Status Never Married Married Separated Divorced Widowed Formal Education Years 1-5 6-10 11-15 16-20 20+ Income Less than $10,000 $10,000 - $49,999 $50,000 - $99,999 $100,000 - $149,999 $150,000 or more 35 Procedures Upon registering for study 1 on Amazon Mechanical Turk, participants were directed to the study’s Qualtrics survey and randomly assigned to one of 12 pre-randomized between- subject conditions. In each condition, they viewed and rated two ads that were presented in pre- randomized order. Upon completing the message evaluation portion of the study, participants indicated the perceived levels of advertising strategy used in the ads, their general attitude toward the products, their use and purchase frequency, and money they typically pay for the product. Finally, they answered demographic questions and were routed back to Amazon Mechanical Turk to enter a randomly generated code to receive payment. Independent Variables Strategy Types. Using the operational definitions of INFO, TRSF, and TRSP that were based on Puto and Wells’ (1984) informational and transformational advertising strategies and Green and Brock’s (2000) narrative transportation strategy, participants were exposed to messages that were focused on providing one of the strategies: information (INFO), emotional appeal to transform attitudes (TRSF), or transporting readers through a narrative (TRSP). Product Category Repetition. Each of the three strategy types was repeated across two different product categories (running shoe and headphone) to enhance the representativeness of the stimuli. The product categories had been pretested for comparability. Dependent Variables Ad Attitude. Attitudes toward the ad were measured using the scale adopted from MacKenzie and Lutz (1989). Each item entailed a seven-point semantic differential scale anchored negative (1)/positive (7), unpleasant (1)/pleasant (7), bad (1)/good (7), and unfavorable (1)/favorable (7). See Table 3 for all dependent variable measures. Mean Cronbach’s α = .88. 36 Brand Attitude. Study 1 used Mitchell and Olson (1981) four-item measure for brand attitude. Participants were asked to rate on a seven-point semantic differential scale anchored by negative (1)/positive (7), unpleasant (1)/pleasant (7), bad (1)/good (7), and unfavorable (1)/favorable (7). Mean Cronbach’s α = .89. Purchase Intention. Study 1 used a modified version of purchase intention scale developed by Terlutter, Diehl, and Mueller (2006). Each item was rated on a seven-point Likert- Type scale anchored by very unlikely (1)/very likely (7). Mean Cronbach’s α = .88. Control Variables Depending on the product categories used, a number of potential control variables were assessed in order to be controlled across all participants. The following control variables used the same measurements described in Table 2: attitude toward (the product), use frequency, purchase frequency, and money they typically pay for (the product). Finally, demographic variables such as gender, age, ethnicity, education, and household income were also analyzed. Post hoc analysis showed that only attitudes toward the product (both running shoe and headphone) were significant in all of the models, and thus were included in subsequent univariate ANOVA analyses as control variables for the respective product categories. Data Analysis To test the main effects, the dependent variables were submitted to 3 (advertising conditions) x 2 (product category repetition) factorial ANOVA tests using IBM SPSS with product category repetition as the repeated measuring factor. Further, for each product category, the dependent variables were submitted to univariate ANOVA tests controlling for attitude toward the products (running shoe and headphone). 37 Manipulation Checks Narrative transportation manipulation was checked by using the narrative transportation scale short form developed by Appel et al. (2015). Each item was rated on a seven-point Likert- Type scale anchored by not at all (1)/very much (7). See Table 3 for the measurements. Within the study, respondents completed a modified scale adopted from Puto and Wells (1984) that measures informational and transformational strategies on a seven-point Likert-Type scale anchored by strongly disagree (1)/strongly agree (7). See Table 4 for the items. A repeated measure ANOVA showed that the ads using the same strategy in the two product categories did not differ in terms of perceived narrative transportation, perceived informational and perceived transformational strategies. Thus, the responses of the two product categories were averaged for the following analysis. Univariate ANOVA tests using perceived informational and perceived transformational strategies as dependent variables revealed that the INFO condition was rated higher in perceived informational strategy than the TRSF and TRSP conditions, F (2,240) = 5.60, p < .005. A pairwise comparison showed that the TRSF and TRSP conditions did not differ in perceived informational strategy (mean difference = -.21, ns). Further, the INFO condition had the lowest ratings on perceived transformational strategy compared to the TRSF and TRSP conditions, F (2,240) = 11.11, p < .000. However, TRSF and TRSP conditions did not differ in terms of transformational ratings (mean difference = .11, ns). This result was expected given the fact that the informational/transformational scale (Puto & Wells, 1984) was not intended to distinguish a transportational strategy from a transformational strategy. To confirm the narrative transportation manipulation, a univariate ANOVA test with perceived narrative transportation as the dependent variable was conducted. The result showed 38 that the TRSP condition had the highest rating on narrative transportation than the INFO and TRSF conditions, F (2,240) = 17.53, p < .000. Thus, the manipulation was successful. Results Hypothesis 1 to 3 Hypothesis 1 to 3 posited that all three advertising conditions will have positive effects on ad attitudes, brand attitudes and purchase intention regardless of product category. The 3 (advertising conditions) x 2 (product category repetition) factorial ANOVA tests revealed that there were no main effects of product category on the dependent variables, FAdAtt (1, 78) = 1.78, ns; FBrAtt (1, 78) = .38, ns; FPI (1, 78) = .03, ns. This indicated that the levels of ad attitudes, brand attitudes and purchase intention did not differ between the two product categories. Thus, when averaging the dependent variables across the two product categories, one-sample t-tests showed that the dependent variables of all three advertising conditions were significantly larger than zero. Further, all mean values of the dependent variables were above the neutral point (4 on a 7-point scale; see Table 8). In fact, frequency analysis showed that more than 80% of the respondents rated the three dependent variables above the mid-point value (4 on a 7-point scale) across the three advertising strategies (see Table 9). Based on the above analyses, H1, H2, and H3 were supported. 39 Table 8: Descriptive results of the dependent variables of the two product categories, and one- sample t-test of the dependent variables averaged across the two product categories. Ad Attitude Brand Attitude Purchase Intention Running shoe (N = 79) Headphone (N = 79) Combined (N = 79) M INFO 5.59 TRSF 5.31 TRSP 5.87 INFO 5.46 TRSF 5.05 TRSP 5.90 INFO TRSF TRSP SD 0.99 1.42 0.98 1.19 1.27 1.02 0.73 M 5.35 5.14 5.60 5.42 5.00 5.84 5.37 SD 1.26 1.44 1.00 1.12 1.29 1.10 0.84 M 5.02 4.86 5.57 5.28 4.73 5.38 5.10 SD 1.26 1.50 1.31 1.20 1.61 1.54 0.91 5.50 t = 68.48*** t = 57.94*** t = 50.91*** 5.21 1.05 5.12 1.06 4.85 1.22 t = 44.94*** t = 43.71*** t = 36.10*** 5.89 0.70 5.72 0.74 5.48 0.99 t = 76.08*** t = 69.89*** t = 50.19*** Note: † p < .10, * p < .05, ** p < .01, *** p < .001 All scales are seven-point scales ranging from the most negative (1) to the most positive (7) Table 9: Cumulative percentages of responses scored above the mid-point values for the three dependent variables. INFO TRSF TRSP Hypothesis 4 Ad Attitude Brand Attitude Purchase Intention % 95.2% 81.7% 95.1% % 88.0% 81.7% 90.2% % 85.5% 78.0% 84.1% Hypothesis 4 predicted that a transportational strategy will have the highest scores for ad attitude, brand attitude, and purchase intention compared to the transformational strategy, and 40 followed by informational strategy. Results from the 3 (advertising condition) x 2 (product category repetition) factorial ANOVA analyses indicated that the main effects of the advertising strategy were significant for all three dependent variables, FAdAtt (2, 77) = 13.11, p < .000, η2 p = .25; FBrAtt (2, 77) = 12.49, p < .000, η2 p = .25; FPI (2, 77) = 7.36, p < .005, η2 p = .16 (see Figure 6). The main effect of product category, and the interaction of the product category and advertising strategy were both not significant. Figure 6: Estimated means of the dependent variables for the three conditions across the two product categories (NRunning shoe = 79, NHeadphone = 79). e d u t i t t A d A n a e M 6.0 5.8 5.6 5.4 5.2 5.0 4.8 4.6 e d u t i t t A d n a r B n a e M n o i t n e t n I e s a h c r u P n a e M INFO TRSF TRSP INFO TRSF TRSP INFO TRSF TRSP Running shoe Headphone Note: all scales are seven-point scales ranging from the most negative (1) to the most positive (7). When controlling for attitudes toward the products (running shoe = 4.27, headphone = 4.22), post hoc analyses revealed that TRSP ads had the highest ad attitudes (MAdAtt = 5.9, SD = .70), brand attitudes (MBrAtt = 5.7, SD = .70) and purchase intention (MPI = 5.5, SD = 1.0) compared to the INFO (MAdAtt = 5.5, SD = .70; MBrAtt = 5.4, SD = .80; MPI = 5.1, SD = .90) and TRSF (MAdAtt = 5.2, SD = 1.7; MBrAtt = 5.1, SD = 1.1; MPI = 4.9, SD = 1.2) ads. Yet the differences between TRSP and INFO ads in terms of purchase intention were not statistically significant (mean difference = .32, p < .10, see Table 10). H4 was partially supported. 41 Table 10: Pairwise comparisons between advertising strategies. Ad Attitude Brand Attitude Purchase Intention Mean Difference Mean Difference Mean Difference INFO vs. TRSF .35† Combined (N = 79) INFO vs. TRSP -.36** .32† -.33* .35† -.32† TRSF vs. TRSP -.71*** -.65*** -.68** Note: † p < .10, * p < .05, ** p < .01, *** p < .001 Discussion The results of study 1 brought empirical support to the overlapping model of advertising strategies (Figure 4). In essence, the overlapping model highlights the distinctive nature of each of the three advertising strategies, even though the strategies may overlap with each other conceptually. Results from the study 1’s manipulation check and the second pretest both pointed to the limitation of the informational/transformation scales (Taylor’s six-segment model, 1999; and Puto and Wells’s scale,1984) to distinguish transportational strategy from transformational strategy. As the literature review indicated, transportational and transformational strategies share similar aspects of emotional elements but differ in how the viewer is involved—transportational or narrative transportation emphasizes involvement in the story itself rather than in the advertised product (Green et al., 2008). To put it another way, transportation strategy is a unique pathway to advertising persuasion that the traditional definition of transformational strategy has failed to capture. Thus, applying only Puto and Wells’ (1984) definition of transformational strategy is inadequate to study transportational advertising strategy. This requires dedicated scales to measure transportational strategy in advertising (e.g., Appel et al., 2015). 42 All advertising strategies used in study 1 resulted in positive outcomes in terms of ad attitude, brand attitude and purchase intention. However, the degree differed significantly among the three advertising strategies. Across all conditions, transportational strategy consistently outperformed the other two advertising strategies (see Figure 6). According to the literature review, transportational strategy works by emotionally immersing the consumer in the story, reducing the consumer’s critical thoughts while transporting and engaging the consumer in a product-related and oftentimes positive story world. Ads that followed this operational definition of transportational strategy showed better results compared to ads that focused on rational information about the product or those focused on just presenting the emotional and hedonic side of product consumption. Moreover, because transportational ads can overlap with the other two strategies, a combined approach may yield transportational ads with advantages from the informational or transformational strategies. For example, the successful slice-of-life story of a runner appeals to a consumer’s need for ego, a transformational element according to Taylor (1999). The runner’s story also worked in associating the running shoe with an active lifestyle that is unique to the brand. Successful global brands are no strangers to the mixed approach. Nike is known to use an association transfer approach to link its brand and product to certain athlete’s stories and transfer the athlete’s positive attributes to the brand (for example, Figure 1). Compared to the print media used in study 1, a more effective and popular way for storytelling is through video. Over the years, Nike has produced numerous ads telling insprational life stories of celebrities alike. Recently Nike uploaded a video ad featuring Jason Day—an Australian professional golfer and PGA Tour champion—that tells Day’s childhood story ([Nike], 2019a; see Nike’s official YouTube channel 43 https://www.youtube.com/user/nike/videos for more examples). Digital media platforms such as YouTube and Instagram have also provided alternative ways for advertisers to air longer and better versions of the ads at minimun cost. Thus, more advertising money can be used to