Before and After Avatar Exposure

Abstract

Loker et. al. initiated a call for research investigating how third dimension (3-D) affects one’s perceptions of their own body by raising the following questions: Will the ability to see ourselves in 3-D increase body acceptance of normal variations and counteract the popular media images of what constitutes a beautiful body? Or will 3-D views increase dissatisfaction with our real bodies? In response, the researcher guided by self-discrepancy theory, investigated the unique experience of viewing one’s body in 3-D on participants’ self-reported levels of body satisfaction, mood, and appearance management. Results indicated that viewing one’s avatar in 3-D magnifies individuals’ actual–ideal (AI) self-discrepancies regardless of gender. The researcher observed decreased body satisfaction and mood when the magnitude of participants’ AI discrepancies increased. Both men and women indicated wanting to engage in greater appearance management behaviors postavatar viewing compared to baseline reports. The theoretical and applied implications are discussed.

Keywords

body image body scanning appearance management self-discrepancy

In 2008, Loker, Ashdown, and Carnrite (2008) initiated a call for research investigating how the third dimension (3-D) affects one’s perceptions of their own body. Specifically, they raised the following questions: “Will the ability to see ourselves in three dimensions increase body acceptance of normal variations and counteract the popular media images of what constitutes a beautiful body? Or will three-dimensional views increase dissatisfaction with our real bodies?” (p. 175). In response to this call, the researcher, guided by self-discrepancy theory (SDT; Higgins, 1987), investigated the unique experience of viewing one’s body in 3-D on both men’s and women’s self-reported levels of body satisfaction, mood, and appearance management behaviors. The results from this study provide novel insights into the body image literature, designing with technology, and the implementation of 3-D scanning in clothing and textiles research. Before discussing SDT, the researcher begins by providing an overview of the body image literature and research on the self in the 3-D.

Body Image and the Self in the 3-D

Neurologist Paul Schilder (1935) first used the term “body image” in his book The Image and Appearance of the Human Body. He described the term body image as one’s perceptions toward his or her own self (Schilder, 1935). Since Schilder’s seminal work in 1935, many researchers from a variety of academic fields have examined body image from several theoretical and methodological approaches. For instance, body image has been studied by scholars in clothing and textiles (Jung, Lennon, & Rudd, 2001; Ridgway, Parsons, & Sohn, 2017), psychology (López-Guimerà, Levine, Sánchez-Carracedo, & Fauquet, 2010; Ridgway & Clayton, 2016), and mass communication (Clayton, Ridgway, & Hendrickse, 2017; Hendrickse, Arpan, Clayton, & Ridgway, 2017; Myers & Biocca, 1992). Although substantial and fruitful knowledge has been acquired through these researchers’ work, especially in terms of women’s mental and physical well-being, there has been little examination into how viewing one’s own self in 3-D via body-scanning technology influences men’s and women’s perceptions of their bodies, and how this interaction influences specific emotions and appearance management behaviors.

With advancements in technology, the development of 3-D body scanning has changed the way that people can view and interact with their bodies. Body scanning is a process that enables the body to be accurately assessed by measuring it using light displacement and image capture software (Grogan, Gill, Brownbridge, Warnock, & Armitage, 2016; Istook, 2000). This technology provides cloud point data (i.e., a set of 3-D data defined by X, Y, and Z coordinates) that can be used to accurately measure any area of the body. The body scan results in a 3-D avatar that is reflective of the person’s actual body size. Afforded by the ability to accurately measure the body, the most common use of the 3-D body scanner has been for research focused on assessing the fit and function of clothing (Shin & Baytar, 2014), body shape classification (Simmons, 2003), and digital garment design (Ridgway et al., 2017).

In one well-conducted study, Loker et al. (2008) investigated consumers’ comfort with body scanning technology and also assessed participants’ reactions to the intended uses (applications) of body scanning. Loker and colleagues found that a majority of their participants (88% of women, N = 203, aged 35–54) felt comfortable to very comfortable with the body scanning process. Additionally, the researchers asked the participants whether they would be comfortable showing their body scan to family or friends. On average, the researchers found that participants were substantially less likely to share their body scans with others, presumably because they were uncomfortable with the shape of their body via the 3-D avatar. This finding, therefore, represents a direct call for investigating whether 3-D body scanning has negative psychological effects on one’s perception of his or her body and, in general, whether 3-D scanning influences overall well-being. Moreover, it should be noted that in the same study, 94% of men were willing to be scanned in future studies, suggesting positivity toward body scanning technology. Although interesting findings were the result of Loker et al.’s (2008) study, one limitation (potentially outside the scope of their study) was that they did not assess whether viewing one’s body scan influenced perceptions about their own body. Thus, one aim of the current study is to not only body scan men and women but also assess their perceptions about their body scan.

