The power of social learning: How do observational and word-of-mouth learning influence online consumer decision processes?
Introduction
Online consumers tend to collect information to assist consumption decisions (Huang & Benyoucef, 2013) by adopting social learning (SL) methods (Bandura, 1977; Cheung, Liu, & Lee, 2015). One traditional SL method is observational learning (OL) (Bandura & McClelland, 1977; Bikhchandani, Hirshleifer, & Welch, 1998). Consumers can observe the actions of others and then determine how to adopt new behaviors. They can later utilize this information to guide their future actions and avoid unnecessary mistakes in the process of online shopping decision making (Z. Shi & Whinston, 2013). Another SL method is word-of-mouth learning (WOML). Consumers can learn from and be affected by other consumers’ opinions (Arndt, 1967). In practice, online sellers and platform managers have realized the crucial roles of OL and WOML in sales. Online platforms have developed some advanced IT tools to facilitate consumers’ OL and WOM by providing historical sales and online reviews.
Both platform managers and online sellers need to understand whether facilitating WOML, OL, or both simultaneously will be more effective in influencing consumers’ decisions. Because platform managers have to weigh which information should be provided on a limited webpage or whether to develop new IT tools. Sellers must overcome new challenges since managing information to facilitate WOML and OL often requires separate strategies to manage costs and pursue economic utility.
It is still unclear whether OL or WOML is more effective in assisting consumers’ decisions. Although several studies have measured their effects (Gilal, Zhang, Gilal, & Gilal, 2019; Godes & Mayzlin, 2004; Hanson & Putler, 1996; Herhausen, Ludwig, Grewal, Wulf, & Schoegel, 2019; Xinxin Li, Hitt, & Zhang, 2011; Liu, 2006; Nakayama & Wan, 2021; Salganik, Dodds, & Watts, 2006; Soltysinski & Dholakia, 2001; J. Zhang, 2010; Zhuang, Cui, & Peng, 2018), only a few studies have argued their differences on consumers’ purchase decisions (Y. Chen, Wang, & Xie, 2011; Cheung, Xiao, & Liu, 2014; Xitong Li & Wu, 2013). In addition, prior studies have tended to treat consumers’ payment intentions or consumption decisions as the ultimate outcomes of their model (X. Shi, Zheng, & Yang, 2020), particularly when exploring how social learning affects consumers’ final purchase decisions (Y. Chen et al., 2011; Cheung et al., 2014; Xitong Li & Wu, 2013).
However, most consumers often search for product information, evaluate products sold by different sellers, and even add products to their shopping carts, but do not make a purchase decision immediately or ultimately. Thus, most consumer purchase decisions are not disconnected actions but are complex processes, which can be considered problem-solving processes that include different stages (Engel, Blackwell, & Minniard, 1993; Masterson & Pickton, 2010). In practice, frequent interactions in virtual environments may not increase the probability of an eventual sale (Y. Wang & Yu, 2017), but few studies have considered the online consumer decision process as the outcome of research models addressing the effect of social learning on consumers’ decisions. Furthermore, if there is a difference between the effects of OL and WOML on the online consumer decision process, the reason remains poorly understood. In summation, few studies have considered the differences between the effects of OL and WOML on the consumer decision process and their influential mechanisms.
Online consumers’ decisions will also be moderated by some additional factors. For example, when purchasing products of different involvement levels, consumers perceive different correlations (Hong, 2015), and they might exert different weights to learn via OL or WOML. For instance, when purchasing high-involvement products, consumers might collect more information and actively participate in discussions on these products. However, the moderating role of product involvement on the relationship between OL or WOML and online consumers’ decision processes remains unclear.
Therefore, in this paper we explore the power of social learning, specifically how OL and WOML influence the online consumer decision process. We also investigate their mechanisms. We seek to answer the following research questions:
Q1: Do OL and WOML have significant and differential effects on online consumers’ decision processes, and if so, what are these effects?
Q2: What is the influencing mechanism of OL or WOML on online consumers’ decision processes? In other words, what is the mediation factor between OL or WOML and online consumers’ decision processes?
Q3: Does product involvement moderate the role of OL or WOML on online consumers’ decision processes, and if so, how is this moderation effected?
To answer the above research questions, we consider three stages in the online shopping process: “product search,” “product selection and evaluation,” and “final purchase decision” (Engel et al., 1993). We have conducted two separate studies as follows. Study 1 is a pilot study using behavior experiments to explore the significant and differential effects of OL and WOML on the consumer decision process and the moderating role of product involvement. Specifically, we have built an experimental platform for online shopping on which participants could fulfill shopping tasks via OL and WOML separately. Study 2 is a structural equation modeling analysis to confirm the main effect and further reveal the influence mechanisms of OL and WOML on the three online shopping stages. It also explores the moderating effect of product involvement based on motivation reinforcement theory.