produce the ad instead of spent on buying TV air time, thereby increasing the quality of the video. Political advertising has also adopted transportational strategy in telling emotional stories of the candidates via digital channels. According to Wall Street Journal (2018), these “empathy” political ads tell personal stories that touch on the difficulties of the candidates’ lives, raising millions of dollars for the candidates. Although not the scope of study 1, product placement in media content is also a way to embed branded products into a story. According to industry data, product placement ad spending in the U.S. experienced its ninth consecutive year of double-digit growth in 2018 (PQ Media, 2018). A study using store sales data found that product placement in TV shows is more effective than traditional TV interstitial advertising (ads that are inserted between programs; Mummalaneni, Wang, Chintagunta, & Dhar, 2019). Future studies should explore transportational ads used in different forms of advertising. Finally, contrary to one of the study’s hypotheses, INFO ads marginally outperformed TRSF ads when combining the two product categories. This might be due to the limitation of the stimuli to evoke emotional/affective responses, especially when both products (running shoe and headphone) require certain levels of technologial information to gauge their performance. As seen in Figure 7, the TRSF condition showed no particularly noteworthy level of any of the three strategies. In other words, the TRSF condition fell short on its “edge” and was perceived to be a “generalist” rather than a “specialist” strategy. However, this is not a surprising outcome. During the many years of the author’s practice in ad agencies, it is all too common to witness an ad not 44 performing as intended. Ultimately, advertising only works inside the consumer’s mind. This finding also stresses the importance of theorizing and empirically testing advertising strategies. Figure 7: Mean levels of perceived advertising strategy used in the study 1 stimuli when combining the two product categories. Informational 5.5 5.0 4.5 4.0 Transportational Transformational INFO Condition TRSF Condition TRSP Condition Note: all scales are seven-point scales ranging from the most negative (1) to the most positive (7). In sum, the use of advertising strategies can be an overlapping approach with a mixture of different levels of the three strategies. Future studies should empirically examine the effectiveness of different overlapping approaches. 45 CHAPTER 4 STUDY 2 – SINGLE STRATEGY VS. MIXED STRATEGY Study 1 found that the informational (INFO) ad and the transportational (TRSP) ad outperformed ads (the original TRSF condition) that showed no particularly noteworthy level of informational, transformational, or transportational advertising strategies. As informed by the findings of study 1, ads that were perceived to focus on one of the three advertising strategies (informational, transformational, or transportational) resulted in more favorable evaluations compared to ads that were perceived to include elements of all three strategies. However, two questions remain unanswered. First, results from study 1 indicated that the TRSF stimuli were not perceived as truly transformational. Thus, to substantiate the claim at the beginning of this paragraph, further testing was needed to compare an ad that shows elements of the TRSF strategy with an ad that features moderate levels of all three advertising strategies. By using a true TRSF ad, study 2 extended study 1 by testing advertising effectiveness across the three advertising strategies (informational, transformational, and transportational). Second, study 1 employed fictional brands (PUE and KOK) in the stimuli. According to Escalas (2004), fictional brand names result in identical levels of attitudes and behavior intentions. Thus, it is unlikely that attitudes toward the fictional brand and purchase intention measured distinct constructs. Furthermore, the use of fictious brands limits the findings’ generalizability and real-world application. The use of a real brand name depicts a real-world situation and thus may increase the external validity of the study. Study 2 was designed to further examine the different effects of advertising strategies based on an overlapping model of advertising strategies. In study 2, the fictional brand logo was replaced with the Nike logo and the product images were removed in the INFO and TRSP 46 running shoe ads from study 1 (as outlined in the next paragraph). These modified ads represented each respective strategy and the original TRSF running shoe ad was added as a control condition, which represented mixed strategies. A new TRSF running shoe ad was created and pretested to confirm that it used mainly the transformational strategy. All ads were branded under Nike instead of the fictional “PUE” brand. In order to control for pre-existing attitudes toward the Nike brand, attitudes toward Nike and Nike running shoes, as well as purchase history of Nike running shoes were assessed. These were controlled across participants in the analysis. The other measurement scales (namely, perceived informational/transformational strategies, transportational strategies, ad attitude, brand attitude, and purchase intention) were unchanged from study 1. Further, study 2 focused on one product category instead of two because the results of study 1 indicated that the three advertising strategies functioned in similar ways across the two different product categories. Based on the findings of study 1, study 2 applied the same operational definitions of the three advertising strategies and the overlapping model of advertising strategy. The author proposed the following hypotheses: H1: Ads that focus on one advertising strategy (informational, transformational, or transportational) will yield more favorable ad attitudes (H1a), brand attitudes (H1b), and purchase intentions (H1c) compared to an ad that has moderate levels of all three strategies (overlapping model). H2: A transportational advertising strategy will yield more favorable ad attitudes (H2a), brand attitudes (H2b), and purchase intentions (H2c) compared to an informational strategy. 47 Based on the mixed findings of TRSF ads in study 1, a research question was raised as follows: RQ: How will a transformational advertising strategy differ in the evaluations of ad attitude, brand attitude, and purchase intention compared to informational and transportational advertising strategies? Methods Study 2 used a between-subjects factorial design of INFO vs. TRSF vs. TRSP vs. control ads. Participants were randomly exposed to one of the four conditions. The survey was arranged so that all conditions were evenly presented across all participants in a randomized fashion. As outlined in the previous section, study 2 used slightly modified ads from study 1 for the INFO, TRSP and control conditions. A new TRSF condition was created and pretested to confirm that it used mainly a transformational strategy. The pretests are discussed in the following sections. Pretests A two-part pretest employed both quantitative and qualitative methods to confirm the use of a transformational advertising strategy for the new TRSF condition in study 2. Specifically, a quantitative study using Amazon Mechanical Turk subjects was conducted to test the perceived levels of strategy used in the candidate TRSF ads. Also, a qualitative study employing Taylor’s Six Segment Message Strategy Wheel (Taylor, 1999) was conducted to confirm that the selected TRSF ad was indeed using a transformational strategy. Pretest using Amazon Mechanical Turk Subjects. A pretest using an independent sample of participants from Amazon Mechanical Turk (N = 80) was designed to select a new TRSF ad from three candidate ads. Three newly created running shoe ads were created using the operational definition and conditions of a transformational advertising strategy. Each participant 48 viewed all three ads in randomized order. The participants were asked to rate each ad on (1) informational and transformational strategies (same items as in Table 4), (2) ad attitude, (3) brand attitude, (4) purchase intention, and (5) perceived transportational strategy (same items as in Table 3). The criteria for selecting the TRSF ad were: (a) it had the highest level of perceived transformational strategy among all three ads; (b) its perceived level of transformational strategy was significantly higher than its informational or transportational strategy; (c) it had positive (greater than mid-point) ad attitudes, brand attitudes, and purchase intention. Post hoc analysis using repeated ANOVA indicated that among the three candidate ads, ad2 showed a significantly higher level of TRSF than ad1 and ad3 (mean differences ranged from .33 to .46, p < .05), satisfying the first criterion. Further, results of a post hoc analysis showed that ad2’s TRSF level was rated significantly higher than its INFO and TRSP levels (mean differences ranged from .38 to .41, p < .05), satisfying the second criterion. All ads showed positive (greater than the mid-point) ad attitudes, brand attitudes and purchase intentions. Thus, the third criterion was met. Descriptive results of this pretest can be seen in Table 11. Table 11: Perceived levels of advertising strategy used in the study 2 pretest ads. Stimuli Strategy M SD Ad1 (N = 80) Ad2 (N = 80) Ad3 (N = 80) INFO TRSF TRSP INFO TRSF TRSP INFO TRSF TRSP 3.95 4.50 4.02 4.55 4.96 4.58 4.63 4.63 5.31 0.93 1.07 1.44 0.88 1.08 1.43 1.22 1.20 1.25 Note: all scales are seven-point scales ranging from the most negative (1) to the most positive (7). 49 Pretest using an independent researcher. A second pretest using an independent researcher was conducted to confirm that the selected ad from the quantitative pretest was indeed rated as using a tansformational strategy. During this pretest, an independent researcher who was unaffiliated with the study was briefed and trained on the application of Taylor’s six-segment model. The independent researcher then rated ad2 for its levels of informational and transformational strategies using Taylor’s Six Segment Message Strategy Wheel (Taylor, 1999). Following the same procedures outlined in study 1, the researcher coded six individual items: ration, acute need, routine, ego, social, and sensory, which reflect the informational and transformational strategies used in the ad based on Taylor’s six-segment model (1999). Ad2 was coded to be using only Ego and Sensory approaches (see Table 12), and thus, using the transformational strategy rather than the informational strategy because of the presence of the transformational elements of ego and sensory (according to Taylor, 1999). Table 12: Categorization of study 2’s pretest ad2 using Taylor’s Six Segment Message Strategy Wheel (Taylor, 1999). Transmission Ritual (Informational) Strategy (Transformational) Strategy Ration Acute need Routine Ego Social Sensory Ad2 0 0 0 1 0 1 Based on the results of the two pretests, ad2 was selected as the TRSF condition stimulus for study 2. Participants A power analysis was conducted to help determine the proper sample size for study 2. Using G*Power 3.1.9.4 (Faul, Erdfelder, Lang, & Buchner, 2007), a total sample size of 280 was calculated based on an effect size of .20, p value of .05 with a power of 80% for ANOVA four 50 groups. Participants (N = 280) were recruited from Amazon Mechanical Turk. The subjects participated in the study anonymously and received $1.001 as compensation for completing the study 2 survey. See Table 13 for the demographics of the study 2 participants. 1 The payment was based on an estimated 8-minute survey completion time and the current federal minimum wage of $7.25 per hour. 51 Table 13: Demographics of the study 2 participants (N = 280). Variables Sex Male Female Age 18-25 26-30 31-35 36-40 41-45 Percent 49.3% 50.4% 11.4% 22.2% 24.3% 24.6% 17.5% Ethnicity 1.8% American Indian of Native 7.1% Asian American 17.9% Black/African American Latino 1.8% Two or more Ethnicities/Races 1.4% White/Caucasian Other 67.5% 2.5% Marital Status Never Married Married Separated Divorced Education Less than high school High school graduate Some college, no degree Associate’s degree Bachelor's degree Master's degree Professional degree Doctorate degree Income Less than $10,000 $10,000 - $49,999 $50,000 - $99,999 $100,000 - $149,999 $150,000 or more 48.9% 45.7% 1.1% 4.3% 1.1% 18.6% 20.0% 9.6% 30.4% 16.8% 1.4% 2.1% 5.4% 28.9% 41.4% 14.6% 9.6% 52 Procedures Study 2 followed similar procedures as outlined in study 1. Amazon Mechanical Turk subjects registered to participate in study 2 and were directed to the study’s Qualtrics survey site from the Amazon Mechanical Turk interface. Participants were then randomly asked to view and evaluate one of the four study conditions. In each condition, they viewed and rated one running shoe ad that featured one of the advertising strategies or the control stimulus. Upon completing the ad evaluations, participants answered the control questions and the demographic questions and receive a randomly generated code. They were then routed back to Amazon Mechanical Turk to use the code to receive payment. Stimuli The stimuli of the INFO and TRSP conditions were those used in study 1 but with Nike as the brand name and featuring a Nike swoosh logo. As previously discussed, the TRSF stimulus was created and pretested specifically for study 2. All four stimuli use the same visual elements, including a picture of a person, the background, an image of a pair of running shoes, the Nike brand name and logo, the colors, and the font. The INFO ad copy presents mainly product information. The TRSF ad copy focuses on using an emotional appeal to transform attitudes. The TRSP ad copy focuses on transporting readers through a person’s story. Finally, the control stimulus was the original TRSF running shoe ad used in study 1. The control condition features moderate degrees of all three advertising strategies. See Figure 8 for the study 2 stimuli. 