sensory marketing research

• IE Corporate Relations
• IE University

Sensory Marketing: Straight to the Emotions

In marketing, the senses are often used as a tool for communicating with consumers. However, this sensory approach is sometimes limited by two factors: a lack of focus on the integrated use of all five senses and the underdevelopment of sensory marketing as a strategic activity for generating experiences, consolidating a brand’s image, and differentiating products from the competition. Thanks to these elements, plus the growing importance of emotions in driving decisions and generating loyalty, marketers are advised to adopt a methodical and integrated approach to sensory marketing as a core activity capable of enriching the value of a brand.

The senses play a key role in consumer perceptions and exert a powerful influence over buying decisions. Marketers have long sought to integrate the senses into brand communications, albeit generally in a limited and partial way. Today, sensory marketing is recognized as an essential tool for strengthening the connection between brand and consumer by stimulating all the senses and generating emotions.

As part of the marketer’s quest to connect with—and adapt to—today’s constantly evolving and increasingly demanding consumers, sensory marketing is now considered to be a top-priority activity. Sensory marketing leverages all five senses to influence perceptions, memories, and learning processes, with the aim of manipulating consumers’ motivations, desires, and behavior . The goal is to create a sensory experience that strengthens the connection with users through a process that involves both the rational and the emotional parts of the brain, although to varying degrees. As part of this process, the subconscious component facilitates automatic decision-making and behaviors on the basis of lessons learned through past experiences.

The development of sensory marketing has been driven by two main factors. The first factor is scientific research, particularly in the field of neuroscience, which is closely linked to the marketing function. Neuroscience helps us understand the brain processes involved in perception and behavior , as well as the role played by emotion and reason. The second factor is the evolution of the markets. Today’s markets are increasingly competitive and global . Brands, and their ability to differentiate themselves, are more important than ever, and buying behavior is increasingly driven by emotional factors rather than rational processes.

As a consequence, three main areas of activity have developed in sensory marketing . The first area focuses on the creation of buyer and user experiences through what is known as “360° sensory marketing,” with the aim of influencing every stage of the buyer’s behavior . The second area involves harnessing all five senses in an integrated manner, aligning the entire sensory experience towards a single objective. And finally, the third area involves using the senses to consolidate the brand, with emphasis on one predominant, identity-shaping sense—that is, a sensory signature.

A memorable experience can forge a stronger connection to the product or service, increase satisfaction, and influence the consumer’s behavior and attitude.

Optimizing the experience

Creating the right sort of shopping experience—either physical or virtual—is a high-priority objective for brands. A memorable experience can forge a stronger connection to the product or service, increase satisfaction, and influence the consumer’s behavior and attitude. As a result, the consumer becomes more predisposed to make a purchase, spends more time in the store, has more exposure to the various categories, and in turn becomes more predisposed to make future purchases. By setting this process in motion, the brand also improves its image.

Consumer behavior can be influenced by sensory marketing to generate experiences at every stage of the buying process: activation of desire, awareness of the product or service, assessment of the product or service in relation to other options, purchase, and post-purchase evaluation of use or consumption. This sort of 360° sensory marketing serves to define the points of contact between the consumer and the brand at every behavioral stage: before the purchase, during the purchase, and during final use.

Source: Compiled by the authors using data from Prophet.

The points of contact are identified and prioritized according to their importance in the buying process in terms of the specific objectives of each stage, which include the development of brand awareness, the communication of added value and relevance to the consumer, and the actual user experience. Once the partial objectives for each stage have been set, the specific senses that influence each objective are identified and their functions and characteristics are established in order to maximize their contribution to the overall objectives.

Of key importance are two “moments of truth” that take place when the brand interacts with its market. The first moment occurs when the consumer makes a selection at the point of sale , and the second one occurs when the user experiences the product and its benefits.

A higher degree of sensory stimulation means more communication and a better experience.

Five integrated senses

The importance of each of the five senses in the transmission of perceptions and the generation of experiences depends on the nature of the product or service and the stage of buying behavior in question. As a rule, however, a higher degree of sensory stimulation means more communication and a better experience. Research has shown that the involvement of multiple senses can have a multiplier effect on perceptions when the senses in question communicate synergistic messages. In other words, each sensory stimulus reinforces the messages conveyed by all the others, giving rise to stronger, more consistent, and more holistic perceptions. This integrated accumulation of sensory impacts improves the consumer’s perception, lodging it deeper into his or her memory. Multisensory perceptions therefore facilitate faster product recognition and attribution in response to stimuli, as well as higher processing speeds and consequently a better assessment of the message.

Source: Hollis, N. (2007). Smelly Business. ESOMAR Fragrance 2007. Millward Brown.

Vision and touch are recognized as the main senses involved in the perception of most products, but they are typically associated with the more functional objectives and with a predominantly conscious process. The generally lesser-valued senses of taste, smell, and hearing contribute a more emotional component in the generation of experiences and have a greater impact on loyalty.

Sensory branding and sensory signature

The main strategic objective of sensory marketing is to communicate a brand image—in other words, sensory branding. The goal is to use the senses to reinforce the product’s attributes, functional or emotional benefits, values, and personality, conveying its relevance to the consumer and helping to communicate its brand identity, while at the same time—and most importantly—communicating the product’s differential value for a specific segment of customers in an increasingly competitive market.

Sensory branding is developed through a sensory strategy , which determines which senses will be used in the communication of the image and connects each sense with the consumer, while also defining the messages and experiences to be developed by each sense. A key part of sensory branding is the development of a brand’s sensory signature—the main perception associated with a product or service. The sensory signature identifies and differentiates the product and conveys the main message that the marketers wish to communicate to the market.

The sensory signature summarizes a product’s positioning and main competitive advantage. Selecting this signature is therefore a critical decision for any brand. There are only two requirements for a sensory signature: the sense selected must be suitable for communicating the category to a particular consumer segment and, most importantly, the signature must communicate the brand’s differential value proposition. As long as those two requirements are met, any sense can become a sensory signature.

Source: compiled by the authors.

A brand’s sensory strategy is developed through various marketing activities, including the details of the product itself (name, brand, packaging, formula, etc.), the way in which it is communicated (advertising, promotions, sales arguments, etc.), and actions associated with the point of sale (location, merchandising, etc.).

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• Yale Center for Customer Insights

When Sensory Marketing Works and When it Backfires

• Aparna Sundar
• Theodore J. Noseworthy

It depends on whether your brand is sincere or exciting.

If you’ve ever picked up a product and took note of how it feels in your hand, you understand the power of sensory marketing. Manufacturers understand it too, which is why tactile information like the famous contour of a Coca-Cola bottle or the textured burlap packaging of Marfa brand soaps are unique and memorable. Some manufacturers also incorporate smell, as in the scratch-and-sniff packaging by Glade and Tide, while others rely on color, such as the trademark brown of UPS or the robin’s egg blue of Tiffany & Co.

• AS Aparna Sundar is an assistant professor of marketing at the Lundquist College of Business at the University of Oregon. Her research focus on aesthetic issues in marketing.
• TN Theodore J. Noseworthy is an associate professor of marketing at the Schulich School of Business at York University, a Canada Research Chair in Entrepreneurial Innovation and the Public Good, and the Scientific Director of the NOESIS: Innovaton, Design, and Consumption Laboratory.

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ORIGINAL RESEARCH article

Evaluating replicability of ten influential research on sensory marketing.

• 1 Department of Management, The University of Tokyo, Tokyo, Japan
• 2 Department of Food Science and Business, Miyagi University, Sendai, Japan
• 3 Department of Management, Chukyo University, Nagoya, Japan

We attempted to evaluate the replicability and generalizability of ten influential research on sensory marketing by conducting a high-powered and pre-registered replication in online settings in non-WEIRD consumers. The results revealed that only 20% of findings could be successfully replicated, and their effect sizes are as half as the original ones. Two successful studies had relatively larger sample sizes, used sound symbolism, and employed within-participants manipulation of senses. No studies involving visual factors, between-participant manipulation of senses, or interactions between factors could be replicated. Our findings reveal an initial estimate of the replicability and generalizability of sensory marketing.

Introduction

Over the past decade, sensory marketing has become a growing field of research. Sensory marketing is “marketing that engages consumers' senses and affects their perception, judgment, and behavior” ( Krishna, 2012 , p. 332). An influential review, “An integrative review of sensory marketing: Engaging the senses to affect perception, judgment, and behavior” ( Krishna, 2012 ) has been cited more than 1300 times (Google Scholar, 11/2021). Many studies have demonstrated that sensory factors affect consumers' perceptions, judgments, and behaviors ( Krishna and Schwarz, 2014 ; Krishna et al., 2016 ; Wörfel et al., 2022 ). A growing body of research has demonstrated that vision ( Biswas et al., 2017 ), audition ( Motoki et al., 2022 ), olfaction ( Madzharov et al., 2015 ; Iseki et al., 2021 ), touch ( Zwebner et al., 2014 ), and tastes ( Litt and Shiv, 2012 ) influence consumer behaviors. Several reviews on sensory marketing also have appeared including multisensory store atmospherics ( Spence et al., 2014 ), grounded cognition ( Krishna and Schwarz, 2014 ), package design ( Krishna et al., 2017 ), advertising ( Krishna et al., 2016 ), and new technologies ( Petit et al., 2019 ). However, to our knowledge, the replicability of sensory marketing findings has not yet been addressed.

Psychology and behavioral sciences face a replication crisis. Replication can be regarded as the cornerstone for establishing scientific findings in psychology, marketing, and consumer research ( Asendorpf et al., 2013 ; Lynch et al., 2015 ; Ding et al., 2020 ; Edlund et al., 2022 ). Most research on psychology and behavioral science (including consumer psychology) relies on testing statistical hypotheses using empirical observations and data ( Shrout and Rodgers, 2018 ). Statistically significant findings can be replicated using an independent dataset ( Shrout and Rodgers, 2018 ). However, since the 2010s, it has been found that many of the findings published in top-tier journals cannot be replicated ( Open Science Collaboration, 2015 ; Camerer et al., 2018 ). For example, one of the first large replication attempts has found that only 36% of one hundred psychology findings can be replicated ( Open Science Collaboration, 2015 ). Non-successful replications of classic and famous findings in the field of social psychology (morality salience and ego depletion) have also been reported ( Vadillo et al., 2018 ; Klein et al., 2019 ), though consumer research has relied on classical findings ( Ferraro et al., 2005 ; Baumeister et al., 2008 ; Fransen et al., 2008 ). The findings that fail replication include research in the fields of cognitive and social psychology ( Open Science Collaboration, 2015 ; Klein et al., 2019 ). Further, given that consumer psychology has strived by applying the theory/findings of social and cognitive psychology ( Hoyer et al., 2012 ; Malter et al., 2020 ), evaluation of the replicability of consumer research should be required.

