Extending the Theory of Planned Behavior in the Social Commerce Context: A Meta-Analytic SEM (MASEM) Approach

2023 | Information Systems Frontiers | Citations: 0

Authors: Leong, Lai-Ying; Hew, Teck-Soon; Ooi, Keng-Boon; Metri, Bhimaraya; Dwivedi, Yogesh K.

Abstract: The theory of planned behavior (TPB) is popular in social commerce research, but ... Expand

Abstract: The theory of planned behavior (TPB) is popular in social commerce research, but existing studies have yielded inconclusive and inconsistent findings. As TPB was developed before the introduction of the Internet, Web 2.0 technologies, and social media, it is time to evaluate whether its relevance, effectiveness, novelty, and parsimony may have deteriorated due to temporal factors and the emergence of new Information Systems (IS) artifacts. In this work, an extended TPB2 is proposed by integrating social support, and MASEM analysis was conducted using a pooled correlation matrix. The findings revealed that TPB2 can better capture the nature of social commerce, as it can explain 28.4% of the variance in attitude, 59.0% of the variance in behavioral intention, and 9% of the variance in use behavior. In addition, it indicates that masculinity and uncertainty avoidance are the most significant Hofstedian cultural dimensions. The theoretical and managerial implications are also discussed along with the research agenda. Collapse

Topics: behavioral intention artificial intelligence social commerce conversational commerce organizational productivity

Methods: structural equation modeling meta analysis path analysis literature sample literature study

Theories: cultural dimensions theory theory of planned behavior

A Chat-based personal recommendation mechanism with opinion intelligence

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Li, Yung-Ming; hsieh, chin yu; Hsu, Cheng-Chi

Abstract: A Chat-based personal recommendation mechanism with opinion intelligence" (2023 ... Expand

Abstract: A Chat-based personal recommendation mechanism with opinion intelligence" (2023). AbstractWith the rapid development of the Internet and mobile devices, users around the world have accumulated a large amount of comment data on the Internet. Nowadays, users tend to search for information on the Internet before making purchase decisions. How to provide personalized services while avoiding information overload has gradually become an important issue. This study proposes a dialogue-based personalized recommendation mechanism with opinion intelligence. In addition to understanding user preferences through dialogue and interaction with users, we further analyze the content of reviews and the social influence of review authors to better help users. The results show that the dialogue mechanism proposed in this study has a good recommendation effect and can effectively improve user experience. Collapse

Topics: recommender system social influence decision making user experience usability

Methods: experiment chatbot term frequency–inverse document frequency naïve bayes natural language processing

Can Chatbots Be Persuasive? How to Boost the Effectiveness of Chatbot Recommendations for Increasing Purchase Intention

2023 | HICSS | Citations: 0

Authors: Schwede, Melanie; Meyer (Née Mozafari), Nika; Von Schnakenburg, Niclas; Hammerschmidt, Maik

Abstract: Firms increasingly invest in chatbots that provide purchase recommendations. How ... Expand

Abstract: Firms increasingly invest in chatbots that provide purchase recommendations. However, customers often reject recommendations by chatbots because they find neither the contents of the recommendation (message-level persuasiveness) nor the chatbot itself (source-level persuasiveness) persuasive. To overcome these barriers and increase purchase intention, this study examines how the content of recommendation messages should be designed and which communication style the chatbot should use to provide recommendation messages. Results of a 2 (two-sided vs. one-sided recommendation message) ✕ 3 (warm vs. competent vs. neutral communication style) between-subject online experiment show that a two-sided recommendation message increases purchase intention, but only for chatbots using a warm or competent communication style. Whereas a warm chatbot leads to higher purchase intentions of a recommendation through promoting its source persuasiveness, a competent chatbot increases recommendation effectiveness by promoting message persuasiveness. Therefore, firms should refine a chatbot’s communication style for providing recommendations that persuade customers to purchase. Collapse

Topics: intranet social media conversational commerce

Methods: chatbot experiment parametric test ANOVA survey

AI-based chatbots in conversational commerce and their effects on product and price perceptions

2023 | Electronic Markets | Citations: 0

Authors: Sidlauskiene, Justina; Joye, Yannick; Auruskeviciene, Vilte

Abstract: The rise of AI-based chatbots has gradually changed the way consumers shop. Natu ... Expand