53 Figure 8: Study 2 stimuli ads per condition. Informational ad Transformational ad Transportational ad Control condition Independent Variables Strategy Types. Four strategy types were used in study 2: INFO, TRSF, TRSP, and a control condition that featured moderate degrees of the three strategies. Dependent Variables Perceived Transportational Strategy. As in study 1, study 2 used the narrative transportation scale short form developed by Appel et al. (2015). Each item was rated on a seven-point Likert-Type scale anchored by not at all (1)/very much (7) (see Table 3 for items). Mean Cronbach’s α = .86. Perceived Informational and Transformational Strategies. The perceived levels of informational and transformational strategies used in the ads were measured by the 54 informational/transformational scale modified from Puto & Wells (1984). Participants rated each statement on a seven-point Likert-Type scale anchored by strongly disagree (1)/strongly agree (7) (see Table 4 for items). INFO Cronbach’s α = .77, TRSF Cronbach’s α = .83 Ad Attitude. Study 2 used a modified scale developed by MacKenzie and Lutz (1989) to measure attitude toward the ad. Participants were asked to rate their attitudes about the ad on a seven-point semantic differential scale anchored by negative (1)/positive (7), unpleasant (1)/pleasant (7), bad (1)/good (7), and unfavorable (1)/favorable (7) (see Table 3 for the measures). Mean Cronbach’s α = .88. Brand Attitude. Brand attitude was measured by Mitchell and Olson’s (1981) four-item measure for brand attitude. Each item entailed a seven-point semantic differential scale anchored by negative (1)/positive (7), unpleasant (1)/pleasant (7), bad (1)/good (7), and unfavorable (1)/favorable (7). Mean Cronbach’s α = .89. Purchase Intention. Study 2 used a modified version of a purchase intention scale developed by Terlutter, Diehl, and Mueller (2006). Items were rated on a seven-point Likert- Type scale anchored by very unlikely (1)/very likely (7). Mean Cronbach’s α = .90. Control Variables Control variables were: select demographics, brand familiarity, attitude toward and purchase history of Nike running shoes. Demographics. Age, sex, ethnicity, education, and household income of the participants were control variables. Brand Familiarity. Participants rated their familiarity with the Nike brand using a single seven-point semantic differential scale anchored by not at all familiar (1)/extremely familiar (7). 55 Attitude toward Nike Running Shoes. Participants were asked to rate their general attitudes toward Nike running shoes on a three-item seven-point semantic differential scale anchored by negative (1)/positive (7), bad (1)/good (7), and unfavorable (1)/favorable (7). Cronbach’s α = .94. Purchase History of Nike Running Shoes. Participants were asked if they have purchased Nike running shoes during the past 12 months and if so, how many pairs. Data Analysis The study used multivariate ANOVA analysis in IBM SPSS to test the hypotheses and answer the research question. Control variables that showed significant main effects were entered into the test to control for individual differences. Further, post hoc analysis was used to compare advertising strategies against each other in terms of perceived strategy used, ad attitude, brand attitude, and purchase intention. Manipulation Checks A multivariate ANOVA test was conducted to check the difference in perceived strategy used among the study conditions. The result showed that the conditions were significantly different from each other in terms of perceived strategy used F (9, 828) = 16.45 p < .000. Post hoc individual univariate tests showed that the four conditions were indeed different in terms of each of the three advertising strategy, INFO F (3, 276) = 5.11, p < .005; TRSF F (3, 276) = 23.03, p < .000; TRSP F (3, 276) = 36.43, p < .000. Results from pairwise comparisons indicated that the INFO condition was rated highest in perceived informational strategy among the conditions (mean differences ranged from .57 to .64, p < .05). The other three conditions did not differ in terms of perceived informational strategy (mean difference ranged from .01 to .07, ns). See Table 14 for the pairwise comparisons results. 56 Table 14: Differences of perceived advertising strategy used in the study 2 stimuli. INFO vs. Control TRSF vs. Control TRSP vs. Control INFO vs. TRSF INFO vs. TRSP TRSF vs. TRSP Perceived Perceived Perceived Informational Transformational Transportational Strategy Strategy Strategy Mean Difference Mean Difference Mean Difference .57* -.01 -.07 .59* .64* -.05 -.78*** .57** .13 -1.35*** -.91*** .43 -1.16* -.24 .48* 1-.92* -1.64* -.72* Note: † p < .10, * p < .05, ** p < .01, *** p < .001 For the perceived transformational strategy, the TRSF condition was rated higher than the INFO (mean difference = 1.35, p < .000) and the control conditions (mean difference = .57, p < .005) but did not differ from the TRSP condition (mean difference = .43, ns). Further, the TRSP condition did not differ from the control condition in terms of perceived transformational strategy (mean difference = .13, ns). This was not surprising since the informational/transformational scale was not designed to distinguish transportational strategy from transformational strategy. Finally, the INFO condition was rated lower than any of the three conditions (mean differences ranged from -.78 to -1.35, p < .000). Perceived transportational strategy was measured using the narrative transportation short form (Appel et al., 2015). Pairwise comparisons showed that the TRSP condition displayed the highest perceived transportational strategy among the conditions (mean differences ranged from .48 to 1.64, p < .05). The TRSF and control conditions did not differ from each other in perceived transportational strategy (mean difference = -.24, ns) but both showed higher levels than the INFO condition (mean differences ranged from .92 to 1.16, p < .05). See Figure 9 for 57 the perceived advertising strategy used in each of the four conditions. As the results indicated, the manipulation of the study conditions was successful. Figure 9: Mean levels of perceived advertising strategy used in the study 2 stimuli. Informational 5.5 5.0 4.5 4.0 3.5 Transportational Transformational INFO Condition TRSF Condition TRSP Condition Control Note: all scales are seven-point scales ranging from the most negative (1) to the most positive (7). Results Overall, the multivariate analysis with all three dependent variables—ad attitude, brand attitude and purchase intention in the model found statistically significant differences in the dependent variables among the four study conditions, F (9, 825) = 7.34, p < .000. η2 p = .08. Descriptive results of the dependent variables across the four study conditions can be seen in Table 15. Hypothesis testing was conducted using post hoc analyses. Among the control variables—demographics, brand familiarity, attitude toward and purchase history of Nike running shoes—only education was significant in models with purchase intention as the dependent variable. Hence, education was maintained in all models with purchase intention as the dependent variable. 58 Table 15: Descriptive results of study 2 (N = 280). INFO TRSF TRSP M 5.23 5.50 5.93 Control 5.02 Ad Attitude Brand Attitude Purchase Intention SD 1.03 .71 .68 .72 M 5.11 5.42 5.92 4.94 SD 1.03 .81 .74 .58 M 4.90 5.33 5.47 4.65 SD .79 .84 1.00 .76 Note: all scales are seven-point scales ranging from the most negative (1) to the most positive (7). Hypothesis 1 H1 proposed that ads that focus on one advertising strategy (informational, transformational, or transportational) will yield more favorable ad attitudes (H1a), brand attitudes (H1b), and purchase intentions (H1c) compared to an ad that has moderate levels of all three strategies. The study’s first three conditions consisted of three ads that featured one of the three advertising strategies—INFO, TRSF and TRSP. The control condition was an ad that had been tested in study 1 that combined a moderate level of all three advertising strategies. Subsequent analyses were conducted to test each one of the three study conditions against the control condition in terms of the ad outcome variables. As shown in Table 16 and Figure 10, post hoc analysis showed that for ad attitude, brand attitude and purchase intention, there was no statistically significant difference between INFO and the control condition (mean difference ranged from .16 to .23, ns). This indicated that the INFO ad performed similarly to the control condition. However, respondents indicated significantly more positive ad attitudes, brand attitudes and purchase intentions when viewing the TRSF and TRSP ads compared to the control condition ad (TRSF vs. control: mean difference ranged from .47 to .66, p value ranged from < .01 to < .001; TRSP vs. control: mean difference ranged from .82 to .92, p < .001). The results suggested that the TRSF and TRSP ads 59 outperformed the control condition ad for all three dependent variables. This result brought partial support to H1. Table 16: Pairwise comparisons between pairs of study conditions (N = 280). Ad Attitude Brand Attitude Purchase Intentionª Mean Difference Mean Difference Mean Difference INFO vs. Control .21 TRSF vs. Control TRSP vs. Control INFO vs. TRSF INFO vs. TRSP TRSF vs. TRSP .47** .92*** -.26 -.71*** -.44** .16 .48** .99*** -.31 -.82*** -.51** .23 .66*** .82*** -.43* -.59*** -.16 Note: ª controlling for participant education level. * p < .05, ** p < .01, *** p < .001 Figure 10: Estimated means of the dependent variables for the four conditions (N = 280). e d u t i t t A d A n a e M 6 5.8 5.6 5.4 5.2 5 4.8 4.6 e d u t i t t A d n a r B n a e M n o i t n e t n I e s a h c r u P n a e M INFO TRSF TRSP Control INFO TRSF TRSP Control INFO TRSF TRSP Control Note: all scales are seven-point scales ranging from the most negative (1) to the most positive (7). Hypothesis 2 H2 posited that a transportational advertising strategy will have more favorable effects on ad attitude (H2a), brand attitude (H2b), and purchase intention (H2c) compared to an 60 informational strategy. Pairwise comparison analysis was conducted between the INFO and TRSP conditions to test the hypothesis. Results of the pairwise comparison showed that the TRSP condition consistently outperformed the INFO condition for ad attitude (mean difference = .71, p < .001), brand attitude (mean difference = .82, p < .001) and purchase intention (mean difference = .59, p < .001). See Table 16 and Figure 10. Thus, H2 was supported. Research Question The research question asked how a transformational advertising strategy will perform in terms of ad attitude, brand attitude, and purchase intention when compared to informational and transportational advertising strategies. Pairwise comparison analyses were conducted between the INFO and TRSF conditions and between the INFO and TRSP conditions to answer the research question. As seen in Table 16 and Figure 10, the TRSF condition surpassed the INFO condition only in purchase intention (mean difference = .43, p < .05), while the two conditions did not differ in terms of ad attitude (mean difference = .26, ns) and brand attitude (mean difference = .31, ns). Between TRSF and TRSP, respondents rated the TRSF ad lower in terms of ad attitude (mean difference = -.44, p < .01) and brand attitude (mean difference = -.51, p < .01), but not in purchase intention (mean difference = -.16, ns). There was no statistically significant difference between TRSF and TRSP ads regarding purchase intention. Thus, respondents reported greater purchase intention when viewing the TRSF ad than the INFO ad, and more favorable ad attitudes and brand attitudes when viewing the TRSP ad than the TRSF ad. 61 Discussion Built upon the findings of study 1, study 2 further examined the different effects of advertising strategies based on the overlapping model of advertising strategies. Specifically, study 2 tested whether an ad focused on one advertising strategy can have more favorable ad outcomes than an ad that combined moderate levels of different advertising strategies. Furthermore, using a new TRSF condition, study 2 further compared the different effects of the three advertising strategies on ad outcomes. To improve external validity, study 2 employed a real brand name instead of fictional brand names as used in study 1. The findings suggested that ads focused on either the transformational strategy or a transportational strategy yield more desirable ad outcomes than an ad with a combined strategy. As noted in the literature review, both transformational and transportational advertising strategies rely on evoking strong emotions and associate those emotions with either the product/brand or the story. This is especially powerful in creating positive ad outcomes. Previous studies have found that highly emotional ads can elicit more positive attitudes toward the advertising goal (Hamelin, El Moujahid, & Thaichon, 2017), greater purchase intent (McDuff et al., 2015), and more ad recalls (Mehta & Purvis, 2006) than low emotional ads. Thus, this implies that the success of the transformational and transportational strategies is due to their strong emotional focus. Further, the TRSP ad exceeded the control ad for ad outcomes in greater magnitudes than the TRSF ad. This is not surprising given that the transportational strategy aims at not only evoking strong emotions but also reducing critical counterarguments to the ad claim by transporting the message recipients to the story world (Green & Brock, 2000). By definition, the transformational strategy works by communicating the product use experience in an emotional 62 way to associate the product and brand with a unique set of psychological characters (Puto & Wells, 1984). This rather simple process limits the ad’s potential to offer other ways to persuade, one of which is through narrative transportation. As discussed earlier, narrative transportation is a unique pathway to persuasion that traditional dual-processing models failed to capture (Green et al., 2008). The findings of the current study brought support to the above-mentioned claim that a transportational strategy may indeed be a more powerful persuasion tool than other advertising strategies. Moreover, the study’s findings suggest that an ad without a focused strategy not only falls short in eliciting attitude/intention but may also lack in key information to judge the merits of the product and brand. Although the differences between the INFO and the control conditions were not statistically significant, the mean differences favored the INFO condition (INFO vs. control: mean difference ranged from .16 to .23). It is possible that given a larger sample size, ads that use an informational strategy may be able to outperform ads without an intentional ad strategy focus. Overall, the findings brought some support to the overlapping model of advertising strategies, in that a focused ad may be more powerful than an ad with combined strategies, especially when the focus strategy is transformational or transportational. When comparing the three advertising strategies, it is clear that transportational strategy won the race. Transportational strategy not only outranked the informational strategy in every aspect, but it was also rated higher in ad attitude and brand attitude than the transformational strategy. The transformational strategy came in second place by generating greater purchase intention than the informational strategy. 