Consumer psychology is no exception to replication crises. Data Colada ( https://datacolada.org/ ) attempted to replicate ten studies published in the Journal of Marketing Research and the Journal of Consumer Research. The replication attempts by Data Colada revealed that most findings could not be replicated. Even when the results showed the same direction, the effect size 1 was much smaller than that of the original authors ( https://datacolada.org/92 ). This suggests that the effect size of the original findings would be inflated, and it is important to re-evaluate the effect size by a high-powered replication study. Moreover, other researchers have failed to replicate the findings of consumer psychology ( Tunca and Yanar, 2020 ; Tunca et al., 2022 ), even though some can be replicated ( Sarstedt et al., 2017 ). For example, neither two findings that appeared in the Journal of Consumer Research ( Dubois et al., 2011 ; Wang and Griskevicius, 2013 ) were unsuccessfully replicated ( Tunca and Yanar, 2020 ; Tunca et al., 2022 ). Moreover, it has been estimated that most of the process evidence in marketing obtained by mediation analyses is noisy and inadequately powered ( Charlton et al., 2021 ). Conclusively, one researcher has estimated that the replication rate of marketing and consumer research is ~10% ( https://www.openmktg.org/research/replications ).

The present study used pre-registered replication to evaluate the replicability of ten influential research on sensory marketing. Replications of marketing research have been reported, especially in the Replication Corner of the International Journal of Research in Marketing ( Lynch et al., 2015 ). Most replication research published in the Replication Corner is successful (i.e., original findings are successfully replicated) ( Lynch et al., 2015 ). Additionally, only three studies can be regarded as unsuccessful (i.e., the replication score was below the midpoint) ( Lynch et al., 2015 ). In other words, 90% (27/30) of replication studies can be regarded as successful (i.e., replication score was above midpoint) ( Lynch et al., 2015 ). The ratio of successful replication appears to contradict that of pre-registered replication ( https://www.openmktg.org/research/replications ). It is possible that publication bias (i.e., only successful replications were submitted and then published) and/or selective reporting inflated the ratio of successful replications in the Replication Corner ( Lynch et al., 2015 ). Further, it has been suggested that pre-registration 2 helps create credible and robust science ( van't Veer and Giner-Sorolla, 2016 ; Nosek et al., 2018 ). Actually, pre-registration has been also recently regarded as an essential research practice in consumer psychology ( Simmons et al., 2021 ). Therefore, to obtain findings that are more reliable, we employed a pre-registered replication.

It should be noted that our replication attempts were conceptual replication with an extension of populations and settings ( LeBel et al., 2018 ). Most of the facets in the experimental design (e.g., effect, hypotheses, IV/DV construct, IV/DV operationalization, and IV/DV stimuli) ( LeBel et al., 2018 ) are the same as (or very close to) the original research. The populations, physical settings, and contextual variables were different from those in the original research. Most of the original research recruited WEIRD (Western, Educated, Industrialized, Rich, and Democratic) samples (Western university students in most cases) in a laboratory setting. By contrast, our replication research recruited non-WEIRD consumers on an online platform. It has been suggested that participants of most research are from WEIRD samples (96% of top-psychology journals), but WEIRD people constitute only 12% of the population ( Henrich et al., 2010 ). Moreover, consumer research has moved traditional settings/participants (i.e., university students in a lab setting) to the general population from crowdsourcing platforms ( Goodman and Paolacci, 2017 ). Together, our attempts at conceptual replication test whether previous findings in sensory marketing can be generalized to non-WEIRD consumers using an online platform.

The present study aimed to replicate ten influential pieces of research on sensory marketing, relying on the recent attempts of empirical audits and reviews ( O'Donnell et al., 2021 ). The empirical audit and review can be referred to as an approach to assessing the evidentiary value of a research area ( O'Donnell et al., 2021 ). Based on the concepts of empirical audit and review, we replicated ten influential studies on sensory marketing (see Table 1 ). These studies include sound symbolism in brand names ( Klink, 2000 ; Yorkston and Menon, 2004 ; Lowrey and Shrum, 2007 ), product color ( Hagtvedt and Brasel, 2017 ), logo design ( Cian et al., 2014 ; Jiang et al., 2015 ), and visual product depictions ( Madzharov and Block, 2010 ; Elder and Krishna, 2011 ; Chae and Hoegg, 2013 ; Romero and Biswas, 2016 ).

Table 1 . Summary of previous findings that the current research attempts to replicate.

Ten studies were selected for the replication project. Our replication attempts were based on an empirical audit and review ( O'Donnell et al., 2021 ), which is an approach to assessing the evidentiary value of a research area ( O'Donnell et al., 2021 ). According to O'Donnell et al. (2021 ), the following steps involve empirical audits and reviews: (1) identifying the bounds of a topic area; (2) selecting studies that belong to a topic area; and (3) replicating the studies. Based on the concepts of empirical audit and review, we replicated ten influential studies on sensory marketing (see Table 1 ).

Our selection criteria include (1) sensory marketing research, (2) having more than 100 citations, (3) sufficient materials, and/or procedures publicly available, and (4) could be replicated with an online platform. Criterion (1) indicates that studies need to manipulate sensory stimuli in consumer contexts. We did not set any operational definition of sensory stimuli similar to a previous empirical audit and review ( O'Donnell et al., 2021 ), and accepted all manipulations of sensory stimuli written by the original authors. Criterion (2) was added to refer to “influential” research. One factor related to influential research is the number of citations. We set more than 100 citations as our operational requirements. Criterion (3) is needed because we attempted to replicate the research as directly as possible. Criterion (4) was included since we could not recruit a large number of participants during the COVID-19 pandemic and decided to use an online platform for the current project. Two authors evaluated each of the criteria.

We selected one study investigating simple effects (i.e., simple effects of sensory stimuli without mediation and/or moderation) in consumer contexts when the research contained multiple qualifying studies. It should be noted that we did not attempt to replicate all influential research on sensory marketing. It has been suggested that empirical audits and reviews do not always include all relevant research ( O'Donnell et al., 2021 ) possibly because of the limitations of time, money, and human resources.

We opened up the data collection to recruit 1000 Japanese participants. The sample size was determined to be at least 2.5 times the sample size of any original paper ( Simonsohn, 2015 ; O'Donnell et al., 2021 ). The high-power, large sample size allowed us to detect small effects and reduce type 1 errors. Following the pre-registered exclusion criteria, we excluded 247 participants who failed an instructional manipulation check (IMC; Oppenheimer et al., 2009 ; Miura and Kobayashi, 2016 ) and an attention check question (ACQ; e.g., Oppenheimer et al., 2009 ; Aust et al., 2013 ). Detailed information about the data exclusion criteria can be found in the Appendix . Finally, the data of 823 participants (mean age = 40.77, SD of age = 10.48, 428 males, 391 females, 4 did not prefer to say) were analyzed. The participants were recruited from Crowdworks and completed an online survey created on Qualtrics. The study was pre-registered ( https://aspredicted.org/ZW2_C76 ). All participants gave their informed consent before the survey, and the study was conducted following the ethical guidelines of the Declaration of Helsinki.

The participants performed all the studies in randomized order. Within each study, participants were randomly allocated to one condition (e.g., one of the two conditions for Romero and Biswas, 2016 ). Participants completed all the studies within about 10 minutes ( M = 609 s). This procedure was similar with previous replication attempts involving many studies (e.g., participants completing 13 or 15 studies; see Forsell et al., 2019 ). Methods, analyses, and sample sizes were performed before data collection. The details of the methods, procedures, and analyses of each study are presented in the Supplementary material (Methodological Detail Appendix). All surveys were conducted in Japanese. The materials were translated by two authors (K.M. and S.I.).

Klink (2000), marketing letters

We conducted binominal tests as described by Klink (2000) . The data were also analyzed using chi-squared tests, although not pre-registered. The analyses were reported based on chi-squared tests because the statistical value (χ 2 ) can be converted into an effect size (r), commonly used in our replication research. A summary of these findings is presented in Table 2 . We successfully replicated previous findings except for H1i and H1k (i.e., more masculine and stronger).

Table 2 . Summary of findings that the current research replicated Klink (2000) .

Shrum et al. (2012), IJMR

We conducted a 2 (vowel: front, back) × 2 (product category: convertible/knife, 4 × 4 SUV/hammer) mixed model analysis of variance (ANOVA), with a vowel as a within-participants factor and product category as between-participant factors. The results of the analysis revealed a main effect of vowel ( F 1,821 = 252.615, p < 0.001, η 2 p = 0.235) and product category ( F 1,821 = 18.478, p < 0.001, η 2 p = 0.022). As predicted, a significant interaction was found ( F 1,821 = 55.002, p < 0.001, η 2 p = 0.063). Front vowel sounds were preferred over back vowel sounds for convertible and knife (52.56–47.45%; F 1,821 = 4.717, p = 0.030, η 2 p = 0.011). In contrast, back vowel sounds were preferred over front vowel sounds for 4 × 4 vehicle and hammer (59.59–40.41%; F 1,821 = 70.981, p < 0.001, η 2 p = 0.149). Thus, we successfully replicated the previous findings ( Table 3 ).

Table 3 . Summary of findings that the current research replicated Shrum et al. (2012) .

Hagtvedt and Brasel (2017), journal of consumer research

ANOVA was conducted to investigate the effects of saturation (high and low) on the size estimate of the product. The results of the analysis did not reveal any effects of saturation (high saturation: M = 15.46, SD = 1.94, vs. low saturation: M = 15.42, SD = 1.84; F 1,821 = 0.095, p = 0.758, η 2 p = 0.000). ANOVAs were conducted to investigate the effects of saturation (high and low) on attention and arousal. The results of the analysis revealed that high (vs. low) saturation increased attention (high saturation: M = 5.30, SD = 1.07; vs. low saturation: M = 4.75, SD = 1.14; F 1,821 = 49.238, p < 001, η 2 p = 0.057) and arousal (high saturation: M = 5.83, SD = 1.10; vs. low saturation: M = 5.41, SD = 1.20; F 1,821 = 27.539, p < 0.001, η 2 p = 0.033). Given that our main result (i.e., saturation and size estimates) was not significant, we did not perform pre-registered mediation analyses.

Romero and Biswas (2016), journal of consumer research

A chi-squared test was performed to investigate whether the frequencies of choosing healthy food differ in the positions where the foods are located on the left vs. the right side of unhealthy foods. The results of the analysis showed that the frequency of choosing healthy food did not differ regardless of whether it was on the left (24.44%) or the right (25.59%) of the unhealthy food (χ 2 = 0.091, p = 0.763).

Yorkston and Menon (2004), journal of consumer research

An ANOVA was performed to test the effects of sound symbolism of brand name ( F rish, Frosh), diagnosticity of brand name (test, true), and timing (simultaneous, after) on the attribute perception index. The results of the analysis revealed the main effect of sound symbolism of brand name ( F 1,821 = 7.492, p = 0.006, η 2 p = 0.009) such that frosh (vs. frish) had higher ratings in the attribute perception index. However, the results of the analysis did not show a three-way interaction ( F 1,821 = 1.053, p = 0.305, η 2 p = 0.001) or an interaction between sound symbolism and diagnosticity ( F 1,821 = 0.118, p = 0.731, η 2 p = 0.000).

An ANOVA was also conducted to investigate the effects of sound symbolism of brand name ( F rish, Frosh), diagnosticity of brand name (test, true), and timing (simultaneous, after) on the brand attribute index. The results of the analysis did not reveal the main effect of sound symbolism of brand name ( F 1,821 = 0.114, p = 0.736, η 2 p = 0.000), the three-way interaction ( F 1,821 = 1.400, p = 0.237, η 2 p = 0.002), or the interaction between sound symbolism and diagnosticity ( F 1,821 = 1.174, p = 0.279, η 2 p = 0.001).

