O2O Commerce and Consumer Behavior: Comparison
View Latest Version
Please note this is a comparison between Version 3 by Pinyi Yao and Version 2 by Beatrix Zheng.

Online-to-offline (O2O) commerce is a popular business model which links offline business activities with online channels. Consumer behavior in O2O commerce is more complex than in other traditional business models as both online and offline channels are involved.

  • O2O commerce
  • online-to-offline
  • consumer behavior

1. Introduction

In the past few decades, the spread of the Internet and the emergence of electronic commerce (e-commerce) or online shopping have changed the way consumers think and live in an unprecedented trend [1]. With the exponential growth of mobile devices (mainly smartphones) in the last decade, mobile commerce (m-commerce) has emerged, once again changing consumer behavior patterns and dramatically changing the landscape of traditional e-commerce [2]. It means that consumers can make purchases using their smartphones anytime, anywhere. Technology advancements have given rise to new business models and consumer-friendly services, such as mobile payments and online bookings [3]. Online-to-offline commerce (O2O commerce), which has recently been very popular, is one of those new business models. In O2O commerce, consumers typically make the purchase online and then consume the products or services offline [4][5]. To illustrate, consumers search, book, or pay online using a mobile app. They subsequently use location services to find and walk into the target brick-and-mortar store to consume. Alternatively, they receive products or services at home or at the workplace using instant delivery or door-to-door services.
O2O commerce combines online and offline channels, which means bringing online consumers into “real-world” stores [6] or using the online channel to drive offline sales [7]. For those brick-and-mortar businesses that have been impacted by e-commerce and feel left out, O2O commerce brings them new opportunities [8]. The enormous potential profit drives many local businesses or merchants into the O2O market [9]. Meanwhile, O2O commerce also brings great convenience to consumers. One of the most visible examples is the O2O food delivery services [10], which have been widely discussed, especially during the COVID-19 pandemic [11][12]. In addition, the “stay-at-home order” policy in the pandemic has prompted some traditional brick-and-mortar retailers to offer home delivery services through O2O platforms [13].
O2O commerce is growing and expanding rapidly along with the development of mobile Internet and information technology [14]. In addition, since the outbreak of the COVID-19 pandemic, more and more offline businesses are using O2O platforms to find their customers [13][15]. Although O2O commerce has been very popular and has shown to be a successful business model [8], its sustainability is unknown. The rapid expansion of O2O commerce has raised concerns that this business model may not be sustainable [16]. It is necessary to identify current trends in O2O commerce to inform the development of sustainable strategies and the implementation of sustainable management.
Due to the intense competition in the market, most O2O players tend to focus on increasing sales rather than on developing a sustainable relationship with consumers to maintain their business [17]. In the past few years, many O2O-related start-ups have failed, and one possible reason is that they did not closely observe consumer behavior [18]. In order to increase their survival chances, O2O businesses must retain existing customers and attract new ones by understanding consumer behavior to ensure the sustainability of their business. Digitization has extended to all stages of consumers’ purchases [3], making consumer behavior more complex than ever, especially in O2O commerce, as it simultaneously involves online and offline channels. It is worth acknowledging that because O2O commerce is a new and emerging business model, limited studies have attempted to understand these complex consumer behaviors.

2. What Is O2O Commerce?

Similar to other terms of e-commerce such as consumer-to-consumer (C2C) and business-to-consumer (B2C), online-to-offline (O2O) is a type of e-commerce business model. Rampell [6] first proposed the concept of O2O in 2010 and illustrated that the key to O2O is to find consumers online and bring them into offline channels. Tsai et al. [2] argued that O2O commerce provides a seamless purchasing experience between online and offline commerce by any connected device, while Xiao et al. [19] stated that O2O commerce brings offline business activities to online channels which are used to promote offline businesses. Some researchers have distinguished between online-to-offline and offline-to-online commerce [20][21][22]. Although the specific wordings differ, according to Ryu et al. [23], O2O commerce is an integration rather than a competition between online and offline channels, creating new values. In the past, O2O commerce attracted consumers with banner advertisements and digital coupons [7]. Nowadays, O2O commerce plays an essential role in different scenarios of consumers’ lives [12] and covers many types of local businesses, such as catering, ticketing, car-hailing, etc. [23].
Alternatively, O2O commerce can be viewed as an extension or upgrade of traditional e-commerce [19][24][25]. There are several differences between O2O commerce and traditional e-commerce. First, O2O commerce is location-based [2] and focuses on local retail and life service industries [19][26], such as restaurants, hotels, and entertainment. Second, the transactions in O2O commerce typically involve both online and offline channels [27][28]. Third, the features of O2O commerce make it difficult for consumers to return goods as easily as in traditional e-commerce [9][19]. Last, O2O commerce involves more participants, including consumers, offline stores, online platforms, and third-party service providers [29]. O2O commerce extends the scope of traditional e-commerce activities [30].
Business models always seem to change with the evolution of technology [2]. Many new types of O2O commerce are springing up, such as O2O clothing customization [31] and O2O community e-commerce [32]. There are many different scenarios in O2O commerce, but the two most apparent market segments in O2O industry practice, namely, “to-shop” and “to-home” [33][34], are rarely mentioned. The former refers to in-store consumption after paying or booking online. In contrast, the latter refers to receiving products or services at home or at the workplace through instant delivery or door-to-door services.