63 The finding that the transformational strategy outperformed informational strategy in purchase intent is consistent with Laskey et al. (1995), which found that a transformational strategy is superior to an informational in persuasion but it is relatively weaker in key message comprehension. In practice, the transformational strategy is effective toward hedonic consumption but has no effect on functional benefits (Naylor et al., 2008), while the informational strategy is particularly useful in utilitarian situations (Lee et al., 2011). Although this study found that a transformational strategy may outperform an informational strategy in purchase intention, certain caveats should be taken into consideration. First, it is unclear that the product category—Nike running shoes—belongs to the hedonic or the utilitarian side of consumption. Individual differences in brand perception as well as brand-congruity may also influence ad outcomes. Future studies should measure perceived hedonic/utilitarian brand values when comparing the effects of different advertising strategies. Second, the format of the ads— display ad, such as print or digital still image—limits the amount of information available in the ad copy. It is possible that a larger format or a video/interactive ad format could introduce more relevant information about the product and brand and may result in higher ad evaluations. Future studies should consider using other advertising formats such as video to test the effects of different strategies. Finally, the findings of the current study apply to select ad outcomes: ad and brand attitudes, and purchase intention. Researchers should also consider other advertising evaluation measurements especially retrospective measures. Although it is quite common to apply outcome measures immediately following exposure, ad recall and memory are also crucial in understanding advertising effects. For example, previous studies have found positive associations between emotional content and memory (Bakalash & Riemer, 2013; Sharot & 64 Yonelinas, 2008). Retrospective measures of ad recall and memory may yield more insights in understanding advertising strategies that use an emotional approach. Finally, the study’s findings revealing that a transportational strategy outperforms a transformational strategy in ad and brand attitude was as expected. As discussed in the previous section, narrative transportation is a unique pathway to persuasion that not only evokes strong emotions but also reduces critical scrutiny of the ad (Green & Brock, 2000; Green et al., 2008). This gives an advantage to transportational strategy over the transformational strategy, which relies on associating the product and brand with a certain set of emotional characteristics. To date, only a handful of studies has examined narrative transportation’s effects in increasing emotional response and reducing critical ad scrutiny (e.g., Escalas, 2004, 2007; Escalas & Luce, 2003; Zheng, 2010). None used real-world video commercials for this purpose. Thus, this dissertation conducted an additional study that was framed under the transportational advertising strategy operational definition to further examine the unique effects of transportational strategy using real-world video ads. 65 CHAPTER 5 STUDY 3 – TESTING MEDIATORS WITH REAL VIDEO ADS Study 3 was designed to explore the transportational process in more detail and test potential mediators as inferred from the definition of a transportational advertising strategy (Green & Brock, 2000). Green and Brock (2000) state that a transportation approach works by engaging the reader in the story world so that they will be less likely to generate counterarguments to the media message. Similarly, Escalas (2004) found that transportation increases attitudes toward the ad and brand evaluation by reducing critical cognitive responses and eliciting positive emotions. This process is more effective than analytical processing alone (Escalas, 2004). However, previous studies (including Escalas, 2004, 2007; Escalas & Luce, 2003; Zheng, 2010) mainly used self-referencing mental simulation, in which respondents were asked to imagine the narrative themselves following the instructions in the stimuli, instead of constructing and manipulating narrative advertisements. Like Zheng (2010) stated in her study, using a self-referencing mental simulation manipulation is not practical in the real world. Thus, in study 1 and 2, transportational advertisements that employed stories were created following the transportational advertising strategy operational definition and tested in comparison to two other advertising strategies—namely, informational and transformational. Study 3 took a step further by using real video advertisements to test the transportational process and its effect on advertising evaluation. Green and Brock (2000) argue that analytical cognitive responses dominate when the reader is not being transported by the media content. Counterarguments generated by the reader’s cognitive response have been found to mediate ad message acceptance (Wright,1973). Furthermore, prior findings regarding narrative transportation used in advertising (Escalas, 2004, 66 2007; Escalas & Luce, 2003) support that immersion into a story distracts ad viewers from thinking critically about the advertisement, thus, reducing critical scrutiny of the advertising claim. Slater and Rouner (2002) found that the story used in narrative transportation may reduce critical thought even when the story plot is inconsistent with a respondent’s prior beliefs. Based on the above findings, ads that employ a transportational strategy with story elements are predicted to reduce critical thinking about the ads’ claims and, in turn, enhance ad and brand evaluations. In addition, Escalas (2004) found that narrative transportation increases viewers’ upbeat feelings. Chang (2009) indicates that narrative transportation ads produce more warm feelings than analytical ads. This coincides with a real-world situation where, in practice, most advertisers want to associate their brands/products with positive attributes. Thus, study 3 used an operational definition of the transportational advertising strategy that is consistent with the above reasoning: transportational advertising strategy is an advertising strategy that focuses on a story of one or more characters, or a slice-of-life representation of the character(s), always in a positive light. Through immersion into the story, a consumer engages, feels and transports without much critical scrutiny of ad claims, which results in positive affective responses. The positive responses then transfer to the product/brand being advertising (Escalas, 2004), thus, leading to a positive evaluation of not only the ad but also the product and brand. Based on the above discussion, study 3 proposed the following hypotheses: H1: A transportational advertising strategy reduces critical thoughts, which positively mediates the transportational strategy’s effects on ad attitude (H1a), brand attitude (H1b) and purchase intention (H1c). 67 H2: A transportational advertising strategy increases affective responses, which positively mediates the transportational strategy’s effects on ad attitude (H2a), brand attitude (H2b) and purchase intention (H2c). In addition to testing the mediation effects, study 3 also proposed that a transportational strategy will have different effects on ad evaluation based on the consumers’ perceived level of transportational strategy in the ad. Thus, a hypothesis was raised as follow: H3: Ads that use a transportational advertising strategy will have positive effects on ad attitude (H3a), brand attitude (H3b) and purchase intention (H3c). The higher the perceived level of a transportational strategy, the more positive the ad evaluations (H3d). The hypothesized mediating process is illustrated in Figure 11. Figure 11: Transportational process mediation framework. Transportational Strategy H1 – H2 + Critical Thoughta H3 + Affective Responseb H1 – H2 + Ad Attitude Brand Attitude Purchase Intention Note: a Critical thought is measured by the number of support arguments, counterarguments, and source derogations in participants’ responses (Escalas, 2004). b Affective response is measured by the following items: active, alive, cheerful, delighted, energetic, happy, pleased, and stimulated on a seven-point Likert-Type scale anchored by not well (1)/well (7) (Goodstein, Edell, and Moore, 1990). 68 Methods Study 3 consisted of a within-subjects factorial design with three repetitions (product category repetition) and two mediating variables (critical thoughts and affective response). The dependent variables were ad attitude, brand attitude and purchase intention of the advertised product. To increase external validity, study 3 used authentic video advertisements (launched on multiple TV and digital channels) from global commercial brands. All stimuli were pretested to ensure high levels of transportational strategy. A total of three video ads from different product categories were used. Study 3 chose three ads that showed different levels of perceived transportational strategy to test the strategy’s various effects at different levels (low, medium, and high). Pretest A pretest using an independent sample of Amazon Mechanical Turk subjects (N = 80) was designed to test the video ads for perceived levels of transportational strategy used in each ad. Six ads from six different brands the researcher deemed to be transportational were used as candidates: Chipotle ([SAMPICS ANIM, S-ROSE STUDIO], 2017), Extra Gum ([extragum], 2015), Nike ([Nike], 2019b), P&G ([Creativity - Cannes Lions], 2017), Pandora ([TheOfficialPandora], 2015), T-Mobile ([T-Mobile], 2019). At the beginning of the pretest, participants were asked to rate their general attitudes toward and involvement with the six brands. Then they were exposed to the six ads in random order and evaluated the ads in terms of perceived transportational strategy, ad attitude, brand attitude and purchase intention (see Table 3 for the scales). In order to test the different effects of various levels of perceived transportational strategy on ad outcomes, three ads with different ratings of narrative transportation were selected. Descriptive results can be seen in Table 17. 69 Table 17: Perceived levels of transportational strategy used in the study 3 pretest ads. Stimuli Chipotle Ad Extra Gum Ad Nike Ad P&G Ad Pandora Ad T-Mobile Ad TRSP M 4.35 5.01 4.99 4.02 4.58 5.31 SD 1.18 1.29 1.45 1.44 1.43 1.25 Note: all scales are seven-point scales ranging from the most negative (1) to the most positive (7). As indicated in the results, the P&G ad, the Extra Gum ad and the T-Mobile ad were rated most differently in the perceived TRSP levels (in order of low, medium, and high). Furthermore, the pretest results showed that the general attitudes toward the three brands did not differ from each other significantly (ns) nor did the involvements of the three brands (ns). Thus, the P&G ad, the Extra Gum ad, and the T-Mobile ad were selected for the study 3 main test. Participants A power analysis was conducted to determine the proper sample size for study 3. Using G*Power 3.1.9.4 (Faul et al., 2007), a total sample size of 114 was calculated based on an effect size of .10, p value of .05 with a power of 80% for a three-predictor linear multiple regression analysis. Participants (N = 200) were recruited from Amazon Mechanical Turk. The Mechanical Turk subjects participated in the study anonymously and received $1.502 as compensation for completing the survey. See Table 18 for the demographics of the participants. 