Elder and Krishna (2011), journal of consumer research

ANOVA was conducted to investigate the effects of product orientation on a participant's dominant hand (match or mismatch) on purchase intentions. The results of the analysis did not reveal any effects on purchase intentions (match: M = 3.39, SD = 1.75; mismatch: M = 3.36, SD = 1.82; F 1,821 = 0.068, p = 0.794, η 2 p = 0.0001). ANOVA was also conducted to investigate the effects of orientation (match and mismatch) on mental simulations. The results of the analysis did not reveal any effects on mental simulations (match: M = 4.87, SD = 2.00 vs. mismatch: M = 4.86, SD = 2.03; F 1,821 = 0.006, p = 0.941, η 2 p = 0.0000). Given that our main result was not significant, we did not perform pre-registered mediation analyses.

Jiang et al. (2015), journal of consumer research

An ANOVA was conducted to investigate the log-shape-by-product attribute interactions. The results of the analysis did not reveal logo shape-by-cut attribute interactions ( F 2,820 = 0.141, p = 0.868, η 2 p = 0.000). For completeness, we analyzed the effects of circular vs. angular-logo on comfort (circular-logo: M = 5.33, SD = 1.35, vs. angular-logo: M = 5.34, SD = 1.30; F 1,550 = 0.004, p = 0.949, η 2 p = 0.000) and durability judgment (angular-logo: M = 5.37, SD = 1.31 vs. circular-logo: M = 5.37, SD = 1.32; F 1,550 = 0.0001, p = 0.994, η 2 p = 0.000). The analyses did not reveal the effects of logo shape on comfort and durability judgments.

Additionally, Jiang et al. (2015) confirmed that logo shape does not affect attitudes. Therefore, an ANOVA was conducted to investigate the effect of angular and circular-logo shapes on attitude. The results showed that there were no differences in attitudes between logo shapes (circular-logo: M = 5.08, SD = 1.23, vs. angular-logo: M = 5.13, SD = 1.20, vs. control: M = 5.18, SD = 1.21; F 2,820 = 0.478, p = 0.620, η 2 p = 0.001).

Chae and Hoegg (2013), journal of consumer research

In this replication, 85 participants were unable to identify the shopping goal in the manipulation check and were excluded, leaving 738 participants in the analysis. An ANOVA was conducted to investigate the effects of desirable attributes (antique, modern, and control) and position (left and right) on product attitudes. The results of the analysis revealed the main effect of the desirable attribute ( F 2,732 = 7.952, p < 0.001, η 2 p = 0.021) but not the main effect of position ( F 1,732 = 1.093, p = 0.296, η 2 p = 0.002). A significant interaction was found ( F 2,732 = 2.758, p = 0.064, η 2 p = 0.008). However, post hoc analyses did not reveal any hypothetical findings. Antique priming did not influence product attitudes depending on position (left: M = 5.95, SD = 1.47 vs. right: M = 5.98, SD = 1.53; F 1,732 = 0.032, p = 0.859, η 2 p = 0.000). Modern priming did not influence product attitudes depending on position (right: M = 5.47, SD = 1.41 vs. left: M = 5.63, SD = 1.68; F 1,732 = 0.695, p = 0.405, η 2 p = 0.001). Given that our main result was not significant, we did not perform pre-registered mediation analyses.

Cian et al. (2014), journal of marketing research

An ANOVA was conducted to investigate the effect of logo dynamism (high and low) on brand attitudes. The results of the analysis did not reveal an effect on brand attitudes (high dynamism: M = 5.64, SD = 1.05; vs. low dynamism: M = 5.59, SD = 1.15; F 1,821 = 0.393, p = 0.531, η 2 p = 0.001). An ANOVA was also conducted to investigate the effect of logo dynamism (high and low) on perceived movement as a manipulation check. The results of the analysis revealed an effect on perceived movement (high dynamism: M = 5.85, SD = 1.27, vs. low dynamism: M = 5.00, SD = 1.50; F 1,821 = 77.863, p < 0.001, η 2 p = 0.087).

Madzharov and Block (2010), journal of consumer psychology

ANOVA was conducted to investigate the effect of product units displayed on the package ( f our and seven) on perceived product quantity. The results of the analysis did not reveal any effect on perceived product quantity ( f our: M = 21.40, SD = 100.28; seven: M = 17. 71, SD = 9.14; F 1,821 = 0.565, p = 0.452, η 2 p = 0.001). Since there was an outlier in the response for perceived product quantity (i.e., a response = 2020), the Mann-Whitney U test (nonparametric test) was not pre-registered. The results of the analysis did not reveal any effect on the perceived product quantity ( U = 78,727, Z = 1.743, p = 0.081, r = 0.061).

ANOVA was also conducted to investigate the effect of product units displayed on the package ( f our, seven) on the serving size. The results of the analysis did not reveal an effect on serving size ( f our: M = 4.45, SD = 3.00, vs. seven: M = 4.80, SD = 3.52; F 1,821 = 2.380, p = 0.123, η 2 p = 0.003).

Summary of findings

A summary of our replication findings is shown in Tables 4 , 5 . The results reveal that only 20% (2/10) of the findings could be successfully replicated in terms of statistical significance ( p < 0.05; 95% CI not including zero in the hypothesized direction). In other words, replication of eight studies (80%, 8/10) did not reach statistical significance. Moreover, the replication effect sizes were smaller than the original effect sizes in all ten studies. The replication effect sizes of the two successfully replicated findings were half of the original ones. We also provided estimated power and the upper bounds of 95% for the estimated power of the original studies. Most of the original studies (i.e., their main DVs of 8/10 unsuccessful replications) included <20% power in their 95% CIs. This suggests that most of the original findings that we attempted to replicate appear to have much smaller effects that thousands of sample sizes are needed for detection.

Table 4 . Main findings of selected 10 studies.

Table 5 . Detailed findings of selected 10 studies.

We employ empirical audits and reviews ( O'Donnell et al., 2021 ), which can be used to reach aggregate conclusions to evaluate research designs (e.g., manipulations and measures) that empirically strengthen (or weaken) evidence. Two successful studies had relatively larger sample sizes, used sound symbolism, and employed within-participants manipulation of senses. No studies involving visual factors, between-participant manipulation of senses, or interactions between factors could be replicated. This suggests that specific research designs (e.g., the type of senses, manipulation of senses, and number of sample sizes) influence the credibility of the findings.

Our research examined the replicability of ten influential studies on sensory marketing. Sensory marketing has attracted the attention of researchers and practitioners over the last decade ( Krishna, 2011 , 2012 ). An influential article on sensory marketing ( Krishna, 2011 ) was cited more than 1400 times in Google Scholar (February 2022). However, to our knowledge, no research has attempted to replicate these findings from the perspective of empirical audit and review ( O'Donnell et al., 2021 ). In general, our findings demonstrated that 20% (2/10) of the influential research on sensory marketing was successfully replicated. Our findings suggest that not all influential research can be replicated, and indicate the importance of replication research to examine the reliability of prior findings in consumer psychology and marketing.

Contribution to replication attempts in consumer psychology

Our findings contribute to replication attempts in consumer psychology. The sciences are facing a replication crisis. Since it has been revealed that famous psychological findings are less replicable ( Open Science Collaboration, 2015 ), several attempts have been made in psychology ( Klein et al., 2018 ), economics ( Camerer et al., 2016 ), and behavioral sciences ( Camerer et al., 2018 ). However, replication attempts in consumer psychology have been relatively rare [see O'Donnell et al., 2021 and Data Colada ( http://datacolada.org/ ) for a few exemptions]. Our study is one of the first attempts to replicate research on consumer psychology, especially from the standpoint of empirical audits and reviews ( O'Donnell et al., 2021 ). The replication findings revealed that 20% could be replicated in terms of pre-registered analyses ( p < 0.05). Together, our results demonstrate the replicability of the scientific findings in consumer psychology.

Which findings can or cannot be replicated?

Our results reveal the sensory marketing findings that can or cannot be replicated. First, sensory marketing research that capitalizes on sound symbolism is replicable. Our replication attempts successfully replicated two findings ( Klink, 2000 ; Shrum et al., 2012 ) and partially replicated one of them ( Yorkston and Menon, 2004 ). This is consistent with the argument that sound symbolism is robust and found in diverse cultures ( Cwiek et al., 2022 ). Among sensory marketing research, the reliability of the findings might differ depending on the type of sensory stimuli. Findings based on sounds, especially sound symbolism, appear to be more reliable than those based on other sensory stimuli.

Second, replicable findings tend to manipulate sensory stimuli within-participants. Two of the replicable findings adopt the within-participants manipulation of sensory stimuli ( Klink, 2000 ; Shrum et al., 2012 ); though not all research on within-participants manipulation can be replicated ( Romero and Biswas, 2016 ). Two sensory stimuli (e.g., hypothetical brand names, including front vowels and back vowels) are evaluated side by side simultaneously (i.e., joint rather than separate evaluation) ( Hsee, 1996 ). Recent research has suggested that the effects of sensory stimuli (i.e., verticality and horizontality of photos) on judgment appear to be more reliably obtained in within-participants than between-participant designs ( Zhang et al., 2022 ). Some findings that were not successfully replicated in our research employed between-participant manipulations of sensory stimuli ( Elder and Krishna, 2011 ; Hagtvedt and Brasel, 2017 ). Together, our replication attempts suggest that within-participants manipulation creates more reliable and replicable findings.

Third, previous findings obtained from larger samples and effect sizes are more replicable. We successfully replicated two findings that had relatively larger samples and effect sizes ( Klink, 2000 ; Shrum et al., 2012 ). Their sample sizes ( Klink, 2000 ; Shrum et al., 2012 ) are the top two largest among the ten research targets. The effect size of Klink (2000) is the largest, and that of Shrum et al. (2012) is the third largest among the ten sensory marketing studies. Klink (2000) had n = 265 and r = 0.46 (mean r of Study 1 H1a-m). Shrum et al. (2012) had n = 357 (combined analysis of Experiment 1a-c) and r = 0.39. This appears to be consistent with the evidence that the sample size and effect size in the original study were associated with the success of the replications ( Soto, 2019 ); however, see Altmejd et al. (2019) .

Our findings contribute to the research on sensory marketing in digital environments. Consumers tend to spend a lot of time shopping in digital environments (e.g., e-commerce). This tendency is present in WEIRD and non-WEIRD consumers. Although some studies have attempted to investigate the role of sensory stimuli in consumer behavior in an online setting (e.g., Rodríguez et al., 2021 ), less is known about how previous influential findings on sensory marketing, mostly obtained from WEIRD consumers in an offline setting, can be generalized to non-WEIRD consumers in an online setting. To fill this gap, we attempted to replicate ten influential studies on sensory marketing in digital environments by recruiting non-WEIRD consumers. The results revealed that only 20% of the findings of the influential studies can be replicated by non-WEIRD consumers in an online setting. Given that our replication research does not cover all aspects of sensory marketing in digital and/or virtual environments, more such research is needed. For example, it would be intriguing to test in future research the replicability and generalizability of findings involving new sensory-enabling technologies ( Petit et al., 2019 ).