3. Consumer Behavior in O2O Commerce

Consumer behavior involves many things. It reflects the totality of consumers’ decisions in terms of “the acquisition, consumption, and disposition of goods, services, activities, experiences, people, and ideas by (human) decision-making units” [35]. Consumer behavior includes the consumers’ emotional, mental, and behavioral responses that precede, determine, or follow activities such as purchasing, using, and distributing goods and services [36] (p. 8). Although research has shown that consumer behavior is difficult to predict, it has always been an area of interest for scholars and marketers. Back in the 1960s and 1970s, Howard and Sheth [37] and Fishbein and Ajzen [38] proposed traditional models to explain consumer behavior. As e-commerce became popular, some researchers argued that online consumer behavior is different from offline behavior, and that new theories or models are required [39].
A review paper by Hwang and Jeong [40] discussed the factors affecting consumer behavior in e-commerce from the individual, website, and environmental dimensions and reported that many constructs had been used to study online consumer behavior. Technology acceptance and use behavior has been the subject of many classic studies in e-commerce. Haryanti and Subriadi’s [41] literature review showed popular theories and models in e-commerce research, namely TRA [38], TPB [42], TAM [43][44], UTAUT [45], and UTAUT2 [46] (see Table 1). They also found variables outside these theories and models, with trust and perceived risk being the most widely used. In addition, the information systems success model (ISSM) developed by DeLone and McLean [47] and the expectation–confirmation model (ECM) proposed by Bhattacherjee [48] have been used to explain consumers’ e-commerce adoption and use continuance behavior in many studies [49][50]. Table 1 shows exogenous variables from these theories and models that affect consumer behavior in e-commerce.
Table 1. Exogenous variables of popular theories and models in e-commerce literature.
Theories/Models Exogenous Variables (Factors)
Theory of reasoned action (TRA) Attitude
Subjective norm
Theory of planned behavior (TPB) Perceived behavioral control
Subjective norm
Technology acceptance model (TAM) Perceived usefulness
Perceived Ease of Use
Unified theory of acceptance and use of technology (UTAUT) Performance expectancy
Effort expectancy
Social influence
Facilitating conditions
The extension of the unified theory of acceptance and use of technology (UTAUT2) Performance expectancy
Effort expectancy
Social influence
Facilitating conditions
Hedonic motivation
Price value
Habit
Information systems success model (ISSM) Information quality
System quality
Service quality
Expectation–confirmation model (ECM) Perceived usefulness
Confirmation
However, when it comes to O2O commerce, the situation seems to get more complex as it includes both online and offline channels. Wang et al. [30] pointed out that free-riding and showrooming are typical consumer behaviors in the omnichannel market (i.e., the O2O market). Free-riding refers to consumers searching for information in one channel and purchasing in another [51][52]. Consumers usually compare different channels and choose the one with higher added value to buy products or services [53]. Showrooming refers to consumers selecting goods onffline and buying offnline [54], which reflects consumers’ pursuit of transaction cost minimization on the premise of ensuring product efficacy [55]. Additionally, compared with traditional e-commerce, O2O commerce involves more participants and technological innovation, making consumer behavior more complex. For instance, O2O transactions include activities such as online matchmaking, online payment, and offline consumption [29], as well as technologies such as location systems, near-field communication (NFC), and quick response (QR) codes [22].
Similar to traditional e-commerce, O2O commerce can be viewed by consumers as an innovative information technology service, hence the technology use literature is relevant for understanding consumer behavior related to O2O services [56]. Previous models or constructs have been used to explain consumer behavior in O2O commerce, being the most widely concerned with the TAM and service quality (e.g., [20][57]). However, discussions have been sporadic and limited as the factors influencing consumers’ O2O behavior have been loosely theorized. For instance, the food choice motives discussed in O2O food delivery [58] may not apply in other O2O scenarios. Furthermore, more evidence is needed to demonstrate that the theories and models applied in the prior e-commerce literature can explain consumer behavior in the context of O2O.