2 The payment was based on an estimated 12-minute survey completion time and the current federal minimum wage of $7.25 per hour. 70 Table 18: Demographics of the study 3 participants (N = 200). Variables Sex Male Female Age 18-25 26-30 31-35 36-40 41-45 Percent 47.5% 52.5% 12.0% 19.5% 24.5% 24.0% 20.0% Ethnicity 3.5% American Indian of Native 5.0% Asian American 15.5% Black/African American Latino 1.0% Two or more Ethnicities/Races 1.5% White/Caucasian Other 72.5% 1.0% Marital Status Never Married Married Separated Divorced Education Less than high school High school graduate Some college, no degree Associate’s degree Bachelor's degree Master's degree Professional degree Doctorate degree Income Less than $10,000 $10,000 - $49,999 $50,000 - $99,999 $100,000 - $149,999 $150,000 or more 49.0% 46.0% .5% 4.5% 1.0% 19.0% 21.0% 8.0% 31.5% 15.5% 1.5% 2.5% 5.0% 29.0% 42.0% 13.0% 11.0% 71 Procedures Study 3 followed similar procedures as in study 1. Upon registering for the study, the Amazon Mechanical Turk subjects were directed to the study’s Qualtrics survey where they provided their consent to participate in study 3. Participants were then instructed to view and evaluate all three ads displayed in randomized order. Participants finished viewing the entire ad then proceeded to the evaluation stage. Upon finishing evaluating all the ads, participants answered the control variable questions and the demographic questions and were routed back to Amazon Mechanical Turk to enter a randomly generated code to receive payment. Stimuli Three video ads were pretested and selected for the study, the P&G “Strong - Thank You, Mom Campaign” (two minutes), the Extra Gum “The Story of Sarah & Juan” (one minute and 57 seconds), and the T-Mobile “Jake’s Dad” (one minute). All ads were played unaltered in 1280 x 720 pixel, or 16:9 format with similar levels of sound volume. See Figure 12 for the screenshots of the ads. The screenshots were taken at fixed intervals according to the specific length of the video. For example, for the two minute P&G ad, a screenshot was taken at the beginning of the ad, subsequent screenshots were taken 12 seconds apart. 72 Figure 12: Screenshots of study 3 stimuli. P&G “Strong - Thank You, Mom Campaign” ([Creativity - Cannes Lions], 2017) Extra Gum “The Story of Sarah & Juan” ([extragum], 2015) T-Mobile “Jake’s Dad” ([T-Mobile], 2019). Independent Variable (IV) Perceived Transportational Strategy. Study 3 used a modified narrative transportation scale short form to measure perceived transportation (Appel et al., 2015). Participants rated five items on a seven-point Likert scale anchored by (1) not at all to (7) very much (see Table 3 for the items). Mean Cronbach’s α = .85. 73 Mediators (M) Critical Thoughts (CT). Study 3 adopted the thought listing process used by Escalas (2004) based on Wright’s response taxonomy (1973). After viewing each ad, participants were asked to type what they were thinking while viewing the ad into a text box. The responses were then coded into the following categories: support arguments, counterarguments, and source derogations by two independent coders unaffiliated with the study. According to the advertising response taxonomy developed by Wright (1973), support arguments activate when the incoming information is congruent with existing beliefs Alternatively, counterarguments emerge when the incoming advertising information is different from a viewer’s existing belief system. For example, in response to an ad’s claim about the advantage of a certain product, the consumer may immediately think of a disadvantage or an alternative solution to the problem described in the ad. (Wright, 1973). Finally, source derogation focuses on an ad viewer’s negative evaluations of a sponsoring organization or a spokesperson instead of the ad’s claim (Wright, 1973). Source derogations can be viewed as a form of counterargument when the source is viewed as biased, this makes source derogations especially suitable for studying advertisements. Codes of the response taxonomy were entered into Microsoft Excel and the intercoder reliability coefficients were calculated by using ReCal (Reliability Calculator), an online utility that computes intercoder reliability coefficients (Freelon, 2010, 2013). The average measure of agreement for the responses was .88, which was deemed satisfactory (Krippendorff, 2004). Affect Responses (AR). Study 3 used a modified version of Goodstein, Edell, and Moore’s (1990) feelings scale to measure affective responses to the ads. Participants rated how well the following words describe how they felt while viewing the ad: active, alive, cheerful, delighted, 74 energetic, happy, pleased, stimulated. Each item was rated on a seven-point Likert-Type scale anchored by not well (1)/very well (7). Mean Cronbach’s α = .78. Dependent Variables (DV) Ad Attitude (Aatt). Study 3 measured attitudes toward the ad using an adapted scale from MacKenzie and Lutz (1989). Items are rated on a seven-point semantic differential scale anchored by negative (1)/positive (7), unpleasant (1)/pleasant (7), bad (1)/good (7), and unfavorable (1)/favorable (7) (see Table 3 for the measures). Mean Cronbach’s α = .89. Brand Attitude (Batt). Study 3 measured attitudes toward the brand using Mitchell and Olson’s (1981) four-item scale. Each item entailed on a seven-point semantic differential scale anchored by negative (1)/positive (7), unpleasant (1)/pleasant (7), bad (1)/good (7), and unfavorable (1)/favorable (7). Mean Cronbach’s α = .92. Purchase Intention (PI). Intention to purchase the advertised product was measured by the scale developed by Terlutter, Diehl, and Mueller (2006). Items were rated on a seven-point Likert-Type scale anchored by very unlikely (1)/very likely (7). Mean Cronbach’s α = .90. Control Variables Control variables were: select demographics, previous exposure to the ad, brand familiarity, attitude toward and purchase history of the advertised product. Demographics. Age, sex, ethnicity, marriage, education, and household income were control variables. Previous Exposure to the Ad. Participants were asked if they have previously seen the study stimuli and if so, how many times. Brand Familiarity. Participants rated brand familiarity using a single item seven-point semantic differential scale anchored by not at all familiar (1)/extremely familiar (7). 75 Attitude toward the Product. Participants were asked to rate their general attitudes toward each of the three products used in the study on a three-item seven-point semantic differential scale anchored by negative (1)/positive (7), bad (1)/good (7), and unfavorable (1)/favorable (7). Mean Cronbach’s α = .77. Purchase History of the Advertised Product. Participants were asked if they have purchased the product being advertised in the past 12 months and if so, how many times. Data Analysis This study used the PROCESS macro on SPSS (Hayes, 2017) to estimate direct and indirect effects of the mediation models. PROCESS is an OLS and logistic regression path analysis tool widely used in many disciplines for mediation and moderation analyses. This study used PROCESS v3 with 5,000 bootstrap samples for the full mediation models, and PROCESS v2 with 1,000 bootstrap samples and the Sobel tests for the partial mediation models. The Sobel test is a specialized t test that provides estimates of whether the reduction in the direct effect of the independent variable given the indirect effect of the mediator is statistically significant. A statistically significant Sobel test indicates that the mediation effect is statistically significant (Preacher & Leonardelli, 2001). The Z value generated by the Sobel test is similar to the t value in a typical t test, whereas the κ2 value can be seen roughly as the R2 in regression analysis, which indicates the percentage of variance explained by the model (Preacher & Kelley, 2011). Additionally, repeated ANOVA analysis was conducted to test the differences among the three study stimuli for all variables. 76 Results Hypothesis 1 H1 proposed that the use of a transportational advertising strategy will reduce critical thoughts, and this effect will positively mediate the transportational strategy’s effects on ad attitude (H1a), brand attitude (H1b) and purchase intention (H1c). To confirm the mediation effect, four conditions must be met (Baron & Kenny, 1986): (1) it has to be true that the IV has a significant effect on the DV (Figure 13 path cdirect); (2) the IV must also have a significant effect on the mediator (Figure 13 path a); (3) the mediator must significantly affect the DV (Figure 13 path b); (4) when the mediator is included in the model, the original path cdirect becomes not significant (full mediation—the mediator fully mediates the relationship between the IV and DV) or the reduction in the direct effect (path cindirect vs. path cdirect) is statistically significant (partial mediation—the mediator partially mediates the relationship between the IV and DV). Figure 13: Mediation path model. a IV M b cdirect / cindirect DV Note: path a: IV predicts M; path b: M predicts DV when M is entered into the model; path cdirect: IV predicts DV directly; path cindirect: IV predicts DV when M is entered into the model. Satisfying the first condition, the perceived transportational strategy positively predicted ad attitude, brand attitude and purchase intention across all three study stimuli (see paths cdirect in Table 19). Results showed that F (1, 198) ranged from 4.30 to 18.05, p value ranged from < .01 to < .001, R2 ranged from .02 to .08, and βIV ranged from .10 to .44. None of the control variables showed significant effects in the models. 77 Meeting the second condition, the perceived transportational strategy negatively predicted critical thoughts across all stimuli (see paths a in Table 19). Results showed that F (1, 198) ranged from 4.49 to 43.08, p value ranged from < .05 to < .001, R2 ranged from .02 to .07, and βIV ranged from -.14 to -.42. Table 19: Critical thoughts mediation model results for the three study stimuli (N = 200). Patha F 43.08* 7.97*** 5.01* 10.78*** 9.67** ns R2 .03 .07 .03 .10 .05 Z = 1.98*, κ2 = .04 10.55*** 9.57** .10 .05 Z = 1.96*, κ2 = .04 4.49* 9.77*** 4.30* 8.92*** 7.43** .02 .09 .02 .08 .04 ns Z = 1.70†, κ2 = .03 9.53*** 7.15** .09 .04 Z = 1.74†, κ2 = .04 14.68*** 15.36*** 18.05*** .07 .13 .08 Z = 2.50*, κ2 = .06 15.73*** 17.99*** .13 .08 Z = 2.50*, κ2 = .06 6.76** 10.15** .06 .05 Z = 1.59*, κ2 = .03 β -.14* -.17*** .10** .07 -.17*** .12** .10* -.16*** .12** .10* -.17** -.17*** .11* .08 -.14*** .14** .12* -.15*** .13** .11* -.42*** -.22*** .43*** .34** -.22*** .44*** .33** -.15† .34** .34* Stimulus P&G Extra Gum T-Mobile DV Aatt Batt PI Aatt Batt PI Aatt Batt PI a b cdirect cindirect b cdirect cindirect b cdirect cindirect a b cdirect cindirect b cdirect cindirect b cdirect cindirect a b cdirect cindirect b cdirect cindirect b cdirect cindirect 78 Note: a a: IV predicts M; b: M predicts DV when M is entered into the model; cdirect: IV predicts DV directly; cindirect: IV predicts DV when M is entered into the model. † p < .10, * p < .05, ** p < .01, *** p < .001. Since the study has multiple DVs, the three DVs each have their own path b and paths cdirect / cindirect. However, there is only one path a for each of the mediators. Meeting the third condition, across all three ads, critical thoughts negatively predicted all three DVs (see paths b in Table 19). Results showed that F (2, 197) ranged from 6.76 to 15.73, p value ranged from < .01 to < .001, R2 ranged from .06 to .13, and βM ranged from -.14 to -.22. However, the βM for PI in the T-Mobile ad was not significant (ns, see path b for the T-Mobile ad in Table 19). Still, the mediation model for the T-Mobile ad remained significant. For the final condition, full mediation was achieved for the mediation model with ad attitude as the DV in the P&G and the Extra Gum ads—the addition of critical thoughts reduced the direct effects of the perceived transportation strategy to be non-significant (see paths cindirect for the P&G and the Extra Gum ads in Table 19). In other words, the paths cindirect for ad attitude in the P&G and the Extra Gum ads became non-significant when the mediator was added into the model. Results showed that, for ad attitude, βIV ranged from .07 to .08, ns. Thus, full mediations were achieved for ad attitude in the two ads. However, for other DVs in the P&G and T-Mobile ads, only partial mediations were observed (see paths cindirect for the P&G and T-Mobile ads in Table 19). With the mediator added in the models, the cindirect paths remained significant. This meant that the mediator by itself did not fully predict the DVs, the IV still exerted a certain influence directly on the DVs. Yet, the effect sizes for those IVs were significantly weakened (as indicated by statistically significant Z values). Results indicated that the Z value ranged 1.59 to 2.50, p < .05, κ2 ranged from .03 to .06, and βIV ranged from .10 to .34. Thus, partial mediations were achieved. 79 For brand attitude and purchase intention in the Extra Gum ad, the mediation models failed to achieve statistical significance (see paths cindirect for the Extra Gum ad in Table 19). Results showed that the Z values ranged from 1.59 to 2.50, ns, κ2 ranged from .03 to .06. Thus, taking into account the above results, H1 was partially supported. Hypothesis 2 H2 posited that the use of a transportational advertising strategy will increase affective responses, which will mediate the transportational strategy’s effects on ad attitude (H2a), brand attitude (H2b) and purchase intention (H2c). As with hypothesis 1, similar conditions must be met to demonstrate the mediation effects. Results of the mediation models with affective responses as the mediator can be seen in Table 20. First, as shown in the results of hypothesis 1, a perceived transportational strategy positively predicted ad attitude, brand attitude and purchase intention across all three study stimuli (see paths cdirect in Table 19 and Table 20). Thus, condition 1 was met. Next, all the path a results in the models were significant, indicating that a perceived transportational strategy positively predicted affective responses across all three ads. Results showed that F (1, 198) ranged from 27.30 to 43.08, p < .001, R2 ranged from .12 to .18, βIV ranged from .35 to .42 (see paths a in Table 20). Condition 2 was met. Third, affective responses positively predicted ad attitude, brand attitude, and purchase intention for all three ads, F (2, 197) ranged from 11.65 to 33.74, p < .001, R2 ranged from .11 to .26, βM ranged from .16 to .37 (see paths b in Table 20). Condition 3 was met. Finally, with affective responses in the model as mediators, the direct effects of a perceived transportational strategy were not significant for ad attitude, brand attitude and purchase intention in all three ads, βIV ranged from .01 to .18, ns (see paths cindirect in Table 20). 