Limitation of the current study

Our study had the following limitations. First, the differences in participants' nationality/cultures between the present and the original research might influence our findings. Our participants were non-WEIRD consumers (i.e., Japanese consumers), while the participants in the original research were WEIRD consumers. It should be noted that we did not aim for the exact or direct replications ( LeBel et al., 2018 ). Rather, we conducted conceptual replications by recruiting non-WEIRD consumers to test the generalizability of previous findings on sensory marketing. Our results cast doubt on the generalizability of previous findings on sensory marketing. Further research should conduct the direct replications to recruit the same characteristic of the participant with the original studies. Second, our study did not attempt to replicate influential research on sensory marketing that addressed olfaction, taste, and haptics. Namely, the senses manipulated as independent variables were limited to auditory and visual senses, although replication of Klink (2000) addressed all senses except the independent variable. It is important to conduct additional studies that consider various sense factors. Moreover, participants went through ten studies sequentially at the same time. The fatigues, the order effects, and/or attentiveness possibly influence our findings. However, it should be noted that the required time (and the number of studies) was shorted than that of previous attempts of replications projects. Our participants completed the ten studies within about 10 minutes ( M = 609 s), while participants in a previous replication project completed 13 or 15 studies for about 30 min (see Klein et al., 2018 ). Our attention check was also conducted after performing all the studies, which possibly suggesting that participants could attend to the tasks. Given this, our procedures apparently not induced excessive fatigues and not lead to the problem of attentiveness. The order of ten studies were also randomized and could cancel out any order effects. Nevertheless, further research should treat with issues. Finally, our results might be influenced by differences in the materials and instructions. We used the same visual stimuli as the original ones. In the case of auditory stimuli (i.e., sounds in brand names), the manipulation of the auditory stimuli was the same as that of the original ones. However, the presentation of the stimuli differed between the original study and our study. Klink (2000) presented brand-name stimuli in a 2-page booklet. Yorkston and Menon (2004) had participants read a press release containing brand-name stimuli. We cannot confirm whether our replication study exactly followed the instructions and materials in the original research because detailed instructions and materials were missing in many cases. Further research is required to clarify this issue.

We evaluated the replicability of ten influential research on sensory marketing by conducting a high-powered and pre-registered replication. The results demonstrated that only 20% (2/10) of study findings could be successfully replicated, and their effect sizes are half of the original ones. Successfully replicated findings are characterized by relatively larger sample sizes, use of sound symbolism, and within-participants manipulation of the senses. Our findings reveal an initial estimate of the replicability of sensory marketing and provide implications for how to build a cumulative science in consumer psychology.

Data availability statement

The original contributions presented in the study are included in the article/ Supplementary material , further inquiries can be directed to the corresponding author/s. Data, materials, and analysis code are available at https://osf.io/tnmvq/ .

Ethics statement

Ethical approval was not provided for this study on human participants because online experiments and participants' responses are anonymous. The patients/participants provided their written informed consent to participate in this study.

Author contributions

Conceptualization, data curation, formal analysis, investigation, methodology, project administration, resources, software, validation, visualization, and writing—review and editing: KM and SI. Roles/writing—original draft: KM. Both authors contributed to the article and approved the submitted version.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcomm.2022.1048896/full#supplementary-material

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2. ^ Pre-registration refers to the process of registering the contents of a study to be conducted with a third-party organization (e.g., https://osf.io/ , https://aspredicted.org/ ). The contents to be pre-registered include hypotheses, sample size, independent variables, dependent variables, analysis methods, and data exclusion criteria. Researchers must conduct the study based on these pre-registered contents. In principle, deviations from the pre-registered content are not permitted. Hence, pre-registration can prevent questionable research practices (QRPs) such as p-hacking, HARKing, and cherry picking.

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Keywords: sensory marketing, consumer psychology, replication, pre-registration, consumer behavior

Citation: Motoki K and Iseki S (2022) Evaluating replicability of ten influential research on sensory marketing. Front. Commun. 7:1048896. doi: 10.3389/fcomm.2022.1048896

Received: 20 September 2022; Accepted: 30 September 2022; Published: 28 October 2022.

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Copyright © 2022 Motoki and Iseki. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Kosuke Motoki, motoki@e.u-tokyo.ac.jp

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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European Business Review

ISSN : 0955-534X

Article publication date: 17 May 2011

The purpose of this paper is to present the multi‐sensory brand‐experience concept in relation to the human mind and senses. It also seeks to propose a sensory marketing (SM) model of the multi‐sensory brand‐experience hypothesis.

Design/methodology/approach

This paper applies exploratory and explanatory approaches to investigating the multi‐sensory brand‐experience concept within the context of discovery. The qualitative study is built on primary and secondary data sources, including personal interviews with experts and managers.

The multi‐sensory brand‐experience hypothesis suggests that firms should apply sensorial strategies and three explanatory levels within an SM model. It allows firms through means as sensors, sensations, and sensory expressions to differentiate and position a brand in the human mind as image.

Research limitations/implications

A theoretical implication is that the multi‐sensory brand‐experience hypothesis emphasizes the significance of the human mind and senses in value‐generating processes. Another theoretical implication is that the hypothesis illustrates the shortcomings of the transaction and relationship marketing models in considering the multi‐sensory brand‐experience concept. It is worth conducting additional research on the multi‐sensory interplay between the human senses in value‐generating processes.

Practical implications

The findings offer additional insights to managers on the multi‐sensory brand‐experience concept. This research opens up opportunities for managers to identify emotional/psychological linkages in differentiating, distinguishing and positioning a brand as an image in the human mind.

Originality/value

The main contribution of this research lies in developing the multi‐sensory brand‐experience hypothesis within a SM model. It fills a major gap in the marketing literature and research in stressing the need to rethink conventional marketing models.

• Sensory perception
• Marketing models

Hultén, B. (2011), "Sensory marketing: the multi‐sensory brand‐experience concept", European Business Review , Vol. 23 No. 3, pp. 256-273. https://doi.org/10.1108/09555341111130245

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This chapter presents sensory marketing in practice and theory. A sensory marketing framework is discussed and compared with mass and relationship marketing. Five sensorial strategies are suggested that emphasize the human senses as the center of a firm’s sensory marketing. At the end of the chapter the importance of the human senses, the brand, and experience logic in sensory marketing is discussed.

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Hultén, B., Broweus, N., van Dijk, M. (2009). What is Sensory Marketing?. In: Sensory Marketing. Palgrave Macmillan, London. https://doi.org/10.1057/9780230237049_1

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Research Article

The impact of AR online shopping experience on customer purchase intention: An empirical study based on the TAM model

Contributed equally to this work with: Chunrong Guo, Xiaodong Zhang

Roles Data curation, Formal analysis, Investigation, Methodology, Resources, Writing – original draft, Writing – review & editing

Affiliation School of Economics and Management, Ningbo University of Technology, Ningbo, Zhejiang, China

Roles Data curation, Formal analysis, Funding acquisition, Methodology, Resources, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation School of Economics and Management, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia, China

• Chunrong Guo, 
• Xiaodong Zhang

• Published: August 26, 2024
• https://doi.org/10.1371/journal.pone.0309468
• Reader Comments

Augmented Reality (AR) offers a rich business format, convenient applications, great industrial potential, and strong commercial benefits. The integration of AR technology with online shopping has brought tremendous changes to e-commerce. The Technology Acceptance Model (TAM) is a mature model for assessing consumer acceptance of new technologies, and applying it to evaluate the impact of AR online shopping experiences on consumer purchase intention is an urgently needed area of research. Firstly, the typical applications of AR in online shopping were reviewed, and the connotations and experiences of AR online shopping were summarized. Secondly, using the five types of AR online shopping experiences as antecedent variables, and perceived ease of use and perceived usefulness as intermediate variables, a theoretical model was constructed to explore the impact of AR online shopping experiences on customer purchase intentions, followed by an empirical study. Finally, suggestions were proposed for optimizing the online shopping experience to enhance purchase intentions. The article expands the application scenarios of the Technology Acceptance Model and enriches the theory of consumer behavior in Metaverse e-commerce.

Citation: Guo C, Zhang X (2024) The impact of AR online shopping experience on customer purchase intention: An empirical study based on the TAM model. PLoS ONE 19(8): e0309468. https://doi.org/10.1371/journal.pone.0309468

Editor: Ricardo Limongi, Federal University of Goias: Universidade Federal de Goias, BRAZIL

Received: May 3, 2024; Accepted: August 8, 2024; Published: August 26, 2024

Copyright: © 2024 Guo, Zhang. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting information files.

Funding: This work was supported by the Interdisciplinary Research Fund of Inner Mongolia Agricultural University, “Research on Open Innovation Intelligent Decision-Making in E-commerce Based on Federated Learning” (Project Number: BR231518); Program for Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region, “Research on E-commerce Intelligent Marketing Based on Multimodal Learning” (Project Number: NJYT24014); National Key R&D Program of China, “Intergovernmental International Science and Technology Innovation Cooperation” Key Special Project, “Research on Sino-Mongolian Agricultural and Pastoral Supply Chain” (Project Number: 2021YFE0190200); National Social Science Fund of China Post-funding Project, “Research on the Internationalization Development of Chinese Cross-border E-commerce Brands” (Project Number: 20FGLB033); Inner Mongolia Autonomous Region Graduate Education Teaching Reform Project, “Research on the Training Model for New Business Graduates in Inner Mongolia under the Background of Digital Economy” (Project Number: JGCG2022059). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

1 Introduction

With the advent of the digital age, augmented reality (AR) technology has shown transformative potential across multiple industries, particularly in the realm of e-commerce [ 1 ]. Major retailers and brand corporations such as Google, Apple, Alibaba, Amazon, and Facebook have begun to employ AR technology to attract customers and boost sales. They actively integrate AR services into their business spheres to enhance customer awareness, brand engagement, and brand loyalty [ 2 ]. Surveys indicate that approximately 75% of consumers expect to experience AR services when shopping online, 71% state that they would shop more frequently if retailers utilized AR, and 40% are willing to pay more for products offered through AR. The AR market is projected to reach $50 billion by 2024 [ 3 – 6 ]. Due to their direct relationship with sales conversion rates and customer satisfaction, consumer shopping experiences have become one of the primary focuses of marketing management [ 7 ]. The AR strategy is crucial for merchants, especially in the highly competitive e-commerce market. A deep understanding and leveraging of AR technology’s potential can provide significant competitive advantages for businesses. Firstly, if studies find that AR experiences significantly enhance consumer purchase intentions, e-commerce platforms will be more inclined to invest in AR technology. Secondly, by identifying specific pain points in the user experience within AR applications through research, e-commerce businesses can optimize their AR applications, enhancing user satisfaction and loyalty. However, current research primarily explores the application of AR technology in e-commerce and its impact on consumer perceptions and behaviors [ 3 – 5 , 8 – 10 ], aiming to understand the psychological and behavioral changes consumers undergo during AR experiences. Whang et al. (2021) adopted the concept of consumer control to investigate the mediating and moderating effects of AR experiences on purchase intention within the shopping environment for beauty products, with a focus on cognitive control and behavioral control [ 11 ]. However, comprehensive studies that specifically investigate the impact of AR online shopping experiences on consumer purchase intention and analyze the intrinsic mechanisms behind consumer acceptance of this new technology remain rare. Unlike previous studies, this research applies the Technology Acceptance Model (TAM) to explore how AR online shopping experiences affect consumer purchase intentions. It evaluates consumers’ attitudes towards AR online shopping experiences, how these experiences influence perceived usefulness and perceived ease of use, and how these factors translate into purchasing behavior. Although the TAM model has been widely used to assess consumer acceptance of new technologies, applying it to evaluate AR use in online shopping remains a largely unexplored area of research. This study introduces five types of AR online shopping experiences as antecedent variables to comprehensively analyze their impact on customer purchase intention. It aims to help e-commerce companies understand how different types of AR experiences influence consumer behavior, thereby enabling them to optimize user experiences in a targeted manner. The study explores the role of perceived ease of use and perceived usefulness as mediating variables between AR online shopping experiences and purchase intention, revealing the intrinsic mechanisms through which AR experiences affect consumer purchase intention and providing a theoretical basis for optimizing AR applications. Therefore, this study extends the application scenarios of the Technology Acceptance Model. Based on the empirical research results, specific optimization suggestions are proposed to enhance customer purchase intention. These suggestions offer actionable strategies for e-commerce marketers and service providers, improving the market competitiveness of e-commerce platforms. By analyzing AR online shopping experiences, this study enriches the theory of consumer behavior in Metaverse e-commerce and provides new perspectives and methods for future e-commerce research in the Metaverse environment.

