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Volume 16, August

J. Theor. Appl. Electron. Commer. Res., Volume 16, Issue 6 (September 2021) – 7 articles

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Article
SenseTrust: A Sentiment Based Trust Model in Social Network
J. Theor. Appl. Electron. Commer. Res. 2021, 16(6), 2031-2050; https://doi.org/10.3390/jtaer16060114 - 27 Jul 2021
Viewed by 229
Abstract
Online social networks, as popular media and communications tools with their own extensive uses, play key roles in public opinion polls, politics, economy, and even governance. An important issue regarding these networks is the use of multiple sources of publishing or re-publishing news [...] Read more.
Online social networks, as popular media and communications tools with their own extensive uses, play key roles in public opinion polls, politics, economy, and even governance. An important issue regarding these networks is the use of multiple sources of publishing or re-publishing news and propositions that can influence audiences depending on the level of trust in these sources between users. Therefore, estimating the level of trust in social networks between users can predict the extent of social networks’ impact on news and different publication and re-publication sources, and correspondingly provide effective strategies in news dissemination, advertisements, and other diverse contents for trustees. Therefore, trust is introduced and interpreted in the present study. A large portion of interactions in social networks is based on sending and receiving texts employing natural language processing techniques. A Hidden Markov Model (HMM) was designed via an efficient model, namely SenseTrust, to estimate the level of trust between users in social networks. Full article
(This article belongs to the Section e-Commerce Analytics)
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Article
A Financial Incentive Mechanism for Truthful Reporting Assurance in Online Crowdsourcing Platforms
J. Theor. Appl. Electron. Commer. Res. 2021, 16(6), 2014-2030; https://doi.org/10.3390/jtaer16060113 - 26 Jul 2021
Viewed by 276
Abstract
In today’s world, crowdsourcing is regarded as an effective strategy to deal with a high volume of small issues whose solutions can have their own complexities in systems. Moreover, requesters are currently providing hundreds of thousands of tasks in online job markets and [...] Read more.
In today’s world, crowdsourcing is regarded as an effective strategy to deal with a high volume of small issues whose solutions can have their own complexities in systems. Moreover, requesters are currently providing hundreds of thousands of tasks in online job markets and workers need to perform these tasks to earn money. Thus far, various aspects of crowdsourcing including budget management, mechanism design for price management, forcing workers to behave truthfully in bidding prices, or maximized gains of crowdsourcing have been considered in different studies. One of the main existing challenges in crowdsourcing is how to ensure truthful reporting is provided by contributing workers. Since the amount of pay to workers is directly correlated with the number of tasks performed by them over a period of time, it can be predicted that strong incentives encourage them to carry out more tasks by giving untruthful answers (providing the first possible answer without examining it) in order to increase the amount of pay. However, crowdsourcing requesters need to obtain truthful reporting as an output of tasks assigned to workers. In this study, a mechanism was developed whose implementation in crowdsourcing could ensure truthful reporting by workers. The mechanism provided in this study was evaluated as more budget feasible and it was also fairer for requesters and workers due to its well-defined procedure. Full article
(This article belongs to the Section e-Commerce Analytics)
Article
Omni-Channel Customer Experience (In)Consistency and Service Success: A Study Based on Polynomial Regression Analysis
by and
J. Theor. Appl. Electron. Commer. Res. 2021, 16(6), 1997-2013; https://doi.org/10.3390/jtaer16060112 - 25 Jul 2021
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Abstract
Drawing on expectation disconfirmation theory, this study explores the dyadic nature of omni-channel consistency on customer experience. Specifically, we propose a conceptual model that focuses on a brand’s offline channel customer experience relative to that of its online channel, and test the influences [...] Read more.
Drawing on expectation disconfirmation theory, this study explores the dyadic nature of omni-channel consistency on customer experience. Specifically, we propose a conceptual model that focuses on a brand’s offline channel customer experience relative to that of its online channel, and test the influences of customer experience (in)consistency on customer satisfaction, which then improves repurchase intention and word-of-mouth. The results of polynomial regressions on 265 survey respondents indicate that given omni-channel customer experience inconsistency, customers prefer consistent online and offline experiences. For omni-channel consistency at lower levels of customer experience quality, customers prefer consistency at higher levels of quality. For omni-channel inconsistency where offline customer experience quality is lower than that online, customers prefer omni-channel inconsistency, where offline customer experience quality is higher than that online. These findings produce not only theoretical contributions but also insightful suggestions for how customer experience can be taken into consideration in the promotion of a brand’s omni-channel service success. Full article
(This article belongs to the Special Issue Emerging Topics in Omni-Channel Operations)
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Article
Empty the Shopping Cart? The Effect of Shopping Cart Item Sorting on Online Shopping Cart Abandonment Behavior
J. Theor. Appl. Electron. Commer. Res. 2021, 16(6), 1973-1996; https://doi.org/10.3390/jtaer16060111 - 07 Jul 2021
Viewed by 539
Abstract
The vigorous development of e-commerce has led to online retailers or platforms increasing the capacity of online shopping carts. A large number of products are added to the online shopping cart, but they are not “emptied.” The resulting behavior of products being stuck [...] Read more.