In a more recent body scanning study, Grogan, Gill, Brownbridge, Warnock, and Armitage (2016) examined the long-term impacts of body scanning on women’s body satisfaction by conducting a mixed method longitudinal study with a sample of 91 women. Grogan et al. (2016) found that 34% of the women who had been body scanned later (one to three months after the scan) reported that they “felt more negative about their bodies since being scanned” (Grogan et al., 2016, p. 69). However, a limitation of the study recognized by Grogan et al. (2016) was that there was no baseline or pretest data for comparison of the postbody scanning results. Hence, Grogan et al.’s (2016) experimental design was a posttest only experiment, which does not provide clear evidence to whether the results were due to the body scanning process or the dispositional nature of the participants. Thus, a methodological aim of this study was to collect baseline data prior to the body scanning process for comparison to the postbody–scan data; thus, the current study was a pre–posttest experimental design. To examine how viewing one’s self in 3-D influences perception change (from baseline to postavatar viewing), this study employs SDT —a theory with much predictive power for understanding the psychological effects often found in body image research.

Body Scanning and SDT

According to Higgins’s SDT(1987), there are “three basic domains of the self,” which include the actual self, the ideal self, and the ought self (pp. 320–321). The actual self represents the attributes one believes he or she actually possesses, the ideal self represents the attributes one desires to possess, and the ought self represents the attributes one believes she or he should possess (Higgins, 1987). A self-discrepancy is produced when there is a conflict between these domains, resulting in negative emotions and vulnerabilities (Barnett, Moore, & Harp, 2017; Higgins, 1987). Specifically, Barnett, Moore, and Harp (2017) found that sadness, downheartedness, and feeling more alone were feelings experienced by those who believed their actual selves were further from their ideal selves. Such emotions were found to be stronger in those who had a greater magnitude of self-discrepancy (Higgins, 1987). In the body scanning and avatar literature, researchers have shown that individuals report being “shocked” by their body scans (Grogan et al., 2016). One explanation for this outcome is that certain sociocultural norms have led to the pressure for individuals to meet certain body type standards, such as the pressure for women to be toned and slender (Frith, 2012; Tiggemann & Andrew, 2012). When confronted by an avatar that does not meet the participant’s standard or their mental representation of their actual body, negative emotions are likely to arise as the discrepancy between their ideal and actual self increases. Thus, a general and important prediction made in this study is that when participants are confronted with their avatar, a 3-D figure that accurately represents their actual body size, participants’ actual–ideal (AI) discrepancies will increase in magnitude compared to reports prior to their body scan (i.e., baseline).

Hypothesis 1a: Participants’ AI self-discrepancies will increase in magnitude postavatar viewing compared to baseline reports.

Researchers have indicated that self-discrepancies are often influenced by gender (Muth & Cash, 1997). Muth and Cash (1997) demonstrated that women have greater AI discrepancies than men (Cohen’s d = .52), as well as several other discrepancies pertaining to body proportions (see Muth & Cash, 1997, for a review). Researchers have also shown that women exhibit greater cognitive differentiation than men in their awareness and perceptions of their bodies (Brown, Cash, & Mikulka, 1990; Fisher, 1986). Therefore, the following prediction is made:

Hypothesis 1b: Women’s AI self-discrepancies will increase significantly more in magnitude than men postavatar viewing compared to baseline reports.

SDT posits that when individuals detect a discrepancy between their actual and ideal selves, or when such an AI discrepancy increases in magnitude, individuals experience dejection-related emotions, such as decreased satisfaction with one’s self (Higgins, 1987). Researchers have shown that when women, in particular, are confronted with their ideal body type, body satisfaction reports significantly decrease compared to when they are confronted with body types that are similar to their own (Clayton et al., 2017). In fact, Halliwell and Dittmar (2006) indicated as a result of a recent meta-analysis that discrepancies between a woman’s actual and ideal self has been identified as a key predictor to body dissatisfaction and decreased mood. Thus, SDT would predict that participants’ body satisfaction and mood ratings should significantly decrease if their AI discrepancies increase as a function of being confronted with the 3-D avatars that represent their actual body sizes. Therefore,

Hypothesis 2a: Participants’ body satisfaction ratings will be lower postavatar viewing compared to baseline reports.