AR (Augmented Reality) and VR (Virtual Reality) are key gateways into the metaverse, serving as the intersection and overlay of virtual and real worlds. These two technologies differ in their technical aspects, devices used, application fields, advantages, and potential, as shown in Table 1 . AR, with its superior interactivity, real-time capabilities, visual effects, high portability, and ease of connectivity, demonstrates strong application value and development trends in areas such as e-commerce, shopping, marketing, advertising, social interaction, and entertainment.

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https://doi.org/10.1371/journal.pone.0309468.t001

2 Basic theories

2.1 ar online shopping.

Augmented Reality technology originated in the 1990s, but it was not until the early 21st century, with the widespread adoption of smartphones and high-speed internet, that this technology began to be applied in the online shopping sector. Retailers and tech companies invested in image recognition improvements and 3D modeling technologies to enable a more realistic product experience for consumers. One of the earliest applications was a virtual fitting room that allowed users to try on clothes via a web camera. In 2017, IKEA launched an AR app named “IKEA Place” that allowed users to virtually place furniture in their homes to see how it would look in a real environment. After 2020, the use of AR technology in online shopping became more widespread. Besides virtual try-ons, it was also used for home decor, cosmetics selection, and even in some high-tech stores for AR virtual shopping assistants (see Table 2 ). E-commerce giants like Amazon and Alibaba integrated AR technology into their shopping platforms, providing a richer and more interactive online shopping experience. For instance, Amazon’s AR View feature allows users to virtually view products in their own living spaces. In the future, augmented reality will further merge with technologies such as virtual reality and blockchain to create a comprehensive metaverse digital shopping environment, offering users a fully immersive and interactive shopping experience.

https://doi.org/10.1371/journal.pone.0309468.t002

Despite the rapid development of AR online shopping in practice, there is currently no unified definition. Summarizing existing theoretical research and practical developments, AR online shopping is an innovative shopping method that combines the convenience of online shopping with the experiential aspect of physical shopping. It overlays virtual information and images in the consumer’s actual environment and displays them in 3D. This allows consumers to perceive and interact with virtual elements in a more realistic and three-dimensional way in real-time, providing a richer and more immersive shopping experience. Consequently, consumers can more accurately understand the appearance and functions of products before purchasing, thereby enhancing shopping efficiency and satisfaction [ 13 , 14 ]. AR online shopping displays product elements in three-dimensional (3D) form and assimilates virtual objects into the physical reality, allowing users to experience the coexistence of real and virtual elements in the same space and interact with products in an enhanced manner [ 15 , 16 ]. AR interaction technology enables users to virtually try, verify, and inspect products from various angles and size [ 17 ], It responds instantly to user actions such as rotating, zooming, or altering products, and any changes made during user interactions are immediately reflected in the AR interface. This instant interaction enhances the dynamism and enjoyment of shopping [ 18 , 19 ], increases consumer engagement, and promotes sales in a heuristic and effective manner [ 20 ]. AR technology allows users to make personalized adjustments and trials according to their preferences and needs, turning customers into co-designers of the products they wish to purchase, thereby creating personalized products or customizing them in a personalized way [ 21 ].

Immersion refers to the degree to which an individual’s senses are cut off from the real world and replaced by a virtual simulation [ 16 ]. Initially a way for gamers to interact with their physical environment, immersive AR technology is now used to enhance e-commerce platforms through richer media experiences, simpler navigation, and the multidimensional and multisensory presentation of products [ 8 ], placing customers in a new immersive space. This allows users to navigate spatial locations via web browsers in an interactive simulated manner, experiencing the sensation of shopping in person at actual locations, thus creating a retail store shopping feel accessible from anywhere. This can evoke emotional, cognitive, and behavioral responses [ 9 ], enhancing enjoyment, perceived usefulness, and purchase intent. With rapid advancements in augmented reality technology, along with the swift development of VR, 5G/6G, Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communications (URLLC), Artificial Intelligence (AI), and blockchain technology, immersive 3D experiences and multisensory communications blur the lines between virtual and physical worlds to form a metaverse of mixed reality [ 22 ]. Within the context of the metaverse, AR, VR, and XR (Extended Reality) have all made significant progress, considered the next generation of the internet or social media, poised to revolutionize shopping and marketing [ 23 ].

2.2 Experiential marketing

Intense competition has made the functional attributes of products and services increasingly similar, making experience a key differentiator among businesses, especially in the retail environment. A core goal for businesses is to create outstanding customer experiences. Experiential marketing has become a standard practice for many merchants [ 24 ]. It goes beyond the transactional level of traditional marketing of products and services, focusing instead on creating emotional and sensory connections with consumers. The core of experiential marketing is to create a comprehensive consumption experience. Experiential marketing includes five main dimensions. Sensory Experience (SENSE): Sensory experience focuses on stimulating the consumer’s senses—sight, hearing, smell, touch, and taste. By creating an appealing visual environment, playing pleasing music, offering unique tastes and scents, or providing tactile experiences, businesses can enhance consumers’ product perceptions and memories. Emotional Experience (FEEL): Emotional experience aims to evoke consumers’ emotional responses and emotional connections. For example, businesses can touch consumers’ hearts through emotionally resonant advertising, storytelling, or user experiences. This type of experience might be based on joy, surprise, nostalgia, or other emotions, with the goal of establishing a deeper emotional connection with consumers. Creative Cognitive Experience (THINK): The creative cognitive experience encourages consumers to actively think, explore, and innovate. This type of experience often stimulates consumers’ curiosity and imagination by solving problems, offering novel perspectives, or introducing unfamiliar concepts. For example, consumers’ thinking and engagement may be stimulated through interactive exhibitions, educational workshops, or innovative product demonstrations. Physical Experience, Actions, and Lifestyle (ACT): Physical experience involves consumer behaviors and direct interactions with products or services, including using products, participating in activities, or adopting specific lifestyles. For example, experiential retail stores or interactive exhibitions encourage consumers to engage with and experience the brand’s lifestyle. Social Identity Experience (RELATE): The social identity experience emphasizes the relationships between consumers and others, and how they define their social identities through brands. This can be achieved through interactions on social media, community events, or associations with certain cultures or groups. For example, some brands incorporate specific cultural values or social movements, making consumers feel like part of a larger group [ 25 , 26 ]. In summary, experiential marketing creates a comprehensive and immersive consumer experience through these five dimensions, aiming to establish a deeper emotional connection between the brand and consumers [ 27 ].

In the field of online shopping, creating a unique online shopping experience has become key to attracting customers and maintaining customer loyalty. Experiential marketing in online shopping has now become a focus of attention for both academic researchers and practitioners. Key factors in building positive online experiences include vividness, interactivity, and uniqueness. However, achieving these objectives faces several challenges. On one hand, due to the complex cognitive structures of consumers, exploring the mechanisms behind consumer online buying behaviors is difficult. On the other hand, virtual experiences have certain limitations that directly impact customer purchasing behavior. Marketers should seek innovative methods to overcome these challenges, including the use of metaverse technologies such as augmented reality and virtual reality, enabling consumers to interact with virtual content in the real world and experience it in a holistic manner.

2.3 Technology Acceptance Model (TAM)

The Technology Acceptance Model (TAM), proposed by Davis in 1989 [ 28 ], has been a key theoretical framework widely used in the field of information systems since the late 1980s [ 29 ]. TAM aims to explain and predict user behavior in accepting and using new technologies [ 30 ]. The model suggests that an individual’s intention to use a technology is primarily determined by two main factors: Perceived Ease of Use (PEOU) and Perceived Usefulness (PU). Perceived Usefulness refers to the user’s belief that using a particular technology will enhance their job performance, meaning that a practical technology is more likely to be accepted and used by users [ 31 ]. Perceived Ease of Use refers to the user’s perception of how easy or difficult a technology is to use; if users believe a technology is easy to use, they are more likely to adopt it [ 32 ]. Perceived Usefulness is influenced by Perceived Ease of Use because, all other conditions being equal, a technology that is easier to use is more likely to be accepted.

Since its inception in 1991, the Technology Acceptance Model has generated over 1,000 related publications in the field of management, making it one of the most popular theoretical models [ 7 ]. TAM has also become an appropriate hypothesis model for studying the acceptance of AI technology in e-commerce [ 33 ]. Magsamen-Conrad et al. [ 34 ] have used Perceived Ease of Use to define the comfort level when using social networking platforms. Jacob and Pattusamy [ 35 ] have described how Perceived Usefulness indicates the extent to which using social networks can aid in sustaining people’s learning activities. If users believe that the technology behind an online shopping experience is useful and easy to use, they are more inclined to use that technology. However, the TAM model focuses only on the extrinsic motivations for technology use [ 23 ]. To enhance the explanatory power of TAM, researchers have expanded it by incorporating various external variables when using the model. This expansion allows studies to address the intrinsic motivations of users for using a particular technology. One such variable is Perceived Enjoyment, meaning if consumers enjoy the online shopping experience, they are likely to have a positive attitude towards the specific technology [ 36 ]. Another expansion of TAM involves the introduction of the trust factor, especially in e-commerce and online environments, where perceived trust is considered a key factor influencing user acceptance and use of new technologies. The inclusion of trust has enhanced the model’s accuracy in predicting user behavior [ 37 ]. Some scholars have also introduced subjective norms and external regulations, Research by Wang et al. [ 33 ] found that in the use of AI technology in e-commerce, subjective norms positively influence perceived usefulness and perceived ease of use, and trust has a positive effect on perceived usefulness. Pan et al. [ 23 ] studied how TAM-related factors influence two types of usage behaviors on current metaverse platforms. The driving forces for using popular metaverses are perceived usefulness and subjective norms, while the adoption of emerging metaverses is significantly influenced by perceived enjoyment and external regulations. In 2003, Venkatesh et al. [ 38 ] proposed the Unified Theory of Acceptance and Use of Technology (UTAUT) model, which adds “facilitating conditions” that influence users’ intentions to accept and use technology as well as actual usage behavior, helping researchers and practitioners better understand the process of technology acceptance.

3 Research hypotheses and model construction

3.1 research hypotheses, 3.1.1 antecedent variables..