80 Condition 4 was met. The results indicated that affective responses fully mediated the relationships between the perceived transportational strategy and the DVs. Thus, hypothesis 2 was supported. 81 Table 20: Affective responses mediation model results for the three study stimuli (N = 200). Stimulus P&G Extra Gum T-Mobile DV Aatt Batt PI Aatt Batt PI Aatt Batt PI Patha F a b cdirect cindirect b cdirect cindirect b cdirect cindirect a b cdirect cindirect b cdirect cindirect b cdirect cindirect a b cdirect cindirect b cdirect cindirect b cdirect cindirect 43.08*** 13.19*** 5.01* 17.54*** 9.67** 17.64*** 9.57** 35.22*** 11.65*** 4.30* 13.86*** 7.43** 13.81*** 7.15** 27.30*** 33.74*** 18.05*** 33.73*** 17.99*** 20.45*** 10.15** R2 .18 .12 .03 .15 .05 .15 .05 .15 .11 .02 .12 .04 .12 .04 .12 .25 .08 .26 .08 .17 .05 ns ns ns ns ns ns ns ns ns β .42*** .16*** .10* .01 .17*** .12** .04 .17*** .12** .04 .39*** .19*** .11* .02 .19*** .14** .05 .19*** .13** .05 .35*** .30*** .43*** .18 .30*** .44*** .17 .37*** .34** .17 Note: a a: IV predicts M; b: M predicts DV when M is entered into the model; cdirect: IV predicts DV directly; cindirect: IV predicts DV when M is entered into the model. † p < .10, * p < .05, ** p < .01, *** p < .001. Table 19 and 20 had identical cdirect values for the respective DVs because the cdirect paths did not take into account the two mediators. 82 Hypothesis 3 H3 posited that the use of a transportational advertising strategy will have positive effects on ad attitude (H3a), brand attitude (H3b) and purchase intention (H3c), and that the higher the message recipients perceived level of transportational strategy in the ads, the more positive the ad evaluations (H3d). To test H3a to c, one-sample t tests were performed to confirm that all DVs were significantly larger than zero. To test H3d, a series of repeated ANOVA analyses were conducted to test the differences across the three ads for all DVs. Descriptive results of the study variables can be seen in Table 21. Table 21: Descriptive results of study 3 main study (N = 200). Stimulus Variables TRSP Aatt Batt PI P&G M 4.31 5.13 4.73 4.40 SD .92 .94 .94 .92 Affective response 4.48 1.97 Extra Gum T-Mobile Critical thought TRSP Aatt Batt PI 3.09 5.12 5.12 5.03 4.52 Affective response 4.20 Critical thought TRSP Aatt Batt 3.19 5.53 5.16 5.06 4.56 PI Affective response 5.02 Critical thought 2.60 1.28 1.63 1.39 1.39 .92 1.69 1.48 1.29 .95 .92 1.69 1.59 1.27 Note: all scales are seven-point scales ranging from the most negative (1) to the most positive (7). 83 Results of the one-sample t test showed that ad attitude, brand attitude and purchase intention for all three ads were significantly larger than zero, t (199) ranged from 51.27 to 77.77, p < .001. In addition, the means of all DVs were higher than the mid-point value (4 on a 7-point scale) across the three ads (see Table 21). H3a to c were supported. Consistent with the pretest results (see Table 17), the three ads held relative similar perceived TRSP levels, in that on average, the T-Mobile ad was rated as having the highest TRSP level (M = 5.53, seven-point scale ranging from the most negative to the most positive), followed by the Extra Gum ad (M = 5.12) and then P&G ad (M = 4.31). To test H3d, the first step was to confirm that the three ads had different levels of perceived transportational strategy. A repeated ANOVA test with perceived TRSP as the DV showed that the T-Mobile ad showed the highest perceived TRSP, followed by the Extra Gum ad and the P&G ad, F (2, 198) = 118.26, p < .001. Next, a series of repeated ANOVA tests were performed with ad attitude, brand attitude and purchase intention as the DVs. For ad attitude, there was no statistically significant difference found among the three ads, F (2, 198) = 1.39, ns. For brand attitude, significant differences were found, F (2, 198) = 396.71, p < .001. Post hoc analysis showed that the Extra Gum ad and the T-Mobile ad outperformed the P&G ad (mean difference ranged from .30 to .33, p value ranged from < .01 to < .001, see Table 22). The Extra Gum ad did not differ from the T-Mobile ad in terms of brand attitude. For purchase intention, pairwise comparisons showed that the T-Mobile ad differed significantly from the P&G ad (mean difference = .16, p < .001). No statistically significant results were found between other ad pairs (see Table 22 and Figure 14). 84 Table 22: Pairwise comparisons across the three ads in study 3 for ad attitude, brand attitude and purchase intention (N = 200). Ad Attitude Brand Attitude Purchase Intention Mean Difference Mean Difference Mean Difference P&G vs. Extra Gum P&G vs. T-Mobile Extra Gum vs. T-Mobile .04 .04 -.01 -.30** -.33*** -.03 -.12 -.16*** -.04 Note: † p < .10, * p < .05, ** p < .01, *** p < .001 Figure 14: Estimated means of perceived transportational strategy, ad attitude, brand attitude and purchase intention for the three ads (N = 200). P S R T d e v i e c r e P n a e M 5.6 5.4 5.2 5.0 4.8 4.6 4.4 4.2 e d u t i t t A d n a r B n a e M P&G Extra Gum T-Mobile n o i t n e t n I e s a h c r u P n a e M e d u t i t t A d A n a e M 5.6 5.4 5.2 5.0 4.8 4.6 4.4 4.2 P&G Extra Gum T-Mobile P&G T-Mobile P&G Extra Gum T-Mobile Extra Gum Note: all scales are seven-point scales ranging from the most negative (1) to the most positive (7). 85 Additionally, it can be observed from Table 19 and 20 that the T-Mobile ad had the highest direct βIV levels—regression coefficient (ranged from .34 to .44), followed by the Extra Gum ad (βIV ranged from .11 to .14) and the P&G ad (βIV ranged from .10 to .12). The βIV level indicated that for a certain level of increase in the IV (perceived transportational strategy), there was a one unit increase in the DV. For example, a .44 increase in perceived transportational strategy in the T-Mobile ad was associated with one unit increase in ad attitude, this increase was statistically significant, F (1, 198) = 18.05, p < .001, βIV = .44 (see Table 19 or Table 20). Based on the above analyses, H3d was supported. Discussion Study 3 was designed to explore the transportational mediation framework by employing two mediators suggested by previous literature. Specifically, Green and Brock (2000) proposed that narrative transportation is a unique pathway to persuasion not only by embedding the reader in a story world filled with emotions, but also reduce the reader’s critical scrutiny to the story claims through immersion into the story. This proposition was tested by a number of studies in the advertising literature using fictious ads and brands (e.g., Chang, 2009; Escalas, 2004). To further validate the mediation process of narrative transportation, study 3 tested real-world video ads produced by large global brands. The results were encouraging. The transportational advertising strategy worked by reducing critical thoughts toward the advertising product and brand. This relationship partially mediated the outcome variables, namely ad attitude, brand attitude, and purchase intention. Two out of the three ads used in this study demonstrated partial mediation effects by reducing critical thoughts, which suggests that the mediating effect of critical thoughts did not fully explain the effects of the transportation strategy. Additional factors might be at work here, such as affective responses. 86 Next, study 3 found that a transportational strategy worked by increasing affective responses toward the ad and the brand. This relationship fully mediated the outcome variables. That is, with the inclusion of affective responses, the direct effects of a transportational strategy greatly decreased to be non-significant. In fact, judging from the increase of R2 before and after adding affective responses, one can deduce that the inclusion of the mediator greatly improved the explanatory power of the models. For example, the R2 increased by 212% (.25 vs. .08) for ad attitude, 225% (.26 vs. .08) for brand attitude and 240% (.17 vs. .05) for purchase intention in the T-Mobile ad, and these increases were statistically significant. The finding that affective responses fully mediated the transportational process while critical thoughts only partially mediated said relationship deserves some thought. A possible explanation is that the stimuli chosen for study 3 convey mostly positive emotions—resolving a father-son conflict in the T-Mobile ad, lovers reunited in the Extra Gum ad, and loving mothers in the P&G ad. By nature, positive emotions tend to elicit more positive affective responses (Deighton et al., 1989), which can result in more robust mediation effects. It is possible that an ad that elicits mainly negative emotions, e.g, anti-smoking PSAs using fear appeals, will produce some different mediation results. Future studies should analyze ads that tap into negative emotions to gain a better picture of the transportational process. Overall, the above results support the narrative transportation framework proposed by early studies (Green & Brock, 2000). With the mediating constructs—critical thoughts and affective responses, the transportational mediation process became more powerful as evidenced by the improvements in the R2s. However, it is still too early to say that these are definitive results for a transportational advertising strategy. The experimental nature of this study did not point to causality nor did it prove the correct sequence of occurrences. For instance, there may be 87 interaction effects between critical thoughts and affective responses, or even moderating mediation effects across the constructs. Additionally, factors other than narrative transportation might be affecting the mediators and the outcome variables, such as argument strength (Escalas, 2004), or vividness of product depiction (Zheng, 2010). Furthermore, considering the rich media format of a video ad, other elements of the ad, e.g., music, length of the video, models/celebrities featured in the ad might further complicate the understanding the ad effects. For instance, the Extra Gum ad used in study 3 featured a classic song “Can’t Help Falling in Love” by Haley Reinhart, a popular version of the song was recorded by Elvis Presley in 1961 (Presley, 1961). More empirical work needs to be done to reveal more of the effects of elements of a transportational strategy. The study 3 findings also showed that ads with higher perceived transportational strategy levels may carry more favorable ad outcomes than those with lower levels. An important assumption of the narrative transportation theory (Green & Brock, 2000) is the detachment from reality by engaging in the narrative. The story’s ability to deliver an out-of-reality experience is an important factor determining its transportational effect. A vivid story exerts a stronger influence in the ad over a weak one (Zheng, 2010), and the more immersed the reader in the story, the more likely that transportation will occur. The findings of this study were in line with the theoretical framework of narrative transportation in that the more the ad features elements of a transportational strategy, the more likely narrative transportation will occur, which may lead to more positive ad evaluations. Another key assumption of the narrative transportation theory that the current study did not test is the consumer’s imagination (Green & Brock, 2000) or the ability to be transported (Dunlop, Wakefield & Kashima, 2008). Transportability measures a message recipient’s 88 tendency to be transported by the message (Dal Cin, Zanna, & Fong, 2004). Individual differences in transportability may affect one’s ability to immerse into the story and thus be transported. Moreover, scholars have proposed other individual factors that may influence the transportational process, such as ease of imagery generation (Petrova & Cialdini, 2005) and the need for cognition (Green et al., 2008). Clearly, individual differences in viewers are a key factor in understanding the transportational process. Future studies should take into account such factors and map a more complete process of a transportational strategy in advertising. 