Hypothesis 3a: Participants’ moods will be lower postavatar viewing compared to baseline reports.

Researchers have shown that women relative to men are more likely to experience-specific dejection-related emotions associated with AI self-discrepancies. Specifically, when an AI self-discrepancy emerges, women, compared to men, report significantly less body satisfaction and mood (Feingold & Mazzella, 1998; Halliwell & Dittmar, 2006). One explanation for this finding, as posited by Muth and Cash (1997), is that women are simply more psychologically invested in their appearances. Based on the literature and since the researcher predicted that women will experience greater AI discrepancies than men (see Hypothesis 1b), it would stand to reason based on SDT that women will also report significantly lower body satisfaction and mood than men postavatar viewing compared to baseline reports.

Hypothesis 2b: Body satisfaction and gender will interact such that body satisfaction will be lower for women than men postavatar viewing compared to baseline reports.

Hypothesis 3b: Mood and gender will interact such that mood will be lower for women than men postavatar viewing compared to baseline reports.

Researchers have shown that individuals attempt to alleviate AI discrepancies by managing their appearances. Kaiser (1996) described appearance management as encompassing “all attention, decisions, and acts related to personal appearance” (p. 5). Individuals take part in appearance management behaviors as a means of self-expression and enhancing personal identity (T. W. Johnson, Francis, & Burns, 2007; Rudd, 1997). Such behaviors include the use of cosmetics, personal grooming procedures, apparel choices, body modification, and any physical activity that could potentially enhance one’s appearance (Lee & Johnson, 2009; Lennon & Rudd, 1994; Rudd, 1997). Appearance management behaviors are typically classified into categories of risky and nonrisky behaviors (K. K. P. Johnson, Kim, Lee, & Kim, 2014). Risky behaviors stem from negative emotional consequences, which may develop as a result of an emerging self-discrepancy and poor body image (Bessenoff & Snow, 2006; McKinley, 1998; Schilder, 1935). While it is recognized that adverse consequences do exist for both males and females, it is primarily observed from the perspective of female populations (Neumark-Sztainer & Eisenberg, 2014). As demonstrated by Weinberger, Kersting, Riedel-Heller, and Luck-Sikorski (2016), in a recent meta-analysis, perhaps the reason for the overwhelming focus solely on women is that women tend to have greater body dissatisfaction compared to men (Weinberger, Kersting, Riedel-Heller, & Luck-Sikorski, 2016). However, focusing primarily on women does not take into account how men differ in regard to appearance management behaviors as a result of body dissatisfaction. Researchers have indicated that although disordered eating, particularly bulimic behaviors, have been found to be present in male populations, growth hormone derivatives, supplements, and anabolic steroids were found to be more common when there was higher concern over muscularity and thinness (Neumark-Sztainer & Eisenberg, 2014). This drive to manage one’s appearance is induced by the need to reduce the discrepancy between what one perceives their actual body to be and the ideal body. Based on this literature, the researcher predicts that participants will report greater appearance management behaviors in order to reduce their AI self-discrepancy postavatar viewing compared to baseline reports. However, there is little to no evidence pertaining to gender and appearance management behaviors as related to AI discrepancy reduction. Therefore, the following hypothesis and research question are posed:

Hypothesis 4: Participants will report greater likelihood to engage in more appearance management behaviors postavatar viewing compared to baseline reports.

Research Question 1: Will likelihood to engage in appearance management behaviors significantly increase for women as compared to men when comparing postavatar viewing with baseline reports?

Method

Power Analysis and Participants

In order to reduce the probability of obtaining a false negative (i.e., Type II error), a priori power analysis was conducted using G*Power software (Faul, Erdfelder, Lang, & Buchner, 2007). A mixed analysis of variance (ANOVA): Fixed effects, main effects, and interactions statistical test with a moderate effect size of f = 0.30, α = 0.05, power = 0.8, and two groups (men and women) revealed that 90 participants were needed to be sufficiently powered. As a result, men and women (N = 101) were recruited from the local community and university. Flyers were placed on bulletin boards on campus, in local restaurants, and in shops. The participants’ ages ranged from 18 to 35 years old (M = 19.87, SD = 2.02). Most participants (66%) were Caucasian, 22% Hispanic, 6% African American, and 6% other. A majority of participants (67%) were female.