The most prominent issue people face when shopping online is still the lack of physical contact with products and insufficient information about them. Online shopping cannot provide the immediate experience and trial opportunities that physical stores offer. The product images, descriptions, and even videos in online shopping may significantly differ from the actual goods received [ 39 ], leading to consumer disappointment and the choice to leave. The ideal solution to this problem is to provide a virtual product experience on consumers’ own shopping devices. AR technology overlays digital information on real-world visual elements, seamlessly integrating virtual products into consumers’ real environments. This not only allows consumers to browse products in entirely new ways but also offers a more personalized and interactive shopping experience, enabling people to have an “immersive” shopping experience without actual contact with the product [ 14 ]. With the widespread use of mobile devices such as smartphones and tablets, the application of AR technology in online shopping has become increasingly convenient and popular, changing the way people shop [ 40 ]. For instance, virtual try-ons or trials, which are very popular in the fashion and retail industries, allow consumers to virtually try on clothes or shoes, or test various cosmetics on their faces to preview effects before purchasing. They can even try out furniture and decorative items at home, to better understand how these products would look in actual use [ 11 , 41 ]. AR technology enables interactive product displays, allowing consumers to view 3D models of products through AR apps on their smartphones or tablets, understand products from different angles, zoom in on details, and even observe different configurations and colors of the product [ 42 ]. Moreover, AR can create an exciting, enjoyable, and fun atmosphere, providing users with a gamified shopping experience. For example, customers can participate in virtual treasure hunts, searching for specific virtual items in the store to receive discounts or rewards [ 43 ]. With these innovative features, AR enhances consumer engagement in online shopping. The AR experience significantly impacts perceived ease of use, leading us to propose the hypothesis:

• H1: Sensory experience has a significant impact on perceived ease of use
• H2: Emotional experience has a significant impact on perceived ease of use
• H3: Cognitive experience has a significant impact on perceived ease of use
• H4: Action experience has a significant impact on perceived ease of use
• H5: Relational experience has a significant impact on perceived ease of use

Although existing literature has covered various aspects of AR technology, including its applications in fields such as education, healthcare, and entertainment, there has been limited in-depth discussion on its impact in the e-commerce sector, especially in terms of how AR technology influences consumer purchase intentions. Compared to traditional online product displays, AR offers better immersion, novelty, and enjoyment [ 43 ], and it has a significantly positive impact on consumers’ online purchase intentions by enhancing user experience [ 44 ]. Uhm et al. [ 10 ] have further confirmed that augmented reality will improve consumers’ diagnostic perceptions, psychological distance, risk perception, and purchase intentions in e-commerce products, but to varying degrees, with greater impacts on diagnostic perceptions and purchase intentions. Xu et al. [ 3 ] identified key AR features in the e-commerce environment and analyzed their effectiveness in helping consumers understand products deeply and creating an engaging atmosphere for customers. Immersive overlays, creative scenarios, and digital twins are important developmental pathways for the e-commerce metaverse [ 45 ]. We propose the hypothesis that the AR online shopping experience has a significant impact on perceived usefulness:

• H6: Sensory experience has a significant impact on perceived usefulness
• H7: Emotional experience has a significant impact on perceived usefulness
• H8: Cognitive experience has a significant impact on perceived usefulness
• H9: Behavioral experience has a significant impact on perceived usefulness
• H10: Relational experience has a significant impact on perceived usefulness

3.1.2 Mediating variables.

By integrating AR-based product displays into e-commerce channels, a key goal in the evolution of AR applications in e-commerce is to define and create platforms that merge the physical world of reality with the virtual world of products or services, forming an augmented reality environment. This allows users to overlay and interact with virtual objects within their real-life surroundings, obtain relevant information, engage in creating personalized products, and enhance the shopping experience [ 13 ]. Therefore, the characteristics of AR online shopping are reflected in three aspects: vividness, interactivity, and immersion. In the context of e-commerce, vividness is often interpreted as the quality of product presentation [ 46 ]. Wang [ 47 ] studied the impact of information-oriented and entertainment-oriented smart shopping experiences on consumer purchase intentions. AR technology integrates sensory virtual digital content such as sound, video, graphics, and images, projecting holographic three-dimensional images of products into the surrounding real-world environment in a vivid and novel way [ 48 , 49 ]. It displays multi-dimensional elements of products, delivering higher quality visual, auditory, and tactile stimuli to media users. This enhances the perceived information quality, expands the number of sensory dimensions a user can experience, and allows users to perceive and interact with virtual elements in a more realistic and three-dimensional manner [ 50 ]. Consequently, users can psychologically pre-experience product experiences in future consumption environments, assess the suitability of the products, enhance confidence in their purchasing decisions, and form more enduring memories of the information [ 45 ]. Therefore, the following hypotheses are proposed:

• H11: Perceived ease of use has a significant impact on purchase intentions
• H12: Perceived usefulness has a significant impact on purchase intentions

3.2 Model construction

Combining the Technology Acceptance Model (TAM), the AR shopping experience incorporates sensory experience, emotional experience, cognitive experience, behavioral experience, and relational experience as antecedent variables. Perceived ease of use and perceived usefulness are treated as mediating variables, and purchase intention as the dependent variable. We construct a theoretical model on the impact of AR online shopping experience on customer purchase intentions, as shown in Fig 1 .

https://doi.org/10.1371/journal.pone.0309468.g001

3.3 Variable measurement

Based on a thorough consideration of related research and practical developments in AR online shopping, a model has been developed to examine the impact of the AR shopping experience on customer purchase intentions. This model consists of eight latent variables (sensory experience, emotional experience, cognitive experience, behavioral experience, relational experience, perceived ease of use, perceived usefulness, and purchase intention) and 30 measurement variables, as seen in Table 3 . Each item is measured using a 5-point Likert scale, where 1, 2, 3, 4, and 5 represent “strongly disagree,” “disagree,” “neutral,” “agree,” and “strongly agree,” respectively, allowing respondents to make effective perceptual judgments. The development of the items referred to established scales used in expert and scholarly research and was adjusted according to the characteristics of AR e-commerce shopping, ensuring the accuracy and reliability of the scales.

https://doi.org/10.1371/journal.pone.0309468.t003

4 Empirical study

4.1 reliability and validity analysis.

Based on the measurement items for the relevant variables, a survey questionnaire was developed. The questionnaire focuses on the experiences and evaluations of consumers who have shopped using AR online. The survey was conducted online, targeting AR online shopping consumers, and 279 questionnaires were collected. After discarding invalid questionnaires, 202 valid responses were retained. The standardized Cronbach’s alpha coefficients of the samples are all greater than 0.8, indicating a high level of reliability for the entire survey questionnaire. This suggests that the survey questionnaire is both reliable and stable. Therefore, it is necessary to maintain the measurement items for sensory experience, emotional experience, cognitive experience, behavioral experience, relational experience, perceived ease of use, perceived usefulness, and purchase intention. The Kaiser-Meyer-Olkin (KMO) test statistic is primarily used to compare the simple correlations and partial correlations among variables. When the sum of squares of all simple correlations among variables is significantly greater than the sum of squares of partial correlations, the KMO value approaches 1. The closer the KMO value is to 1, the stronger the correlation among the variables, and the more suitable they are for factor analysis. The KMO values for all variables are not less than 0.7, indicating that factor analysis can be conducted. The Average Variance Extracted (AVE) can test the internal consistency within structural variables. When the AVE value is greater than 0.50, it indicates that the latent variable has good measurement validity. The AVE values for all variables in the table are greater than 0.7, indicating that the validity of the survey questionnaire meets the requirements, as shown in Table 4 .

https://doi.org/10.1371/journal.pone.0309468.t004

The square roots of the AVE for each variable are greater than their correlation coefficients with other variables in the same column, indicating that the measurement scale has good discriminant validity, as shown in Table 5 .

https://doi.org/10.1371/journal.pone.0309468.t005

4.2 Estimation of structural equation model

Using AMOS 22 software to fit the structural equation model, the initial structural equation model yielded T-values of -4.779 for “TE→PEOU” and -4.526 for “RE→PEOU,” which do not meet the standard of T-values > 1.96. After removing the two non-significant paths “TE→PEOU” and “RE→PEOU,” the model was refitted, resulting in the revised structural equation model and path coefficients as shown in Fig 2 .

Note: *** indicates that the significance (sig) value is less than 0.001.

https://doi.org/10.1371/journal.pone.0309468.g002

In the revised model’s path coefficient test results, all path T-values exceeded the minimum standard of 1.96, and all p-values were significant at the 0.001 level. Overall, the path coefficients in the revised model are quite significant. From the perspective of various fit indices, the structural equation model has a χ2/df value of 4.601, which is less than 10; GFI value of 0.810, close to 1; AGFI value of 0.742, close to 1; RMSEA value of 0.034, less than 0.05; and NFI, CFI, and IFI values are 0.708, 0.754, and 0.756, respectively, all close to 1, as shown in Table 6 . These results indicate that the model fits well and has good adaptability, and the model should be accepted.

https://doi.org/10.1371/journal.pone.0309468.t006

Using the Bootstrap method to test for mediating effects, the sample was bootstrapped 5000 times with replacement at a 95% confidence level, and the results indicate the presence of mediating effects, as shown in Table 7 .

https://doi.org/10.1371/journal.pone.0309468.t007

4.3 Hypothesis testing

The study demonstrates significant effects of sensory experience (SE) on perceived ease of use (PEOU), emotional experience (EE) on PEOU, behavioral experience (AE) on PEOU, sensory experience on perceived usefulness (PU), emotional experience on PU, cognitive experience (TE) on PU, behavioral experience on PU, and relational experience (RE) on PU. Additionally, PEOU on purchase intention (PI) and PU on PI are significantly impacted. However, the hypotheses that relational experience significantly affects PEOU and that cognitive experience significantly affects PEOU are not supported, as shown in Table 8 .

https://doi.org/10.1371/journal.pone.0309468.t008

Among all the effects, the impact of PU on PI is the greatest, with a coefficient of 1.010; followed by the impact of RE on PI, with a coefficient of 0.611; the third highest is the impact of AE on PEOU, with a coefficient of 0.598; the fourth is the impact of AE on PI, with a coefficient of 0.563; the smallest impact is from PEOU on PI, with a coefficient of 0.020, as detailed in Fig 3 .

https://doi.org/10.1371/journal.pone.0309468.g003

5 Discussion

5.1 conclusions.