89 CHAPTER 6 GENERAL DISCUSSION AND CONCLUSIONS This dissertation studied a transportational strategy’s potential in advertising persuasion in comparison to two traditionally recognized advertising strategies—informational and transformational. The author proposed an overlapping model of advertising strategies encompassing informational, transformational and transportational advertising strategies. It was proposed that an advertisement can use a combination of the three advertising strategies at varying degrees according to the advertiser’s strategic goal. Overall, this dissertation found that if executed effectively, a transportation advertising strategy can outperform an informational strategy or a transformational strategy in terms of ad evaluations of ad attitude, brand attitude and purchase intention. In the two product categories being tested in study 1, the transportational ads were consistently rated higher in all ad outcome variables for the running shoe ads and the headphone ads. A well-executed transportational ad— as defined by the operational definition of this dissertation—should focus on delivering a story that has the potential to evoke strong emotions and transport the consumer into the story world. The emotions associated with the story affect the consumer in a positive light, generating positive attitudes toward the ad and brand. This effect is stronger than the strategies that merely present product information. This finding echoes the industry’s insight into today’s digital advertising world. Because of the advancement of programmatic media buying, digital advertising is increasingly adapting artificial intelligence in not only media planning but also advertising creativity. Many online ads are generated by computer programs according to the relevance of the target audience and the context. Britt Nolan, Chief Creative Officer of DDB, spoke during an 90 event hosted by DePaul University’s College of Communication. He stressed that human emotion should be at the core of advertising creativity, not a machine or computer programming (Nolan, 2019). An ad may succeed by relying on technology to select and present the correct information, but an effective ad always evokes strong emotion that lingers on (Nolan, 2019). A case in point is Samsung’s multi-Cannes Lions-winning campaign by Leo Burnett Chicago (Jardine, 2017). Originally designed to be an informationally focused ad that features how Samsung connects with its users through its many technological advancements, the Samsung ostrich ad instead was made to tell an emotional story of an ostrich learning to fly after it tried on a VR goggle with a Samsung Galaxy S8 smartphone. The ad was part of the campaign “#DoWhatYouCan't”, symbolizing how Samsung’s technology can enable users to achieve something as miraculous as a flying ostrich (Jardine, 2017). Additional empirical evidence concerning advertising strategies was found in study 2 of the dissertation. Advertising strategies that focus on an emotional aspect received the highest ad rating compared to an informational ad or an ad that features moderate levels of combined strategies. Returning to the overlapping model of advertising strategies, study results infer that ads that focused on one strategy can be more effective than ads using multiple strategies. Results from study 2 also pointed to the advantage of a transportational strategy. As conceptualization of a narrative transportation ad suggests, a transportational strategy works via two pathways—reducing critical scrutiny to the ad claims and increasing affective responses. Study 3 empirically tested this conceptualization and found that both critical thoughts and affective responses mediated the relationship between a transportational strategy and ad outcomes. This finding is congruent with the narrative transportation theory (Green & Brock, 2000) and it sheds more light on a transportation process in a video advertising context. The 91 strong explanatory power of the mediation models also added to the existing evidence about a transportation mediation framework (Escalas, 2004). Finally, although the studies did not specifically test video ads against print ads, it is important to note that if properly executed, video can be a more effective vehicle for transportational advertising than print or still display. The reason is obvious, video can tell a more compelling and potentially engaging story—with moving pictures, music, and lively narration. Better stories lead to better narrative transportation (Green & Brock, 2000). In fact, rich media ads (including video) are among the fastest growing advertising channels in the U.S., projected to grow 20% in 2019 to reach more than $39.8 billion, while banners and other display ads account for only $25.9 billion (eMarketer, 2019). With the advancement in Internet advertising, it is clear that rich media formats will continue to maintain priority among marketers. Managerial Implications The author offers three main takeaways for advertising practitioners based on the findings of the current dissertation. First, rethink your advertising strategy to include a transportational strategy to increase ad likes and considerations (consumers’ willingness to consider your company and products). For campaigns that focus on attitude, engagement, and considerations, the transportational and transformational strategies are good ways to evoke emotion and increase recall. Regardless of the product category, an emotional appeal would connect the product/brand with the consumer at a deeper level more so than a strategy focused on listing product benefits. Ads that echo with consumer’s pain points (the specific problems that the consumer is experiencing) are especially effective in this regard. Moreover, a story would be a more effective avenue to carry that emotional message, especially when it immerses consumers into the story 92 world and detaches their mind from reality. Use rich media to present that story, including but not limited to music, narrative, characters, and even computer-generated images. As per Green and Brock’s (2000) conceptualization of narrative transportation, imagination is another key to a successful transportation experience. The consumer needs to be imagining the story as if it is happening to him/herself. This strategy not only reduces the consumer’s counterarguments but also increases ad/brand likings. Of course, a story requires certain conditions to be met in order to be successfully delivered. Such conditions include necessary space/length, multi/rich media, proper story-brand fit, interpretable storyline, a beginning and oftentimes a happy ending. In this regard, campaigns that merely look to increase awareness may not be suitable candidates for a transportational strategy, primarily due to the space/length consideration. Second, focus on using one particular advertising strategy in an ad. As the overlapping model of advertising strategies suggests, advertisements can be perceived to combine different advertising strategies. Having a focused message strategy is not only effective for branding but it can also release the full potential of that strategy. A transportational strategy should focus on telling an enticing story to immerse the consumer into the narrative. A transformational strategy, however, should focus on associating the product/brand with a unique set of emotions. An informational strategy should focus on presenting key product/brand benefits in a clear and logical way. Of course, every strategy requires a certain amount of information/emotion/story elements. The key is to minimize the noise of the other strategies to be subordinate to the primary strategy. This is best achieved by testing, testing and more testing. Third, during ad testing, add questions about critical counterarguments and affective responses to the survey. As study 3 of this dissertation revealed, critical thoughts and affective responses were two important mediators of a transportational strategy’s effect. To understand 93 why your transportational strategy is or is not working, these two constructs can provide valuable insights above and beyond the transportational scale. Also, try to incorporate other measures if you want to know more about the effects of your ads. Constructs that measure the individual difference in transportability (Dal Cin, Zanna, & Fong, 2004), ease of imagery generation (Petrova & Cialdini, 2005) and need for cognition (Green et al., 2008) are especially useful. Limitations and Future Study This dissertation has limitations. First, the studies were designed to further examine a transportational framework and provide insight into its potential to impact advertising effectiveness. However, this dissertation did not take into account the effects of emotional valence (the extent to which an emotion is positive or negative) on ad outcomes. Although according to Green and Brock (2000), the emotions associated with the narrative story need not be positive for narrative transportation to function. In their argument, any emotions evoked by the story (whether they are positive or negative) will only enhance the transportation experience, making the reader engage even deeper into the story world. However, such theory has not been examined by advertising researchers. It remains unknown if emotional valence has an effect (and to what extent) on the transportational process in advertising. Future studies should empirically explore the potential of both positive and negative emotions in transportational advertising. Second, the studies did not investigate antecedents to the transportational process, such as transportability (Dal Cin, Zanna, & Fong, 2004), ease of imagery generation (Petrova & Cialdini, 2005) and need for cognition (Green et al., 2008). To broaden our understanding of this unique pathway to persuasion, more empirical work is needed to explore the effects of such antecedents, additional narrative frameworks, and new contexts of narrative transportation in advertising. A 94 recent example of such study is a qualitative investigation on narrative transportation’s effects in luxury advertising (see Kim, Lloyd, & Cervellon, 2016). Third, this dissertation primarily used Amazon Mechanical Turk subjects. Although there are many scholars citing this platform as a reliable way to gather data (see Paolacci, Chandler, & Ipeirotis, 2010), there are concerns about its representativity (e.g, Berinsky, Huber, & Lenz, 2012), sampling method, and data quality (see the summary from Paolacci & Chandler, 2014). Future studies should take special care when choosing sampling platforms. Lastly, as mentioned in the study-specific discussion sections of this dissertation, the studies experienced some other limitations, such as ad format (only display and video), ad design (space and length), relatively small sample sizes, limited number of brands and product categories, and limited measures of ad outcomes. Conclusions This dissertation is framed within a narrative transportation theory and proposes a transportational advertising strategy along with two traditional advertising strategies— informational and transformational—that nest within an overlapping model of advertising strategies. Findings of this dissertation show that well-executed transportational ads are rated more positively in ad attitude, brand attitude and purchase intention, followed by transformational ads and, finally, informational ads. Furthermore, based on the overlapping model of advertising strategies, ads without a focused strategy are found to be less effective compared to ads that focus on one single advertising strategy. This dissertation also finds empirical support for a transportation mediation framework with critical thoughts and affective responses as mediators in real-world video ads. Finally, the findings also suggest that the more robust the perceived transportational strategy, the more effective the ad outcomes. 95 APPENDICES 96 Appendix A – Instrument for Study 1 Introduction Thank you for taking part in our study. In this study, we would like you to view two ads and evaluate each of them. [Each participant was exposed to two ads in pre-randomized order as outlined in Table 1. The two ads shared similar questions as listed below. See Figure 5 for the study 1 stimuli.] Ad Evaluations Please rate the ad above: 1 (1) 2 (2) 3 (3) 4 (4) 5 (5) 6 (6) 7 (7) Negative Bad Unfavorable Unpleasant ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ Positive Good Favorable Pleasant Please indicate your attitude toward the brand "PUE": 1 (1) 2 (2) 3 (3) 4 (4) 5 (5) 6 (6) 7 (7) Negative Bad Unfavorable Unpleasant ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ Positive Good Favorable Pleasant Based on the above ad, please rate each of the following statements using the scale from 1 = Very Unlikely to 7 = Very Likely. Unlikely (2) (3) (4) (5) (6) Very Would you like to try the product? (1) With price being irrelevant, could you imagine yourself buying this product? (2) With price being irrelevant, will this product be one of your most likely choices when you buy running shoes? (3) (1) ○ ○ ○ 97 ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ Very Likely (7) ○ ○ ○ Based on the ad from the previous page, please indicate your disagreement/agreement with the following statements using the scale from 1 = Not at all to 7 = Very much. I could picture myself in the scene of the events described in the ad. (1) I was mentally involved in the ad while reading it. (2) I wanted to learn how the ad ended. (3) The ad affected me emotionally. (4) While reading the ad I had a vivid image of the primary character. (5) Not At All (1) (2) (3) (4) (5) (6) ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ Very Much (7) ○ ○ ○ ○ ○ Please indicate your disagreement/agreement with the following statements about the ad from the previous page. Strongly Disagree (1) (2) (3) (4) (5) (6) Agree Strongly (7) ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ I learned something from this ad that I didn’t know before about this brand. (1) I would like to have an experience like the one shown in the ad. (2) The ad did not seem to be addressing me directly. (3) There is nothing special about this ad that makes it different from the others. (4) While I read this ad, I thought about how this brand might be useful to me. (5) This ad did not teach me what to look for when buying this product. (6) ○ ○ ○ ○ ○ ○ 98 This ad was meaningful to me. (7) This ad was very uninformative. (8) This brand fits my lifestyle very well. (9) I could really relate to the ad. (10) ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ Using this brand makes me feel good about myself. (11) ○ ○ ○ ○ ○ ○ ○ The company could provide evidence to support the claims made in the ad. (12) ○ ○ ○ ○ ○ ○ ○ It’s hard to give a specific reason, but somehow this brand is not really for me. (13) ○ ○ ○ ○ ○ ○ ○ This ad did not really hold my attention. (14) ○ ○ ○ ○ ○ ○ ○ If I could change my lifestyle, I would make it less like the people who use this brand. (15) ○ ○ ○ ○ ○ ○ ○ I felt as though I were right there in the ad, experiencing the same thing. (16) ○ ○ ○ ○ ○ ○ ○ I can now accurately compare this brand with other competing brands on matters that are important to me. (17) ○ ○ ○ ○ ○ ○ ○ This ad did not remind me of any experiences or feelings I’ve had in my own life. (18) ○ ○ ○ ○ ○ ○ ○ I have more confidence in my ability to judge the merits of buying this brand now that I have seen this ad. (19) ○ ○ ○ ○ ○ ○ ○ It’s hard to put into words, but this ad leaves me with a good feeling about using this brand. (20) ○ ○ ○ ○ ○ ○ ○ Control Questions Next, we have a few questions about your general attitudes toward running shoes and headphones. What is your general attitude toward running shoes? 