Procedure

Participants completed the experiment one at a time in a body scanning laboratory at a large southeastern university. Participants were seated at a computer terminal in the research lab to complete the baseline questionnaire consisting of demographic, body satisfaction (Body Image State Scale [BISS]; Cash, Fleming, Alindogan, Steadman, & Whitehead, 2002), mood (Brief Mood Introspection Scale [BMIS]; Mayer & Gaschke, 1988), and appearance management behavior (AMB; Lennon & Rudd, 1994). Additionally, participants were asked to identify their actual and ideal body sizes using the Gardner Body-Image Assessment tool, based on known Body Dimensions (BIAS-BD; Gardner, Jappe, & Gardner, 2009). Upon completion of the online questionnaire, participants were body scanned one a time in a TC² NX16 scanner. To ensure the privacy of participants, they were concealed behind two layers of curtains. Participants were body scanned in their own undergarments and were instructed to wear light-colored undergarments to ensure the accuracy of the scan. Additionally, participants were asked to wear white stocking caps over their hair so that the avatar created from the scan would have a complete head.

Once the scan was completed, participants redressed and were reseated at the computer terminal to view and interact with their customized avatar created from their body scan. Participants had 3 min to view and interact with their avatar. Afterward, the participants were prompted by the researcher to take the postbody scan online questionnaire. Their avatar was not available for viewing during the postquestionnaire. The postquestionnaire contained the same items from the baseline questionnaire (not including demographic questions) and contained additional items, which measured the perceived realism of the avatar as an induction check of the digital avatar viewing experience. The self-report items were randomized within subjects, such that no participant viewed the questions in the same order. This study was approved by the university’s institutional review board.

Independent Variable

Absence and presence of one’s digital avatar served as the independent variable in this study. Participants were not exposed to their avatar at baseline but were exposed to their avatar postavatar scan. The avatar consisted of the cloud point data image of the participant’s body scan. As advised by Grogan et al. (2016), the scanner was set to the TC² default setting, which yields an image of the cloud point data. The cloud point data still have the discernible features of the participants, yet is not photographic. The cloud point data avatar was used for ethical reasons and in order to reduce potential embarrassment of the participants since they were scanned in their undergarments (Grogan et al., 2016). An example of the avatar scan can be seen in Figure 1.

Figure 1.

Cloud point data avatar. This figure illustrates the type of avatar that was created from participants’ body scan.

Induction Check for Avatar Similarity

Participants were given the avatar similarity scale (Suh, Kim, & Suh, 2011) postavatar exposure in order to ensure that participants perceived their avatar to be a true representation of the actual bodies. An example item included: “I think that my body and this avatar resemble each other in appearance.” Responses to each item were based on a 7-point Likert-type scale ranging from 1 = strongly disagree to 7 = strongly agree. On average, participants agreed that their avatar represented their actual appearance (M = 5.53, SD = 1.00, range = 2.0–7.0). It should be noted that none of the participants indicated that they strongly disagreed that their avatar was representative of their body. In the current study, responses resulted in a Cronbach’s α of .788.

Dependent Variables

AI discrepancy

The Gardner BIAS-BD (Gardner et al., 2009) was used to measure participants’ AI discrepancies pre- and postexposure to their avatars. The figure drawing scale consists of 17 male and 17 female contour line drawings that use known anthropometric body dimensions. The first figure in the scale represents a body weight that is 60% below the average and the last figure represents a body weight that is 140% above the average. The difference between each figure in the scale is a change of 5% (±) body weight (Gardner et al., 2009). Participants were asked to select which figure on the scale most closely represented their actual (Item 1) and ideal (Item 2) body size. The difference between participants’ actual and ideal ratings was considered their baseline AI discrepancy (pre-AI discrepancy = pre-actual rating − pre-ideal rating). The difference between participants’ actual and ideal ratings postavatar viewing was considered their postavatar AI discrepancy (post-AI discrepancy = postactual rating − postideal rating).

Body image satisfaction

The BISS (Cash et al., 2002; α = .87) was used to measure participants’ stated body satisfaction pre- and postexposure to their avatars. The BISS contains 6-point Likert-type scale items measuring current satisfaction or dissatisfaction with one’s overall physical appearance, body shape and size, weight, feelings of physical attractiveness, current appearance, and current evaluation of one’s appearance relative to how one usually feels. Responses were based on a 9-point Likert-type scale ranging from 1 = extremely dissatisfied to 9 = extremely satisfied. In the current study, responses resulted in a Cronbach’s α of .939.