This study extends the Technology Acceptance Model (TAM) by incorporating five types of AR online shopping experiences (sensory experience, emotional experience, cognitive experience, action experience, and relational experience) as antecedent variables, with perceived ease of use (PEOU) and perceived usefulness (PU) as mediating variables. A structural equation model was constructed and empirically tested to explore the impact mechanisms of AR online shopping experiences on customer purchase intention. The main findings are as follows:

• Positive Impact of Sensory Experience: The vividness, interactivity, and immersive sensory experience of AR enhance the perceived ease of use and perceived usefulness of online shopping for consumers.
• Role of Emotional Experience: The positive emotions triggered by AR improve consumers’ perceptions of the usability and usefulness of online platforms, increasing their shopping pleasure and utility. This supports the findings of studies [ 3 , 8 , 41 ].
• Impact of Cognitive Experience: Cognitive experience significantly influences perceived usefulness but does not affect perceived ease of use. This indicates that the comprehensive and detailed understanding of product information, the vivid presentation of matching effects, the display of post-purchase usage scenarios, interaction with products, matching of related products and scenes, and enhancement of guidance functions provided by AR are very valuable and practical for providing information and aiding decision-making. However, in actual operation, consumers may still find using AR technology somewhat complex, and thus it does not significantly simplify the shopping process or improve shopping efficiency.
• Contribution of Action and Relational Experiences: Action and relational experiences enhance shopping experiences and social interactions, leading to stronger purchase intentions among consumers. Although relational experience does not significantly affect perceived ease of use, it enhances consumers’ sense of social recognition, consistent with the conclusion in the literature [ 11 ] that AR stimulates purchase intentions in shopping environments.
• Mediating Role of Perceived Ease of Use and Perceived Usefulness: Both perceived ease of use and perceived usefulness significantly influence purchase intention, with the impact of perceived usefulness being the greatest. This indicates that although ease of use contributes to an improved shopping experience, it does not significantly drive purchase decisions unless it provides substantial practical utility. This finding aligns with the conclusion in the literature [ 5 ] about the mediating role of perceived value in AR usage motivation and purchase intention.

In summary, this study expands the application scope of the Technology Acceptance Model (TAM), providing new insights into how different types of AR experiences influence consumer behavior. It reveals the multiple impact mechanisms of AR online shopping experiences on customer purchase intention, enriching the theory of consumer behavior in Metaverse e-commerce.

5.2 Recommendations

5.2.1 enhancing scenario construction to empower ar online shopping experience..

The creation of AR scenarios is a crucial step in enhancing the online shopping experience and boosting purchasing intentions. Empowering AR scenarios includes two major aspects. First is the diversification of scenario construction. Currently, AR online shopping scenarios are mainly focused on product demonstrations. Further development needs to create more diversified scenarios, including the integration of AR technology in the production of raw materials, product manufacturing, warehousing and transportation, customer service, and live commerce, allowing consumers to have a more direct and enhanced experience of the entire supply chain. Second is the enrichment of interactive development. Current interactions focus on gesture recognition, but there is a need to further develop technologies such as spatial positioning, eye-tracking, facial recognition, full-body tracking, and random interaction in AR shopping to more accurately determine the shopping space, analyze consumer emotions, display full-body effects and overall environmental effects, and enhance the level of interaction. AR scenario empowerment can simultaneously enhance the five major experiences of AR shopping and positively impact consumers’ online shopping intentions in terms of product discovery, leisure and entertainment, enhanced immersion, improved usefulness and ease of use, promotion of communication, development of word-of-mouth, strengthening brand consolidation, and facilitating commercial conversion, as shown in Fig 4 .

https://doi.org/10.1371/journal.pone.0309468.g004

5.2.2 Enhancing sensory experience in AR online shopping.

Utilize high-definition images and advanced rendering technologies to create realistic 3D models, enhancing the detail and authenticity of products displayed in an AR environment. Incorporate unique visual effects, such as dynamic lighting and shadows or adding interactive elements, to make the user experience more engaging and memorable. Develop a variety of AR application scenarios, allowing users to experience the effects of products in their own environment. Guide users through AR games or interactive tutorials to learn about products, providing an educational and entertaining shopping experience. Design personalized shopping paths that allow users to explore actively within the AR environment based on their interests and shopping habits. Offer unique AR product trial experiences that include multisensory elements, such as simulating the texture and color changes of products, and even providing olfactory and gustatory stimuli, enabling users to feel the products more genuinely and ensuring that the AR trial features align closely with the actual quality and characteristics of the products. Provide highly customized experiences, allowing users to adjust the product’s color, size, or design to suit their personal preferences.

5.2.3 Enhancing the emotional experience in AR online shopping.

Design beautiful, vivid, and attractive AR interfaces to create compelling immersive effects. Analyze user preferences scientifically based on their shopping history data and recommend customized AR product displays. Provide diverse AR display options, such as 3D views and 360-degree rotations, allowing users to explore products from multiple angles and details. Integrate emotional elements into the products, such as using AR to display the product’s story or origins and employing narrative techniques to present products, allowing users to enjoy the storyline while exploring the product, enhancing the emotional connection between users and products. Merge metaverse and virtual reality technologies to create a new shopping environment that transcends traditional online shopping, enabling users to experience products in a novel and fully immersive way. Offer lively and interactive experiences, such as incorporating gamified elements, where users can earn discounts or points by completing small tasks within the AR experience; allow users to customize or experiment with products using AR technology.

5.2.4 Enhancing the cognitive experience in AR online shopping.

Utilize AR technology to provide detailed and comprehensive product information, including 3D models that show every angle and detail of the product; incorporate enhanced description features that automatically display related product specifications, materials, or usage methods when users view specific parts. Offer additional information related to the product, such as customer reviews, production background, and usage scenarios, enabling users to fully understand the product information. Visually demonstrate pairing effects, using AR technology for virtual try-ons or home setups, allowing users to see the product pairing effects intuitively. Provide diverse pairing options and suggestions to help users explore different styles or design proposals. Allow users to freely mix and match different products in a virtual environment, increasing space for experimentation and innovation, and better imagine scenarios post-purchase. Create realistic post-purchase usage scenario simulations, such as allowing users to see the product’s effect in their own home or anticipated usage environment. Integrate emotional elements, such as simulating users’ feelings or life improvement effects after using the product, enhancing emotional resonance. Transform perceptions of traditional e-commerce, emphasizing the unique value provided by AR shopping, such as higher interactivity and more accurate product experiences. Educate and guide users to understand the advantages of AR shopping, such as accuracy, convenience, and personalized experiences. Analyze new insights and feelings gained by users through AR shopping, and how this influences their shopping decision process.

5.2.5 Optimizing the behavioral experience in AR online shopping.

Enhance shopping convenience by developing intuitive and user-friendly AR application interfaces, ensuring that users of all ages and technical levels can easily utilize them. Simplify the shopping process through AR technology, such as implementing one-click shopping, allowing users to directly select and purchase products within the AR experience. Provide efficient product search and filtering tools, enabling users to quickly find the AR experience products they need. Offer dynamic pairing suggestions to help users choose the right product combinations based on their personal style and occasion needs. Enable users to experience the effects of different product combinations at home through virtual try-on and pairing features, reducing the hassles of purchase errors and returns. Shift traditional shopping habits by emphasizing the advantages of AR shopping over traditional flat webpage shopping, such as more realistic product previews and higher interactivity. Educate users on how to effectively use AR technology for shopping, helping them adapt to this new mode of shopping through case demonstrations or tutorials. Encourage merchants to incorporate AR experiences into product displays, enhancing users’ affinity for AR-capable products by providing richer and more in-depth product information, and attracting users with more vivid and immersive shopping experiences. Collect user feedback and continuously improve the AR shopping experience to ensure it meets user needs and exceeds expectations.

5.2.6 Enhancing the relational experience in AR online shopping.

Utilize AR technology to provide direct interaction with products, such as allowing users to rotate, zoom in, and zoom out on product models via gestures or touch, and even try on or test products. Create an interactive virtual environment, for example, by simulating actual usage scenarios, allowing users to experience products in novel ways. Develop AR tools that enable users to virtually place products in their own environments to assess their adaptability and aesthetic fit. Offer virtual pairing suggestions, such as automatically displaying other items that complement the selected product or suggested pairing methods. Facilitate user interaction with the pairing scenario, such as adjusting the lighting or background in the scene to better display the product effects. Act as a shopping guide by using AR technology to provide personalized shopping suggestions, such as recommending products based on a user’s shopping history and preferences. Integrate chatbots or virtual shopping assistants to provide real-time answers and advice, enhancing the interactivity and helpfulness of the shopping experience.

6 Limitations and future directions

The limitations of this study are primarily reflected in the data collection process. The quantitative data used for the structural equation modeling (SEM) analysis were obtained through a cross-sectional survey conducted in China. The sample was concentrated on specific demographic characteristics or geographic regions. The cross-sectional study design only captures data at a single point in time, failing to reveal the long-term impact of AR online shopping experiences on purchase intention, thus limiting the generalizability of the study results. Future research should consider more diverse samples to validate the applicability of the findings across different populations and regions. Additionally, studies should adopt longitudinal designs to track changes in consumer behavior before and after using AR technology, to understand the long-term impact of AR shopping experiences on purchase intention and potential behavior changes. Secondly, this study primarily focused on the impact of AR technology on online shopping experiences and customer purchase intention. Future research could further explore the impact of combining AR and artificial intelligence technologies on e-commerce customer online shopping, as well as investigate the patterns of consumer behavior in the metaverse e-commerce environment.

Supporting information

S1 data. raw data and the means, standard deviations, variances, minimum, and maximum values of the raw data..

https://doi.org/10.1371/journal.pone.0309468.s001

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Research and evaluation of multi-sensory design of product packaging based on vr technology in online shopping environment.

1. Introduction

2. related work, 3.1. ahp-based model for multi-sensory evaluation in virtual packaging, 3.2. hierarchical modeling, 3.2.1. eye-tracker experimentation, 3.2.2. evaluation modeling, 3.3. construction of judgment matrices and consistency tests, 3.3.1. construction of judgment matrices, 3.3.2. relative weight and consistency test, 3.4. vr shopping model construction, 3.4.1. key technologies, 3.4.2. vr shopping model development practice, 4. results and discussion, 4.1. vr shopping model evaluation, 4.2. vr shopping model improvement, 4.2.1. improvement program, 4.2.2. improved validation, 4.2.3. statistical analysis, 5. conclusions, supplementary materials, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Xiao, Y.; Li, Q.; Zhang, Z.; Zhang, Y. Research and Evaluation of Multi-Sensory Design of Product Packaging Based on VR Technology in Online Shopping Environment. Appl. Sci. 2024 , 14 , 7736. https://doi.org/10.3390/app14177736

Xiao Y, Li Q, Zhang Z, Zhang Y. Research and Evaluation of Multi-Sensory Design of Product Packaging Based on VR Technology in Online Shopping Environment. Applied Sciences . 2024; 14(17):7736. https://doi.org/10.3390/app14177736

Xiao, Yingzhe, Qianxi Li, Zhen Zhang, and Yanyue Zhang. 2024. "Research and Evaluation of Multi-Sensory Design of Product Packaging Based on VR Technology in Online Shopping Environment" Applied Sciences 14, no. 17: 7736. https://doi.org/10.3390/app14177736

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• Sensory Toys Market

Region : Global | Format: PDF | Report ID: BRI115328 | SKU ID: 24070363

Sensory Toys Market Size, Share, Growth, And Industry Analysis by Type (Chew Toys, Tactile Toys, Hearing Toys, Sensory Walls and Sensory Tables & Others) by Application (School, Clinic, Family & Others) and Regional Insights and Forecast to 2032

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Pharmacy benefit management market

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SENSORY TOYS MARKET REPORT OVERVIEW

The global sensory toys market size expanded rapidly in 2022 and will grow substantially by 2029, exhibiting a prodigious CAGR during the forecast period.

Sensory toys are specialised merchandise designed to interact one or more of the 5 senses—touch, sight, hearing, taste, and smell—to assist sensory improvement and therapeutic wishes. These toys include more than a few gadgets including fidget spinners, textured balls, chewable gadgets, and sensory packing containers. They are utilized in academic settings to useful resource kids with sensory processing problems and developmental delays via enhancing awareness, lowering anxiety, and enhancing sensory integration. In therapeutic environments, sensory toys help with motor skills and cognitive development. Their flexible programs cause them to precious in each clinical and home settings for enhancing sensory reports and supporting usual developmental progress.