The vigorous development of e-commerce has led to online retailers or platforms increasing the capacity of online shopping carts. A large number of products are added to the online shopping cart, but they are not “emptied.” The resulting behavior of products being stuck in the shopping cart is called the “shopping cart abandonment behavior.” Previous literature has focused on the large number of antecedent variables that affect shopping cart abandonment behavior in the pre-decision stage of online shopping. This previous research has studied how to reduce shopping cart abandonment behavior from the perspective of consumers. By focusing on the post-decision-making stage of shopping, this research proposes to sort the products in a chronological order (ascending and descending order) after the products are added to the shopping cart and reduce shopping cart abandonment behavior through the intermediary of forgetfulness and choice overload. We use an exploratory study and two laboratory experiments to reveal the above intermediary mechanism. Our results show that online shopping cart abandonment generally occurs in shopping carts on all major platforms. Forgetting and shopping cart page rendering may be the reasons that lead to shopping cart abandonment behavior. In the case of targeted tasks, ascending order has a significant impact on abandonment behavior, choice overload mediated this effect. Full article
(This article belongs to the Special Issue Emerging Topics in Omni-Channel Operations)
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Article
The Role of Social Media in the Innovation and Performance of Kuwaiti Enterprises in the Food Sector
J. Theor. Appl. Electron. Commer. Res. 2021, 16(6), 1960-1972; https://doi.org/10.3390/jtaer16060110 - 29 Jun 2021
Viewed by 503
Abstract
This study examined social media’s role in various levels of research, development, and performance within enterprises in Kuwait. The research incorporated four inductive case analyses in various sectors. The case studies epitomize the supply chain of Kuwaiti enterprises, including small and medium-sized enterprises [...] Read more.
This study examined social media’s role in various levels of research, development, and performance within enterprises in Kuwait. The research incorporated four inductive case analyses in various sectors. The case studies epitomize the supply chain of Kuwaiti enterprises, including small and medium-sized enterprises (SMEs). Media richness theory and social exchange effectuation theory were utilized to create an effective theory and a theoretical framework. This study collected data via a questionnaire completed by 100 managers employed by Kuwaiti SMEs specialized in the food sector and interviews with eight managers. Numerical data were analyzed via SPSS software, while textual data were analyzed by applying thematic analysis. The results of this study suggest that Kuwaiti companies should adopt social media platforms and other novel, innovative outlets to publicize their organizations and maximize performance. Social media richness and openness tend to determine the supplier selection process in most Kuwaiti enterprises, leading to positive transactional and social impacts on entrepreneurship. Full article
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Article
Responding to Negative Electronic Word of Mouth to Improve Purchase Intention
J. Theor. Appl. Electron. Commer. Res. 2021, 16(6), 1945-1959; https://doi.org/10.3390/jtaer16060109 - 28 Jun 2021
Viewed by 438
Abstract
Retailers have little control over what their customers say about their products and services online. Review platforms (e.g., Yelp and Travelocity) are rife with negativity, from both real customers with bad experiences and from fake reviews created by competitors. These negative reviews have [...] Read more.
Retailers have little control over what their customers say about their products and services online. Review platforms (e.g., Yelp and Travelocity) are rife with negativity, from both real customers with bad experiences and from fake reviews created by competitors. These negative reviews have been shown to influence the purchasing behavior of future consumers. Many platforms do afford companies some control by including them in the online conversation about their products or services. Crafting a response to a poor review which appeals to future consumers may mitigate some of the negative outcomes associated with that review. This study advances our knowledge of responding to negative reviews by adding to the growing body of research, using a simulation-based experiment to test the influence of three elements of a review response on purchase intention (i.e., an apology, an explanation and a pledge to correct the problem identified in the review). In doing so, the data show that purchase intention increases only when a response contains all three elements. Implications for e-commerce researchers and review platform developers are discussed. Full article
(This article belongs to the Section Digital Marketing and the Connected Consumer)
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Article
Identifying Startups Business Opportunities from UGC on Twitter Chatting: An Exploratory Analysis
J. Theor. Appl. Electron. Commer. Res. 2021, 16(6), 1929-1944; https://doi.org/10.3390/jtaer16060108 - 26 Jun 2021
Viewed by 407
Abstract
The startup business ecosystem in India has experienced exponential growth. The amount of investment in Indian startups in the last decade demonstrates the strong interest of the technology industry to these business models based on innovation. In this context, the present study aims [...] Read more.
The startup business ecosystem in India has experienced exponential growth. The amount of investment in Indian startups in the last decade demonstrates the strong interest of the technology industry to these business models based on innovation. In this context, the present study aims to identify investment opportunities for investors in Indian startups by identifying key indicators that characterize the startup ecosystem in India. To this end, a three steps data mining method is developed using data mining techniques. First, a sentiment analysis (SA), a machine learning approach that classifies the topics into groups expressing feelings, is applied to a dataset. Next, we develop a Latent Dirichlet Allocation (LDA) model, a topic-modeling technique that divides the sample of n = 14.531 tweets from Twitter into topics, using user-generated content (UGC) as data. Finally, in order to identify the characteristics of each topic we apply textual analysis (TA) to identify key indicators. The originality of the present study lies in the methodological process used for data analysis. Our results also contribute to the literature on startups. The results demonstrate that the Indian startup ecosystem is influenced by areas such as fintech, innovation, crowdfunding, hardware, funds, competition, artificial intelligence, augmented reality and electronic commerce. Of note, in view of the exploratory approach of the present study, the results and implications should be taken as descriptive, rather than determining for future investments in the Indian startup ecosystem. Full article
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