Mood

The BMIS (Mayer & Gaschke, 1988; α = .83) was used to measure participants’ moods pre- and postexposure to their avatars. The BMIS contains 16 mood adjectives: lively, happy, sad, tired, caring, content, gloomy, jittery, drowsy, grouchy, peppy, nervous, calm, loving, fed up, and active. Responses to each item are based on a 4-point Likert-type scale ranging from 1 = definitely do not feel to 4 = definitely feel. In the current study, responses resulted in a Cronbach’s α of .883.

Body sculpting

The body sculpting subscale of the AMB Scale (Lennon & Rudd, 1994) was used to assess the participants’ likelihood to manage their appearances pre- and postexposure to their avatars. The subscale consisted of 3 items asking participants their likelihood to diet, exercise, and fast, on a response scale anchored by 1 = very unlikely to 5 = very likely. In the current study, responses resulted in a Cronbach’s α of .790.

Results

Hypothesis 1a predicted that participants’ AI self-discrepancy, which was computed as the difference between participants’ actual and ideal ratings (AI discrepancy = actual rating − ideal rating) at baseline and postavatar viewing, would increase in magnitude from baseline to postavatar viewing. Participants’ ideal-self rating at baseline was M _ideal = 4.94, SD = 2.75, whereas their actual-self rating at baseline was M _actual = 6.18, SD = 3.62. Participants’ ideal-self rating postavatar viewing was M _ideal = 4.82, SD = 2.72, whereas their actual-self rating postavatar viewing was M _actual = 7.07, SD = 4.13. Difference in AI magnitude from baseline-to-postavatar viewing was submitted to a mixed repeated measures ANOVA with gender as the between-subjects factor. Data analysis revealed a significant main effect on self-discrepancy, such that participants’ AI discrepancy at baseline was significantly smaller (M = 1.24, SD = 3.27) compared to postavatar viewing (M = 2.25, SD = 4.20); F(1, 99) = 10.49, p = .002, $η_{p}^{2}$ = .09. Hypothesis 1a was supported. Hypothesis 1b predicted an interaction between self-discrepancy and gender, such that self-discrepancy would be greater for women than men postavatar viewing. However, the mixed ANOVA did not reveal a significant interaction between self-discrepancy and gender, F(1, 99) = 2.37, p = .126, though women had a larger increase in AI discrepancy postavatar viewing (M = 2.56, SD = .40) than men (M = .045, SD = .58) compared to baseline reports. Hypothesis 1b was not supported.

Hypothesis 2a predicted that body satisfaction ratings would be lower postavatar viewing compared to baseline. Results from a mixed ANOVA revealed a significant main effect on body satisfaction, such that self-reported body satisfaction was lower postavatar viewing (M = 5.11, SD = 1.98) compared to baseline (M = 5.58, SD = 1.53), F(1, 99) = 10.02, p = .002, $η_{p}^{2}$ = .09. Hypothesis 2a was supported. Hypothesis 2b predicted an interaction between body satisfaction and gender, such that body satisfaction would be lower for women than men postavatar viewing. Results from the mixed ANOVA also revealed a significant interaction between body satisfaction and gender, F(1, 99) = 6.61, p = .01, $η_{p}^{2}$ = .06. The parameter estimates revealed no significant differences of body satisfaction ratings between men and women at baseline, t(99) = −1.35, p = .179; however, body satisfaction ratings between men and women were significantly different postavatar viewing, t(99) = −2.50, p = .014, such that women reported significantly lower body satisfaction than men after viewing their avatar. Hypothesis 2b was supported (see Figure 2a).

Figure 2.

Interactions for self-report outcomes and gender. (a) Body satisfaction, (b) mood, and (c) appearance management behaviors.

Hypothesis 3a predicted that mood would be lower postavatar viewing compared to baseline. Results from a mixed ANOVA revealed a significant difference in mood, such that self-reported mood was lower postavatar viewing (M = 2.84, SD = .50) compared to baseline (M = 2.96, SD = .41), F(1, 99) = 6.66, p = .011, $η_{p}^{2}$ = .06. Hypothesis 3a was supported. Hypothesis 3b predicted an interaction between mood and gender, such that mood would be lower for women than men postavatar viewing. Results from the mixed ANOVA also revealed a significant interaction between mood and gender, F(1, 99) = 8.72, p = .004, $η_{p}^{2}$ = .08. The parameter estimates revealed no significant differences of mood ratings between men and women at baseline, t(99) = .093, p = .926; however, mood ratings between men and women were significantly different postavatar viewing, t(99)= −2.19, p = .03, such that women reported significantly lower mood than men after viewing their avatar. Hypothesis 3b was supported. See Figure 2b.