References

  1. Liang, D.; Dai, Z.; Wang, M. Assessing Customer Satisfaction of O2O Takeaway Based on Online Reviews by Integrating Fuzzy Comprehensive Evaluation with AHP and Probabilistic Linguistic Term Sets. Appl. Soft Comput. 2021, 98, 106847.
  2. Tsai, T.-M.; Wang, W.-N.; Lin, Y.-T.; Choub, S.-C. An O2O Commerce Service Framework and Its Effectiveness Analysis with Application to Proximity Commerce. Procedia Manuf. 2015, 3, 3498–3505.
  3. Liao, S.-H.; Yang, L.-L. Mobile Payment and Online to Offline Retail Business Models. J. Retail. Consum. Serv. 2020, 57, 102230.
  4. Xiao, S.; Dong, M. Hidden Semi-Markov Model-Based Reputation Management System for Online to Offline (O2O) e-Commerce Markets. Decis. Support Syst. 2015, 77, 87–99.
  5. Kang, J.-W.; Namkung, Y. The Information Quality and Source Credibility Matter in Customers’ Evaluation toward Food O2O Commerce. Int. J. Hosp. Manag. 2019, 78, 189–198.
  6. Rampell, A. Why Online2Offline Commerce Is a Trillion Dollar Opportunity. Available online: https://social.techcrunch.com/2010/08/07/why-online2offline-commerce-is-a-trillion-dollar-opportunity/ (accessed on 1 June 2022).
  7. Phang, C.W.; Tan, C.-H.; Sutanto, J.; Magagna, F.; Lu, X. Leveraging O2O Commerce for Product Promotion: An Empirical Investigation in Mainland China. IEEE Trans. Eng. Manag. 2014, 61, 623–632.
  8. Xiao, L.; Guo, Z.; D’Ambra, J. Benefit-Based O2O Commerce Segmentation: A Means-End Chain Approach. Electron. Commer. Res. 2019, 19, 409–449.
  9. Zhang, X.; Wang, T. Understanding Purchase Intention in O2O E-Commerce: The Effects of Trust Transfer and Online Contents. J. Theor. Appl. Electron. Commer. Res. 2021, 16, 125–139.
  10. Talwar, S.; Dhir, A.; Scuotto, V.; Kaur, P. Barriers and Paradoxical Recommendation Behaviour in Online to Offline (O2O) Services. A Convergent Mixed-Method Study. J. Bus. Res. 2021, 131, 25–39.
  11. Shah, A.M.; Yan, X.; Qayyum, A. Adoption of Mobile Food Ordering Apps for O2O Food Delivery Services during the COVID-19 Outbreak. Br. Food J. 2021.
  12. Yang, F.X.; Li, X.; Lau, V.M.-C.; Zhu, V.Z. To Survive or to Thrive? China’s Luxury Hotel Restaurants Entering O2O Food Delivery Platforms amid the COVID-19 Crisis. Int. J. Hosp. Manag. 2021, 94, 102855.
  13. He, B.; Mirchandani, P.; Shen, Q.; Yang, G. How Should Local Brick-and-Mortar Retailers Offer Delivery Service in a Pandemic World? Self-Building Vs. O2O Platform. Transp. Res. Part E Logist. Transp. Rev. 2021, 154, 102457.
  14. Kim, Y.; Wang, Q.; Roh, T. Do Information and Service Quality Affect Perceived Privacy Protection, Satisfaction, and Loyalty? Evidence from a Chinese O2O-Based Mobile Shopping Application. Telemat. Inform. 2021, 56, 101483.
  15. He, B.; Gupta, V.; Mirchandani, P. Online Selling through O2O Platform or on Your Own? Strategic Implications for Local Brick-and-Mortar Stores. Omega 2021, 103, 102424.
  16. Choi, Y.; Zhang, L.; Debbarma, J.; Lee, H. Sustainable Management of Online to Offline Delivery Apps for Consumers’ Reuse Intention: Focused on the Meituan Apps. Sustainability 2021, 13, 3593.
  17. Xiao, L.; Mi, C.; Zhang, Y.; Ma, J. Examining Consumers’ Behavioral Intention in O2O Commerce from a Relational Perspective: An Exploratory Study. Inf. Syst. Front. 2019, 21, 1045–1068.
