The “holiday effect” in consumer satisfaction: Evidence from review ratings

2023 | Information & Management | Citations: 0

Authors: Deng, Lingfei; Ye, Qiang; Xu, DaPeng; Sun, Fangfang

Abstract: This study explores whether and how a holiday effect exists in consumers’ review ... Expand

Abstract: This study explores whether and how a holiday effect exists in consumers’ review rating behavior, which is well explained by expectancy-disconfirmation theory (EDT). After utilizing the secondary data of online restaurant reviews and conducting an online experiment, we find that consumers have the tendency to form high expec­ tations before holidays, thus leading to more negative disconfirmation and bad moods, which finally induce their negative review rating behavior on holidays. The consumer heterogeneity analyses provide evidence that con­ sumers who are tourists, tourists with less travel experience, and male natives are more likely to post lower review ratings on holidays. Collapse

Topics: online review service quality user-generated content consumer behavior website

Methods: experiment experimental group regression analysis method product review survey

Management of IT Costs in the Digital Age – A Literature Review

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Riedinger, Constanze; Huber, Melanie; Prinz, Niculin; Rentrop, Christopher

Abstract: Digitalization requires organizations to strategically invest in information te ... Expand

Abstract: Digitalization requires organizations to strategically invest in information technology (IT). As a result, the costs associated with IT in companies are rising and technological progress changes the setting for IT management. This poses challenges for IT managers to ensure spend-efficiency and manage IT costs transparently. However, no current literature review gives an overview of how IT cost management (ITCM) research dealt with past transformations. This paper aims to investigate ITCM concepts considering their historical context. It then derives implications for the digital age and identifies future research fields. The historical literature review reveals that ITCM research evolved with technological advances and the target to manage all IT-related costs and evaluate the impact of IT spend. However, the presented concepts lack consideration of current changes that hamper spend-efficiency and strategic decisions. Hence, this paper enables future research to address the identified research gaps. Additionally, practitioners gain awareness of how they can benefit from developed ITCM concepts. Collapse

Topics: IT investment accounting database system cost accounting social media

Methods: literature study historical review theoretical contribution synthesis

Organizations Decentered: Data Objects, Technology and Knowledge

2022 | Organization Science | Citations: 37

Authors: Alaimo, Cristina; Kallinikos, Jannis

Abstract: Data are no longer simply a component of administrative and managerial work but ... Expand

Abstract: Data are no longer simply a component of administrative and managerial work but a pervasive resource and medium through which organizations come to know and act upon the contingencies they confront. We theorize how the ongoing technological developments reinforce the traditional functions of data as instruments of management and control but also reframe and extend their role. By rendering data as technical entities, digital technologies transform the process of knowing and the knowledge functions data fulfil in socioeconomic life. These functions are most of the times mediated by putting together disperse and steadily updatable data in more stable entities we refer to as data objects. Users, customers, products, and physical machines rendered as data objects become the technical and cognitive means through which organizational knowledge, patterns, and practices develop. Such conditions loosen the dependence of data from domain knowledge, reorder the relative significance of internal versus external references in organizations, and contribute to a paradigmatic contemporary development that we identify with the decentering of organizations of which digital platforms are an important specimen. Collapse

Topics: datafication organizational change organizational memory data relevance organizational transformation

Methods: computational algorithm theory development historical review

Behavior-Driven Model Design: A Deep Learning Recommendation Model Jointing Users and Products Reviews

2022 | International Conference on Information Systems | Citations: 0

Authors: Wang, Le; Zhao, Xi; Shen, Zhihao

Abstract: Data-driven is widely mentioned, but the data is generated by user behavior. Ou ... Expand

Abstract: Data-driven is widely mentioned, but the data is generated by user behavior. Our work aims to utilize a behavior-driven model design pattern to improve accuracy and provide explanations in review-based recommendations. Review-based recommendation introduces review text to overcome the sparseness and unexplainably of rating or scoresbased model. Driven by users rating behavior and human cognitive abilities, we proposed a deep learning recommendation model jointing users and products reviews (DLRM-UPR) to learn user preferences and product characteristics adaptively. The DLRM-UPR consists of word, text, and context co-attention layers considering the interaction between each user-product-context pair. Extensive experiments on real datasets demonstrate that DLRM-UPR outperforms existing state-of-the-art models. In addition, the relevant information in the reviews and the suggestion for improving the user experience can be highlighted to explain the recommendation results. Collapse