Hypothesis 4 predicted that participants would report greater likelihood to engage in AMBs postavatar viewing compared to baseline. Results from a mixed ANOVA revealed a significant main effect on likelihood to engage in AMBs, such that self-reported likelihood to engage in AMBs was greater postavatar viewing (M = 3.69, SD = .95) compared to baseline (M = 2.86, SD = .80), F(1, 99) = 86.02, p < .001, $η_{p}^{2}$ = .465. Hypothesis 4 was supported. Research Question 1 asked whether the likelihood of engaging in AMBs significantly increased for women compared to men when comparing postavatar viewing with baseline reports. Results from the mixed ANOVA revealed a significant interaction between AMBs and gender, F(1, 99) = 7.94, p = .006, $η_{p}^{2}$ = .07. The parameter estimates revealed no significant differences of AMB ratings between men (M = 2.90, SD = .76) and women (M = 2.84, SD = .83) at baseline, t(99) = −.385, p = .70; however, likelihood to engage in appearance management ratings between men and women was significantly different postavatar viewing, t(99)= −3.27, p = .001, such that women (M = 3.49, SD = .91) reported significantly fewer AMBs than men (M = 4.12, SD = .89) after viewing their avatars (see Figure 2c).

Discussion

This research was conducted as a response to the call initiated by Loker et al. (2008) to investigate the impact that 3-D body scanning has on the perception of and satisfaction with the body. Guided by SDT (Higgins, 1987), the researcher demonstrated that viewing one’s avatar in 3-D magnifies individuals’ AI self-discrepancies. Moreover, and as predicted by SDT, the researcher observed decreased body satisfaction and mood when the magnitude of participants’ AI discrepancies increased. That is, when participants viewed their 3-D avatars, their AI discrepancies increased and their overall body satisfaction and mood decreased compared to baseline reports.

Although there was not a gender difference pertaining to AI discrepancies postavatar viewing compared to baseline, the researcher observed gender differences on body satisfaction and mood. Specifically, women reported less body satisfaction and dampened mood relative to men postavatar viewing compared to baseline reports. Thus, although AI discrepancies postavatar viewing were not significantly different between genders (though women had greater AI change than men), women experienced significantly greater dejection-related emotions associated with AI discrepancies than men. One explanation for both of these findings as suggested by Muth and Cash (1997) is that women are more invested in their overall appearances and because Western society’s ideal is more discretely defined for women compared to men. Moreover, both men and women indicated wanting to engage in greater AMBs postavatar viewing compared to baseline reports. One obvious and plausible explanation for this finding is that participants wanted to reduce their increased AI discrepancy when confronted with their actual body via 3-D scan. However, and in contrast to the dejection-related emotions observed with women, the researcher observed that men were more likely to engage in AMBs, specifically those related to body sculpting, than women. This finding could be due in part to the fact that men’s ideal is to be more muscular as opposed to women’s ideal, which is to be thinner (Halliwell & Dittmar, 2006; Thompson, Heinberg, Altabe, & Tantleff-Dunn, 1999). This explanation might be especially true for this study since participants reported their responses only on the body sculpting subscale of the appearance management measure. Future researchers should investigate other aspects of AMBs.

Theoretical and Practical Implications

The purpose of this study was to answer Loker et al.’s (2008) question, “Will three-dimensional views increase dissatisfaction with our real bodies?” and to examine the role of gender and AI self-discrepancies in 3-D body image research. In short, the answer to Loker et al.’s (2008) call is simply, yes, 3-D views decrease body satisfaction and mood, especially for women, and increase likelihood to engage in AMBs, especially for men. Viewing one’s own body in 3-D is indeed a novel endeavor and creates new perspectives of one’s body while also providing an accurate depiction of one’s body size and shape that cannot be refuted. During interaction with one’s 3-D avatar, participants were able to view their actual bodies from all angles and perspectives. It is extremely rare for an individual to view their whole body directly from behind, from a bird’s eye view, and a worm’s eye view. Thus, it is feasible that participants may have never experienced their bodies from these (dis)advantage points, thus giving them entirely new perspectives on their actual selves. It is reasonable to assume that participants were able to gather new knowledge about their bodies, which resulted in a greater discrepancy between one’s actual and ideal self. Such a discrepancy, as predicted by Higgins’ (1987) SDT, would result in greater dejection-related emotions such as lower body satisfaction and decreased mood, as was found in this study. These observed effects provide new evidence for the body image and clothing and textiles literature, as this was the first study to demonstrate these findings using body scanning technology in a pre–posttest experimental design.