1 (1) 2 (2) 3 (3) 4 (4) 5 (5) 6 (6) 7 (7) Negative Bad Unfavorable ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ Positive Good Favorable 99 To me, running shoes are... 1 (1) 2 (2) 3 (3) 4 (4) 5 (5) 6 (6) 7 (7) Important Boring Relevant Exciting Means nothing Appealing Fascinating Worthless Involving Not Needed ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ How often do you use running shoes? ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ Unimportant Interesting Irrelevant Unexciting ○ Means a lot to me ○ ○ ○ ○ ○ Unappealing Mundane Valuable Uninvolving Needed o Daily (1) o Several times a week (2) o Once a week (4) o Once every two weeks (5) o Once every three weeks (6) o Once a month (7) o Other (please specify) (8) ________________________________________________ If you are going to purchase a pair of running shoes, how much are you willing to pay? 0 100 200 300 400 500 in U.S. dollars (1) What is your general attitude toward headphones? 1 (1) 2 (2) 3 (3) 4 (4) 5 (5) 6 (6) 7 (7) Negative Bad Unfavorable ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ Positive Good Favorable 100 To me, headphones are... 1 (1) 2 (2) 3 (3) 4 (4) 5 (5) 6 (6) 7 (7) Important Boring Relevant Exciting Means nothing Appealing Fascinating Worthless Involving Not Needed ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ How often do you use headphones? ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ Unimportant Interesting Irrelevant Unexciting ○ Means a lot to me ○ ○ ○ ○ ○ Unappealing Mundane Valuable Uninvolving Needed o Daily (1) o Several times a week (2) o Once a week (4) o Once every two weeks (5) o Once every three weeks (6) o Once a month (7) o Other (please specify) (8) ________________________________________________ If you are going to purchase a set of headphones, how much are you willing to pay? 0 100 200 300 400 500 in U.S. dollars (1) Demographics This is the last part of the study. We have a few more questions related to your demographic characteristics. 101 What is your gender? o Female (1) o Male (2) o Transgender (3) o Prefer not to say (4) What year were you born? [Drop-down menu] ▼ 2005 (1) ... 1950 (56) What is your marital status? o Married (1) o Widowed (2) o Divorced (3) o Separated (4) o Never Married (5) Excluding kindergarten, how many years of formal education do you have? Please enter only numerical values in the space below. ________________________________________________________________ Are you...? o Hispanic (1) o Non-Hispanic (2) Which of the following best describes your ethnic background? o American Indian or Native (1) o White/Caucasian (2) o Asian (3) o Latino (4) o Black/African American (5) o Native Hawaiian or other Pacific Islander (6) o Two or more Ethnicities/Races (7) o Other, please specify: (8) ________________________________________________ What is your total annual family income? o Less than $10,000 (1) o $10,000 - $49,999 (2) o $50,000 - $99,999 (3) o $100,000 - $149,999 (4) o $150,000 or more (5) 102 Validation Code Thank you for participating. Your validation code is: ${e://Field/Random%20ID} Copy this code and paste it into MTurk to receive payment for participating. When you have copied this code, please click the next button to submit your survey. Thank you! 103 Appendix B – Instrument for Study 2 Introduction Thank you for taking part in our study. In this study, we would like you to view a few ads and evaluate each of them. [Each participant was exposed to four ads in randomized order. The ads shared the same questions as listed below. See Figure 8 for the study 2 stimuli.] Ad Evaluations Please rate the ad above: 1 (1) 2 (2) 3 (3) 4 (4) 5 (5) 6 (6) 7 (7) Negative Bad Unfavorable Unpleasant ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ Positive Good Favorable Pleasant Please indicate your attitude toward Nike after seeing the ad: 1 (1) 2 (2) 3 (3) 4 (4) 5 (5) 6 (6) 7 (7) Negative Bad Unfavorable Unpleasant ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ Positive Good Favorable Pleasant Based on the above ad, please rate each of the following statements using the scale from 1 = Very Unlikely to 7 = Very Likely. Unlikely (2) (3) (4) (5) (6) Very Would you like to try the product? (1) With price being irrelevant, could you imagine yourself buying this product? (2) With price being irrelevant, will this product be one of your most likely choices when you buy running shoes? (3) (1) ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ Very Likely (7) ○ ○ ○ 104 Based on the ad from the previous page, please indicate your disagreement/agreement with the following statements using the scale from 1 = Not at all to 7 = Very much. I could picture myself in the scene of the events described in the ad. (1) I was mentally involved in the ad while reading it. (2) I wanted to learn how the ad ended. (3) The ad affected me emotionally. (4) While reading the ad I had a vivid image of the primary character. (5) Not At All (1) (2) (3) (4) (5) (6) ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ Very Much (7) ○ ○ ○ ○ ○ Please indicate your disagreement/agreement with the following statements about the ad from the previous page. Strongly Disagree (1) (2) (3) (4) (5) (6) Agree Strongly (7) ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ I learned something from this ad that I didn’t know before about this brand. (1) I would like to have an experience like the one shown in the ad. (2) The ad did not seem to be addressing me directly. (3) There is nothing special about this ad that makes it different from the others. (4) While I read this ad, I thought about how this brand might be useful to me. (5) This ad did not teach me what to look for when buying this product. (6) ○ ○ ○ ○ ○ ○ 105 This ad was meaningful to me. (7) This ad was very uninformative. (8) This brand fits my lifestyle very well. (9) I could really relate to the ad. (10) ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ Using this brand makes me feel good about myself. (11) ○ ○ ○ ○ ○ ○ ○ The company could provide evidence to support the claims made in the ad. (12) ○ ○ ○ ○ ○ ○ ○ It’s hard to give a specific reason, but somehow this brand is not really for me. (13) ○ ○ ○ ○ ○ ○ ○ This ad did not really hold my attention. (14) ○ ○ ○ ○ ○ ○ ○ If I could change my lifestyle, I would make it less like the people who use this brand. (15) ○ ○ ○ ○ ○ ○ ○ I felt as though I were right there in the ad, experiencing the same thing. (16) ○ ○ ○ ○ ○ ○ ○ I can now accurately compare this brand with other competing brands on matters that are important to me. (17) ○ ○ ○ ○ ○ ○ ○ This ad did not remind me of any experiences or feelings I’ve had in my own life. (18) ○ ○ ○ ○ ○ ○ ○ I have more confidence in my ability to judge the merits of buying this brand now that I have seen this ad. (19) ○ ○ ○ ○ ○ ○ ○ It’s hard to put into words, but this ad leaves me with a good feeling about using this brand. (20) ○ ○ ○ ○ ○ ○ ○ Control Questions Next, we have a few questions about your general attitudes toward Nike and Nike running shoes. How familiar are you with Nike? 1 (1) 2 (2) 3 (3) 4 (4) 5 (5) 6 (6) 7 (7) Not at all familiar ○ ○ ○ ○ ○ ○ ○ Extremely familiar 106 What is your general attitude toward Nike running shoes? 1 (1) 2 (2) 3 (3) 4 (4) 5 (5) 6 (6) 7 (7) Negative Bad Unfavorable ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ Positive Good Favorable Have you purchased any Nike running shoes during the past 12 months? If so, how many pairs? o Yes (1) o How many? (2) ________________________________________________ o No (3) Demographics This is the last part of the study. We have a few more questions related to your demographic characteristics. What is your gender? o Female (1) o Male (2) o Other, not listed (3) What year were you born? [Drop-down menu] ▼ 2005 (1) ... 1950 (56) What is your marital status? o Married (1) o Widowed (2) o Divorced (3) o Separated (4) o Never Married (5) Please indicate your educational background. o Less than high school o High school graduate o Some college, no degree o Associate's degree, occupational o Associate's degree, academic o Bachelor's degree o Master's degree o Professional degree o Doctorate degree 107 Are you...? o Hispanic (1) o Non-Hispanic (2) Which of the following best describes your ethnic background? o American Indian or Native (1) o White/Caucasian (2) o Asian (3) o Latino (4) o Black/African American (5) o Native Hawaiian or other Pacific Islander (6) o Two or more Ethnicities/Races (7) o Other, please specify: (8) ________________________________________________ What is your total annual family income? o Less than $10,000 (1) o $10,000 - $49,999 (2) o $50,000 - $99,999 (3) o $100,000 - $149,999 (4) o $150,000 or more (5) Validation Code Thank you for participating. Your validation code is: ${e://Field/Random%20ID} Copy this code and paste it into MTurk to receive payment for participating. When you have copied this code, please click the next button to submit your survey. Thank you! 108 Appendix C – Instrument for Study 3 Introduction Thank you for taking part in our study. In this study, we would like you to view three video ads and evaluate each of them. [Each participant was exposed to three video ads in randomized order. The ads shared similar questions as listed below. See Figure 12 for screenshots of study 3 stimuli.] Ad Evaluations Please write down what you were thinking while viewing the ad: ________________________________________________________________ How well do the following words describe how you felt while viewing the ad? Not Well (1) (2) (3) (4) (5) (6) Very Well (7) Active (1) Alive (2) Cheerful (3) Delighted (4) Energetic (5) Happy (6) Pleased (7) Stimulated (8) ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ Please rate the ad you just saw: ○ ○ ○ ○ ○ ○ ○ ○ 1 (1) 2 (2) 3 (3) 4 (4) 5 (5) 6 (6) 7 (7) Negative Bad Unfavorable Unpleasant ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ Positive Good Favorable Pleasant 109 Please indicate your attitude toward P&G after seeing the ad: 1 (1) 2 (2) 3 (3) 4 (4) 5 (5) 6 (6) 7 (7) Negative Bad Unfavorable Unpleasant ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ Positive Good Favorable Pleasant Based on the ad you just saw, please rate each of the following statements using the scale from 1 = Very Unlikely to 7 = Very Likely. Unlikely (2) (3) (4) (5) (6) Very Would you like to try the product? (1) With price being irrelevant, could you imagine yourself buying this product? (2) With price being irrelevant, will this product be one of your most likely choices when you buy running shoes? (3) (1) ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ Very Likely (7) ○ ○ ○ Based on the ad from the previous page, please indicate your disagreement/agreement with the following statements using the scale from 1 = Not at all to 7 = Very much. I could picture myself in the scene of the events described in the ad. (1) I was mentally involved in the ad while reading it. (2) I wanted to learn how the ad ended. (3) The ad affected me emotionally. (4) While reading the ad I had a vivid image of the primary character. (5) Not At All (1) (2) (3) (4) (5) (6) ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ Very Much (7) ○ ○ ○ ○ ○ 110 Control Questions Next, we have a few questions about your general attitudes toward the brands you just saw. Have you seen the [P&G] ad before? If so, how many times? o Yes (1) o How many? (2) ________________________________________________ o No (3) How familiar are you with [P&G]? 1 (1) 2 (2) 3 (3) 4 (4) 5 (5) 6 (6) 7 (7) Not at all familiar ○ ○ ○ ○ ○ ○ ○ Extremely familiar What is your general attitude toward [P&G products]? 1 (1) 2 (2) 3 (3) 4 (4) 5 (5) 6 (6) 7 (7) Negative Bad Unfavorable ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ ○ Positive Good Favorable Have you purchased any [P&G products] during the past 12 months? If so, how many pairs? o Yes (1) o How many? (2) ________________________________________________ o No (3) Demographics This is the last part of the study. We have a few more questions related to your demographic characteristics. What is your gender? o Female (1) o Male (2) o Other, not listed (3) What year were you born? [Drop-down menu] ▼ 2005 (1) ... 1950 (56) 111 What is your marital status? o Married (1) o Widowed (2) o Divorced (3) o Separated (4) o Never Married (5) Please indicate your educational background. o Less than high school o High school graduate o Some college, no degree o Associate's degree, occupational o Associate's degree, academic o Bachelor's degree o Master's degree o Professional degree o Doctorate degree Are you...? o Hispanic (1) o Non-Hispanic (2) Which of the following best describes your ethnic background? o American Indian or Native (1) o White/Caucasian (2) o Asian (3) o Latino (4) o Black/African American (5) o Native Hawaiian or other Pacific Islander (6) o Two or more Ethnicities/Races (7) o Other, please specify: (8) ________________________________________________ What is your total annual family income? o Less than $10,000 (1) o $10,000 - $49,999 (2) o $50,000 - $99,999 (3) o $100,000 - $149,999 (4) o $150,000 or more (5) 112 Validation Code Thank you for participating. 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