The sensory toys market size is developing because of expanded consciousness and prognosis of sensory processing issues and developmental situations like autism. As educational establishments and therapists understand the benefits of sensory toys for enhancing sensory integration and developmental outcomes, demand rises. Additionally, heightened client focus approximately the importance of sensory play for all children, not simply people with precise wishes, contributes to market boom. The trend towards inclusive schooling and healing play similarly fuels this demand, as households and schools are searching for effective tools to support sensory development and beautify getting to know reviews, driving growth within the sensory toys marketplace.

COVID-19 Impact: Pandemic Led Delivery Chain Disruptions And Lockdowns Caused Delays And Shortages Of Sensory Toys

The COVID-19 pandemic has been unprecedented and staggering, with sensory toys market experiencing higher-than-anticipated demand across all regions compared to pre-pandemic levels. The sudden rise in CAGR is attributable to the market’s growth and demand returning to pre-pandemic levels once the pandemic is over.

The pandemic impacted the market in several methods. Initially, delivery chain disruptions and lockdowns caused delays and shortages of sensory toys. However, the pandemic also accelerated the focal point on domestic-based totally learning and remedy, boosting demand for sensory toys as households sought resources to help children's sensory wishes at some point of extended periods at home. The upward push in faraway gaining knowledge of and remedy additionally led to extra use of sensory toys in digital and domestic-based totally settings. Additionally, heightened cognizance of mental fitness and developmental support for the duration of the pandemic drove interest in sensory toys. As an end result, whilst the market confronted demanding situations, it skilled increased demand and increase because of the evolving needs of households and educators. 

LATEST TRENDS

" Developing Demand For Efficient And Sustainable Merchandise Key Trend In Market "

A top notch trend in the marketplace is the developing call for efficient and sustainable merchandise. As consumers end up more environmentally aware, groups are launching sensory toys crafted from natural, biodegradable, or recycled substances. For example, toys crafted from organic cotton, sustainably sourced timber, and non-poisonous, plant-based totally dyes are gaining traction. Leading players like PlanToys and Green Toys are at the vanguard of this fashion, introducing new products that align with environmental sustainability whilst nonetheless supplying the sensory blessings wanted for baby improvement. Additionally, improvements in era have caused the improvement of multi-sensory toys incorporating factors like light, sound, and vibration, offering improved sensory experiences to satisfy various healing and academic needs. 

SENSORY TOYS MARKET SEGMENTATION

Depending on sensory toys market given are types: Chew Toys, Tactile Toys, Hearing Toys, Sensory Walls and Sensory Tables & Others. The Chew Toys type will capture the maximum market share through 2030.  

• Chew Toys: Chew toys are designed for oral sensory stimulation, supporting youngsters manipulate tension and sensory processing desires. Made from secure, long-lasting substances, they're especially useful for those who are trying to find regular oral input.
• Tactile Toys: Tactile toys offer various textures to stimulate the feel of contact. These include items like textured balls and sensory mats. They resource in sensory integration and satisfactory motor talent improvement, making them valuable for kids with sensory processing troubles. Their extensive utility in remedy and play drives their giant market presence.
• Hearing Toys: Hearing toys produce sounds or song, attractive the auditory senses. Examples consist of musical devices and sound-making devices. These toys assist develop auditory processing abilities and language improvement. Although essential, their marketplace share is smaller compared to chunk toys because of their extra precise sensory consciousness.
• Sensory Walls: Sensory partitions feature interactive panels with distinctive textures and sensory factors. They are used in instructional and therapeutic settings to have interaction multiple senses and aid sensory integration remedy. Their versatility and immersive enjoy make them popular in specialized environments, although their utility is greater niche.
• Sensory Tables: Sensory tables are designed to keep substances like sand, water, or rice, making an allowance for sensory exploration. They are usually used in educational settings to promote creativity and sensory improvement. Their adaptability for various sensory sports and simplicity of use makes a contribution to their developing recognition within the market.
• Others: The "Others" segment encompasses numerous non-traditional packages of sensory toys, consisting of in network facilities, daycare centers, and recreational therapy. While these packages make contributions to market variety, they constitute a smaller part of the market. This segment is developing gradually; however its effect is greater area of interest compared to faculties and clinics.

By Application

The market is divided into School, Clinic, Family & Others based on application. The global sensory toys market players in cover segment like School will dominate the market share during 2023-2030.

• School: The school phase is expected to dominate the sensory toys market from 2023 to 2030 because of the growing integration of these toys into educational curricula. Schools are increasingly using sensory toys to assist college students with sensory processing disorders, autism, and other developmental wishes, selling inclusive studying environments and improving student engagement.
• Clinic: The hospital phase includes the use of sensory toys in therapeutic environments, along with occupational and speech therapy. These toys are important for diagnosing and treating sensory processing problems, assisting therapists increase tailor-made interventions. Their application in clinics is critical for developmental guide, however the marketplace proportion is smaller as compared to faculties.
• Family: The segment includes the use of sensory toys at domestic to guide kid's sensory development and manage sensory processing challenges. Parents are increasingly more purchasing these toys for home-primarily based remedy and sensory play, especially as consciousness grows about the benefits of sensory integration. This section is growing progressively however stays secondary to faculties.
• Others: The "Others" section encompasses various applications of sensory toys in non-traditional settings, along with community centers, daycare centers, and recreational therapy. These toys are used to aid a extensive variety of sensory reviews and healing sports, contributing to market variety. Although vital, this section represents a smaller portion of the general marketplace.

DRIVING FACTORS

" Increasing Recognition of The Significance of Sensory Integration Driving The Market Growth "

A principal riding component for the increase of the sensory toys market is the increasing recognition of the significance of sensory integration in baby improvement and healing practices. As focus grows about sensory processing disorders and developmental situations such as autism, there may be a rising call for specialized toys that address those needs. Educational institutions and therapists are integrating sensory toys into their applications to decorate mastering and healing effects. This heightened cognizance is riding dad and mom and caregivers to searching for effective tools to aid sensory improvement and improve universal well-being. The focus on inclusive education and personalized therapy in addition fuels the marketplace’s expansion.

" Remote And Domestic Mastering Environments Driving Sensory Toys Market Growth "

Another driving factor for the sensory toys market growth is the surge in remote and domestic mastering environments. The COVID-19 pandemic expanded this shift, main to expanded use of sensory toys in home settings to guide youngsters’ sensory wishes and improvement. Families are investing in these toys to enhance their kids play studies, control sensory processing challenges, and facilitate effective learning at domestic. This fashion is supported with the aid of the developing availability of sensory toys through online retail channels, which makes it less complicated for parents to get admission to this merchandise. The enlargement of domestic-primarily based education and remedy drives sustained demand for sensory toys, contributing to the marketplace's growth.

RESTRAINING FACTORS

" Excessive Fee of Specialised Toys Key Restraint in Market "

A tremendous restraining issue for the sensory toys market is the excessive fee of specialised toys. Sensory toys are frequently extra high priced than everyday toys due to their unique design, substances, and therapeutic blessings. This can limit accessibility, in particular for families with decrease incomes or in areas with limited healthcare guide. Additionally, the lack of understanding or knowledge of the blessings of sensory toys in some communities may also reduce demand. Furthermore, price range constraints in academic and medical settings can also restrict the considerable adoption of those products, slowing down market boom notwithstanding the increasing consciousness in their importance.

SENSORY TOYS MARKET REGIONAL INSIGHTS

" North America Dominating the Market Driven by Superior Healthcare Infrastructure "

The market is primarily segregated into Europe, Latin America, Asia Pacific, North America and Middle East & Africa.

North America is the main vicinity within the sensory toys market share, pushed by using its superior healthcare infrastructure, high awareness of sensory processing problems, and strong emphasis on inclusive education. The location's dominance is in addition supported via the massive availability of sensory toys through each physical shops and online systems. Additionally, the growing wide variety of youngsters recognized with autism and different developmental issues has elevated the demand for sensory toys in colleges, clinics, and houses. North America's consciousness on early life improvement and therapeutic interventions, along-side the presence of key marketplace players, positions it as a vital hub for innovation and increase in the sensory toys marketplace.

KEY INDUSTRY PLAYERS

" Key Players Focus on Partnerships to Gain a Competitive Advantage "

The sensory toys market is significantly influenced by key industry players that play a pivotal role in driving market dynamics and shaping consumer preferences. These key players possess extensive retail networks and online platforms, providing consumers with easy access to a wide variety of wardrobe options. Their strong global presence and brand recognition have contributed to increased consumer trust and loyalty, driving product adoption. Moreover, these industry giants continually invest in research and development, introducing innovative designs, materials, and smart features in cloth wardrobes, catering to evolving consumer needs and preferences. The collective efforts of these major players significantly impact the competitive landscape and future trajectory of the market.

List of Market Players Profiled

• Lego Group (Denmark)
• Mattel (U.S.)
• Hasbro (U.S.)
• Vtech (China)
• Spin Master (Canada)
• Ravensburger (Germany)
• ZURU Toys (China)
• Kids II (U.S.)
• Simba-Dickie Group (Germany)
• Chicco (Italy)
• Clementoni (Italy)
• Jazwares (U.S.)
• HABA Group (Germany)
• TAKARA TOMY (Japan)
• JUMBO (Netherlands)

INDUSTRIAL DEVELOPMENT

September 2021: Fat Brain Toys, a main organization inside the sensory toys market, released its new line of sensory-centered products known as "InnyBin." This progressive toy is designed to inspire exploration and tactile development in young youngsters by way of letting them push numerous textured shapes thru elastic bands into a cube. The InnyBin quickly gained recognition for its ability to stimulate sensory exploration and fine motor talents. This release displays Fat Brain Toys’ commitment to developing enticing, instructional toys that aid sensory development, aligning with the growing demand for merchandise that cater to children's sensory and developmental needs.

REPORT COVERAGE

The study encompasses a comprehensive SWOT analysis and provides insights into future developments within the market. It examines various factors that contribute to the growth of the market, exploring a wide range of market categories and potential applications that may impact its trajectory in the coming years. The analysis takes into account both current trends and historical turning points, providing a holistic understanding of the market's components and identifying potential areas for growth.

The research report delves into market segmentation, utilizing both qualitative and quantitative research methods to provide a thorough analysis. It also evaluates the impact of financial and strategic perspectives on the market. Furthermore, the report presents national and regional assessments, considering the dominant forces of supply and demand that influence market growth. The competitive landscape is meticulously detailed, including market shares of significant competitors. The report incorporates novel research methodologies and player strategies tailored for the anticipated timeframe. Overall, it offers valuable and comprehensive insights into the market dynamics in a formal and easily understandable manner. 

Frequently Asked Questions

The North America is the leading region in the sensory toys market.

The driving factors of the sensory toys market are increasing recognition of the significance of sensory integration and remote and domestic mastering environments.

The sensory toys market segmentation that you should be aware of, which include, based on type the sensory toys market is classified as Chew Toys, Tactile Toys, Hearing Toys, Sensory Walls and Sensory Tables & Others. Based on application the sensory toys market is classified as School, Clinic, Family & Others.

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