Abstract: The rise of AI-based chatbots has gradually changed the way consumers shop. Natural language processing (NLP) technology and artificial intelligence (AI) are likely to accelerate this trend further. However, consumers still prefer to engage with humans and resist chatbots, which are often perceived as impersonal and lacking the human touch. While the predominant tendency is to make chatbots appear more humanlike, little is known about how anthropomorphic verbal design cues in chatbots influence perceived product personalization and willingness to pay a higher product price in conversational commerce contexts. In the current work, we set out to test this through one pre-test (N = 135) and two online experiments (N = 180 and 237). We find that anthropomorphism significantly and positively affects perceived product personalization, and that this effect is moderated by situational loneliness. Moreover, the results show that the interaction between anthropomorphism and situational loneliness has an impact on the willingness to pay a higher product price. The research findings can be used for future applications of AI-driven chatbots where there is a need to provide personalized and data-driven product recommendations. Collapse

Topics: usability personalization willingness to pay artificial intelligence conversational commerce

Methods: chatbot experiment parametric test natural language processing experimental task

Users taking the blame? How service failure, recovery, and robot design affect user attributions and retention

2022 | Electronic Markets | Citations: 0

Authors: Meyer (née Mozafari), Nika; Schwede, Melanie; Hammerschmidt, Maik; Weiger, Welf Hermann

Abstract: Firms use robots to deliver an ever-expanding range of services. However, as ser ... Expand

Abstract: Firms use robots to deliver an ever-expanding range of services. However, as service failures are common, service recovery actions are necessary to prevent user churn. This research further suggests that firms need to know how to design service robots that avoid alienating users in case of service failures. Robust evidence across two experiments demonstrates that users attribute successful service outcomes internally, while robot-induced service failures are blamed on the firm (and not the robot), confirming the well-known self-serving bias. While this external attributional shift occurs regardless of the robot design (i.e., it is the same for warm vs. competent robots), the findings imply that service recovery minimizes the undesirable external shift and that this effect is particularly pronounced for warm robots. For practitioners, this implies prioritizing service robots with a warm design for maximizing user retention for either type of service outcome (i.e., success, failure, and failure with recovery). For theory, this work demonstrates that attribution represents a meaningful mechanism to explain the proposed relationships. Collapse

Topics: robotic crowdsourcing robo advisor conversational commerce user behavior

Methods: ANOVA parametric test post-hoc analysis survey experiment

Theories: attribution theory cognitive theory

The Effect of Perceived Similarity in Dominance on Customer Self-Disclosure to Chatbots in Conversational Commerce

2020 | European Conference On Information Systems | Citations: 0

Authors: Gnewuch, Ulrich; Yu, Meng; Maedche, Alexander

Abstract: Recent years have seen increased interest in the application of chatbots for co ... Expand

Abstract: Recent years have seen increased interest in the application of chatbots for conversational commerce. However, many chatbots are falling short of their expectations because customers are reluctant to disclose personal information to them (e.g., product interest, email address). Drawing on social response theory and similarity-attraction theory, we investigated (1) how a chatbot's language style influences users' perceived similarity in dominance (i.e., an important facet of personality) between them and the chatbot and (2) how these perceptions influence their self-disclosure behavior. We conducted an online experiment (N=205) with two chatbots with different language styles (dominant vs. submissive). Our results show that users attribute a dominant personality to a chatbot that uses strong language with frequent assertions, commands, and self-confident statements. Moreover, we find that the interplay of the user's own dominance and the chatbot's perceived dominance creates perceptions of similarity. These perceptions of similarity increase users' degree of self-disclosure via an increased likelihood of accepting the chatbot's advice. Our study reveals that language style is an important design feature of chatbots and highlights the need to account for the interplay of design features and user characteristics. Furthermore, it also advances our understanding of the impact of design on self-disclosure behavior. Collapse

Topics: personality electronic mail conversational commerce mobile phone information privacy concern

Methods: chatbot experiment survey parametric test SEM tool

Theories: social response theory

Conversational User Interfaces for Online Shops? A Categorization of Use Cases

2018 | International Conference on Information Systems | Citations: 15

Authors: Baier, Daniel; Rese, Alexandra; Röglinger, Maximilian

Abstract: How do conversational user interfaces for online shops via messaging services an ... Expand

Abstract: How do conversational user interfaces for online shops via messaging services and voice assistants influence customers’ satisfaction? Which use cases are attractive from a customer’s view point? Which use cases are must-be and for which customer segments? The answer to these questions is looked for in this paper. A Segmented Kano perspective is used to derive use case groups and related customer segments simultaneously. The paper starts with an overview on conversational commerce and on chatbots for this purpose. Then, the research method and the use case development is described. Two representative surveys with 2,165 customers of a major German online fashion retailer evaluating 13 messaging service and 2,025 customers evaluating 13 voice assistant use cases were conducted and analyzed. The focus was on the intention to use conversational user interfaces for online shops and the influence on customer satisfaction. Collapse

Topics: electronic commerce conversational agent website conversational commerce Facebook Messenger

Methods: survey cluster analysis chatbot archival research