  18. Huang, J.; Zhou, J.; Liao, G.; Mo, F.; Wang, H. Investigation of Chinese Students’ O2O Shopping through Multiple Devices. Comput. Hum. Behav. 2017, 75, 58–69.
  19. Xiao, L.; Zhang, Y.; Fu, B. Exploring the Moderators and Causal Process of Trust Transfer in Online-to-Offline Commerce. J. Bus. Res. 2019, 98, 214–226.
  20. Chen, C.-C.; Hsiao, K.-L.; Hsieh, C.-H. Understanding Usage Transfer Behavior of Two Way O2O Services. Comput. Hum. Behav. 2019, 100, 184–191.
  21. Cheong, F.; Law, R. Will Macau’s Restaurants Survive or Thrive after Entering the O2O Food Delivery Platform in the COVID-19 Pandemic? Int. J. Environ. Res. Public. Health 2022, 19, 5100.
  22. Moon, Y.; Armstrong, D.J. Service Quality Factors Affecting Customer Attitudes in Online-to-Offline Commerce. Inf. Syst. E-Bus. Manag. 2020, 18, 1–34.
  23. Ryu, M.H.; Kim, E.; Lee, S.Y. How Offline Retailers Adopt O2O: Neighboring Star Shops and Their Proximity Effect. Telecommun. Policy 2022, 46, 102278.
  24. Ji, J.; Zhang, Z.; Yang, L. Comparisons of Initial Carbon Allowance Allocation Rules in an O2O Retail Supply Chain with the Cap-and-Trade Regulation. Int. J. Prod. Econ. 2017, 187, 68–84.
  25. Wu, T.-J.; Zhao, R.-H.; Tzeng, S.-Y. An Empirical Research of Consumer Adoption Behavior on Catering Transformation to Mobile O2O. J. Interdiscip. Math. 2015, 18, 769–788.
  26. Hwang, S.; Kim, S. Does MIM Experience Affect Satisfaction with and Loyalty toward O2O Services? Comput. Hum. Behav. 2018, 82, 70–80.
  27. Huang, C.-C.; Chang, Y.-W.; Hsu, P.Y.; Prassida, G.F. A Cross-Country Investigation of Customer Transactions from Online to Offline Channels. Ind. Manag. Data Syst. 2020, 120, 2397–2422.
  28. Lin, M.; Wang, Z.; Zhang, Z.; Cao, Y. Research on Consumers’ Attitudes in China about Using Online-to-Offline Mode for Purchasing Wooden Furniture. For. Prod. J. 2019, 69, 159–172.
  29. Shen, C.-W.; Chen, M.; Wang, C.-C. Analyzing the Trend of O2O Commerce by Bilingual Text Mining on Social Media. Comput. Hum. Behav. 2019, 101, 474–483.
  30. Wang, C.; Wang, Y.; Wang, J.; Xiao, J.; Liu, J. Factors Influencing Consumers’ Purchase Decision-Making in O2O Business Model: Evidence from Consumers’ Overall Evaluation. J. Retail. Consum. Serv. 2021, 61, 102565.
  31. Li, H.; Gu, L.; Gu, W.; Liu, X. Research on Online-to-Offline Clothing Customization Mode Based on Consumer Perceived Value. Fangzhi Xuebao/J. Text. Res. 2020, 41, 128–135.
  32. Zhu, Y.; Wei, Y.; Zhou, Z.; Jiang, H. Consumers’ Continuous Use Intention of O2O E-Commerce Platform on Community: A Value Co-Creation Perspective. Sustainability 2022, 14, 1666.
  33. Statista. Gross Merchandise Value of the To-Shop O2O Market in China from 1st Half of 2016 to 1st Half of 2019. Available online: https://www.statista.com/statistics/1147378/china-gmv-of-to-shop-o2o-market/ (accessed on 1 June 2022).
  34. Statista. Gross Merchandise Value of To-Home O2O Market in China from 1st Half of 2016 to 1st Half of 2019. Available online: https://www.statista.com/statistics/1147402/china-gmv-of-to-home-o2o-market/ (accessed on 1 June 2022).
  35. Jacoby, J. Consumer Psychology: An Octennium. Annu. Rev. Psychol. 1976, 27, 331–358.
  36. Kardes, F.R.; Cronley, M.L.; Cline, T.W. Consumer Behavior; South-Western Cengage Learning: Mason, OH, USA, 2010; ISBN 978-0-538-74540-6.
  37. Howard, J.A.; Sheth, J.N. The Theory of Buyer Behavior; Wiley: New York, NY, USA, 1969; ISBN 978-0-471-41657-9.