The implications of these findings are 2-fold. First, from a theoretical perspective, the researcher has provided in her study novel evidence for the body image and clothing literature by demonstrating that outcomes predicted by SDT are upheld in 3-D environments. In the future, researchers should continue examining the implications of 3-D environments in body image research, as it relates to individuals’ body image concerns. Second, this study provides several practical implications. A recent article in Huffington Post (Frankel, 2017) noted that body scanning continues to become more accessible to consumers and uses for body scanning technology have become more widespread. Indeed, body scanning is now also utilized in both the health and fitness industries. Frankel (2017), a writer for Huffington Post, noted that Styku (a leader in body scanning technology) scans over 450 people a day across industries including the retail industry. However, based on the results from this study, perhaps retailers should use preliminary caution when implementing the body scanning process as part of the in-store experience. Although a body scan may aid the consumer in identifying the correct size (right fit) for apparel, it may also create negative affect/dejection-related emotions, which might hinder the shopping experience and result in negative psychological effects on the consumer. Obviously, eliciting negative affect would be an undesirable outcome for retailers. The body scanning process may further result in consumers feeling the need to manage their appearances through diet and exercise, as was the case in this study. Thus, in the future, researchers should investigate the relationship between 3-D body scanning during consumers’ in-store experiences and the ways in which that influences subsequent purchase intentions. Similarly, clothing and textile scholars who use body scanning technology as part of their class curricula may want to consider alternative means to acquiring body dimensions. Requiring students to scan their bodies then use their 3-D scans as part of the coursework might elicit emotional discomfort and negative psychological effects in the classroom. Given these potential negative outcomes, body scanning assignments should be executed following alternative methods, including using dress forms and mannequins to create 3-D body models. Alternatively, instructors could consider using a fit model who is not affiliated with the university or is unknown to the students.

Limitations and Future Research

Despite the theoretical and practical findings offered here, the study is not without limitations. With any pre–posttest design, it is possible that participants became sensitive to the measures, resulting in less accurate responses in the posttest survey. Additionally, participants were shown the cloud point data version of their avatar as a means to limit potential embarrassment. The cloud point data did include the participants’ facial features; however, there was no skin applied, resulting in less detail (see Figure 1). As body scanning technology advances, participants might be able to be scanned in swimwear or other body conforming apparel and view their avatar in a full-color photographic version. Hence, in the future, researchers should investigate how other formats of avatar production affect participants’ reactions to viewing their own avatar. Such reactions might include self-objectification ramifications that were not tested in this study; thus, self-objectification theory (Fredrickson & Roberts, 1997; Fredrickson, Roberts, Noll, Quinn, & Twenge, 1998) should be tested in future body scanning experimental designs. Finally, as with most body image studies, there was an uneven balance of men and women in the study; the sample was predominately women (67%) and Caucasian (66%). Including a more diverse sample size in terms of ethnicity might yield different results since one’s definition of ideal beauty is often constructed through cultural beliefs. Researchers conducting replication studies should consider having an equal distribution of men and women while also exploring specific ethnic groups and demographics.

Conclusion

The purpose of this study was to answer Loker et al.’s (2008) question, “Will three-dimensional views increase dissatisfaction with our real bodies?” (p. 175) and to examine the role of gender and AI self-discrepancies in 3-D body image research. In short, the answer to Loker et al.’s (2008) call is simply, yes, 3-D views decrease body satisfaction and mood, especially for women, and increase likelihood to engage in AMBs, especially for men. This pre–posttest experimental design and subsequent results serve to propel this line of inquiry toward a better understanding of the impact of body scanning technology on body image, designing with technology, and the implementation of 3-D scanning in clothing and textiles research.

Footnotes

Author’s Note

Author Jessica L. Ridgway is now affiliated to Retail Entrepreneurship, Jim Moran School of Entrepreneurship, Florida State University.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Jessica L. Ridgway

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