This study provides several theoretical and practical contributions. First, to the best of our knowledge, our research is one of the first to explore and compare the different effects of OL and WOML on the three stages of online consumer decision making, which enriches the literature that discusses the power of online social learning on the online consumers’ decision processes. Second, although prior studies have drawn on various information theories, such as signal theory (Cheung et al., 2014) and information cascade theory (Y. Wang & Yu, 2017), this study introduces the motivation reinforcement theory from the perspective of social psychology to better understand the impact of OL and WOML on online consumers’ decision processes. Third, this study creatively investigates the moderating role of product involvement on the relationships between OL or WOML and consumers’ decision processes, and further explores the moderating role of product involvement on the relationships between OL or WOML and consumers’ motivations. Furthermore, the results of our research can offer a theoretical basis for sellers and platform managers to fully utilize to the power of social learning and so provide better shopping-related information to improve the convenience and efficiencies of the online consumer's shopping experience.
This paper proceeds as follows: Section 2 provides the theoretical background. Section 3 develops the hypothesis. Section 4 describes the research methodology and presents the results of studies 1 and 2. Section 5 demonstrates our main findings and discusses the implications for management scholars and practitioners. Finally, Section 6 discusses the limitations and directions for future research.
Section snippets
The consumer decision process
Most consumer purchase decisions are not a disconnected action but a complex process, which can be considered a problem-solving process that includes either three stages (i.e., search, evaluation, and purchase) (Engel et al., 1993) or five stages (i.e., need recognition, information search, evaluation of alternatives, purchase decision, post-purchase behavior) (Masterson & Pickton, 2010). Previous studies have attempted to integrate the various stages of the decision-making process (William B
Consumer decision efficiency
In this research context, we take consumer decision efficiency of the online shopping process (i.e., search, evaluation, and purchase) as a dependent variable. The concept of efficiency is fundamental to economics and summarizes the notion of obtaining the largest possible output, given the available technology and constraints, from a set of inputs (Sproles, Geistfeld, & Badenhop, 1978). In a general sense, efficiency is the (often measurable) ability to avoid wasting materials, energy,
Research methodology
In this paper, we conducted two studies as follows. Study 1 is a pilot study using behavior experiments to explore the significant differential effects of two methods of social learning on the consumer decision processes and the moderating role of product involvement. Study 2 is a structural equation modeling analysis to confirm the findings of study 1 and further reveal influence mechanisms based on motivation reinforcement theory, including a) the mediation factor between social learning and
Discussion
Numerous prior studies have measured the effect of OL or WOML on consumers’ consumption decisions (Chevalier & Mayzlin, 2006; Gilal et al., 2019; Godes & Mayzlin, 2004; Hanson & Putler, 1996; Xinxin Li et al., 2011; Liu, 2006; Nakayama & Wan, 2021; Salganik et al., 2006; Soltysinski & Dholakia, 2001; J. Zhang, 2010; Zhuang et al., 2018), but few studies have investigated the differences between the effects of OL and WOML (Y. Chen et al., 2011; Cheung et al., 2014; Herhausen et al., 2019;
Limitations and future research
This study elaborated on the influence of social learning on the consumer decision process and investigated the mechanisms and the moderating role of product involvement. However, there are still some limitations to this study. First, the research sample mainly comprised college students because of our focus on campus recruitment. Future research should target other populations. Second, from the perspective of the information selection process, we focused on two social learning methods that
Authors' contribution
Fenghua Wang: Formal Analysis, Conceptualization, Methodology, Validation, Writing-Original Draft, Data Curation.
Mohan Wang: Formal analysis, Software, Investigation, Writing-Original Draft, Writing-Review & Editing, Visualization.
Yan Wan: Conceptualization, Resources, Supervision, Funding Acquisition.
Jia Jin: Writing-Review & Editing, Analysis.
Yu Pan: Conceptualization, Data Curation, Supervision, Resources, Project administration, Funding Acquisition.
Declaration of Competing Interest
None.
Acknowledgments
The authors are grateful for the helpful input of the editor, associate editor, and reviewers. In addition, the authors acknowledge support from the National Natural Science Foundation of China (71772124, 71942003, 71671115, 71701139; 71874018), the Ministry of Education of China (17YJA630097), and the Fundamental Research Funds for Central Universities (no. 20161140019).
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The first two authors contribute equally to this work.