  38. Fishbein, M.; Ajzen, I. Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research; Addison-Wesley Series in Social Psychology; Addison-Wesley: Boston, MA, USA, 1975; ISBN 978-0-201-02089-2.
  39. Cai, S.; Xu, Y. Effects of Outcome, Process and Shopping Enjoyment on Online Consumer Behaviour. Electron. Commer. Res. Appl. 2006, 5, 272–281.
  40. Hwang, Y.; Jeong, J. Electronic Commerce and Online Consumer Behavior Research: A Literature Review. Inf. Dev. 2016, 32, 377–388.
  41. Haryanti, T.; Subriadi, A.P. Factors and Theories for E-Commerce Adoption: A Literature Review. Int. J. Electron. Commer. Stud. 2020, 11, 87–105.
  42. Ajzen, I. The Theory of Planned Behavior. Organ. Behav. Hum. Decis. Process. 1991, 50, 179–211.
  43. Davis, F.D. Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Q. 1989, 13, 319.
  44. Davis, F.D.; Bagozzi, R.P.; Warshaw, P.R. User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Manag. Sci. 1989, 35, 982–1003.
  45. Venkatesh, V.; Morris, M.G.; Davis, G.B.; Davis, F.D. User Acceptance of Information Technology: Toward a Unified View. MIS Q. 2003, 27, 425.
  46. Venkatesh, V.; Thong, J.Y.L.; Xu, X. Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Q. 2012, 36, 157.
  47. DeLone, W.H.; McLean, E.R. The DeLone and McLean Model of Information Systems Success: A Ten-Year Update. J. Manag. Inf. Syst. 2003, 19, 9–30.
  48. Bhattacherjee, A. Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Q. 2001, 25, 351.
  49. Harasis, A.A.; Qureshi, M.I.; Rasli, A. Development of Research Continuous Usage Intention of E-Commerce. A Systematic Review of Literature from 2009 to 2015. Int. J. Eng. Technol. 2018, 7, 73.
  50. Wang, Y.-S. Assessing E-Commerce Systems Success: A Respecification and Validation of the DeLone and McLean Model of IS Success. Inf. Syst. J. 2008, 18, 529–557.
  51. Lee, S.; Cude, B.J. Consumer Complaint Channel Choice in Online and Offline Purchases: Consumer Complaint Channels. Int. J. Consum. Stud. 2012, 36, 90–96.
  52. Pantano, E.; Viassone, M. Engaging Consumers on New Integrated Multichannel Retail Settings: Challenges for Retailers. J. Retail. Consum. Serv. 2015, 25, 106–114.
  53. Fassnacht, M.; Unterhuber, S. Consumer Response to Online/Offline Price Differentiation. J. Retail. Consum. Serv. 2016, 28, 137–148.
  54. Rapp, A.; Baker, T.L.; Bachrach, D.G.; Ogilvie, J.; Beitelspacher, L.S. Perceived Customer Showrooming Behavior and the Effect on Retail Salesperson Self-Efficacy and Performance. J. Retail. 2015, 91, 358–369.
  55. Basak, S.; Basu, P.; Avittathur, B.; Sikdar, S. A Game Theoretic Analysis of Multichannel Retail in the Context of “Showrooming”. Decis. Support Syst. 2017, 103, 34–45.
  56. Bhattacherjee, A. An Empirical Analysis of the Antecedents of Electronic Commerce Service Continuance. Decis. Support Syst. 2001, 32, 201–214.
  57. Prassida, G.F.; Hsu, P.-Y.; Chang, Y.-W. Understanding How O2O Service Synergies Drive Customer Continuance Intention: A Study of OTAs and Hotels. Asia Pac. J. Tour. Res. 2021, 26, 1139–1155.
  58. Wang, O.; Scrimgeour, F. Consumer Adoption of Online-to-Offline Food Delivery Services in China and New Zealand. Br. Food J. 2021, 124, 1590–1608.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)
ScholarVision Creations
Feedback