Topics: user behavior recommender system user experience online review

Methods: word embedding ablation analysis artificial neural network product review experiment

Online Review Censorship

2022 | International Conference on Information Systems | Citations: 0

Authors: sanatizadeh, Aida; Burtch, Gordon; Hong, Yili; Hu, Yuheng

Abstract: Ample anecdotal evidence in the media notes that many businesses seek to 'silen ... Expand

Abstract: Ample anecdotal evidence in the media notes that many businesses seek to 'silence' negative reviews, e.g., via legal threat. Despite attention toward this issue, we are aware of no systematic analyses addressing it. We address that gap here, leveraging review data from TripAdvisor.com. First, we estimate that ~1% of truthful reviews are deleted within six months of posting and that negative reviews are significantly more likely to be deleted, consistent with a mechanism of censorship. The effect is substantial; we estimate that a 1star decrease in rating valence is associated with an approximate 25% (0.25 pp) increase in the probability of deletion. Second, we examine how freedom of expression (FoE) in a country associates with characteristics of (uncensored) online reviews. We find that FoE associates with larger review volumes, lower review valence, and faster review posting. We discuss implications for online ratings platforms, consumers, and research opportunities Collapse

Topics: online review fake review user-generated content Google+ social media

Methods: support vector machine term frequency–inverse document frequency product review deep learning BERT

Consumer preference analysis based on text comments and ratings: A multi-attribute decision-making perspective

2022 | Information & Management | Citations: 0

Authors: Zhu, Bin; Guo, Dingfei; Ren, Long

Abstract: With the popularity of social media, extracting consumer preferences from online ... Expand

Abstract: With the popularity of social media, extracting consumer preferences from online consumer-generated content is of vital importance for product/service providers to develop tailored marketing strategies. However, existing approaches face difficulties analyzing consumer preferences over different attributes of alternatives (restaurants, hotels, etc.), which hinders product/service providers from comprehensively understanding consumer choice decisions. To address this issue, we solve for the consumer preferences over the attributes represented by attribute weights based on consumers’ historical data, including text comments and overall ratings. Specifically, for each comment and a corresponding rating, we first employ sentiment analysis to calculate values of the attributes, and then develop a quadratic programming model to solve for the weights. Based on a stream of a consumer’s text comments and overall ratings, we can correspondingly obtain a stream of weights indexed by the comment time. We then model this stream of weights as hesitant judgments and employ a hesitant multiplicative programming method to solve for the final weights that fit the consumer’s preferences over attributes at the highest satisficing level. In the application of recommendation, our approach not only provides insights into the consumer’s preferences but also has higher prediction power compared with some state-of-the-art methods. Collapse

Topics: recommender system artificial intelligence

Methods: sentiment analysis optimization model word embedding non linear optimization historical review

Theories: theory of bounded rationality

Machine learning in information systems - a bibliographic review and open research issues

2021 | Electronic Markets | Citations: 0

Authors: Abdel-Karim, Benjamin M.; Pfeuffer, Nicolas; Hinz, Oliver

Abstract: Artificial Intelligence (AI) and Machine Learning (ML) are currently hot topics ... Expand

Abstract: Artificial Intelligence (AI) and Machine Learning (ML) are currently hot topics in industry and business practice, while management-oriented research disciplines seem reluctant to adopt these sophisticated data analytics methods as research instruments. Even the Information Systems (IS) discipline with its close connections to Computer Science seems to be conservative when conducting empirical research endeavors. To assess the magnitude of the problem and to understand its causes, we conducted a bibliographic review on publications in high-level IS journals. We reviewed 1,838 articles that matched corresponding keyword-queries in journals from the AIS senior scholar basket, Electronic Markets and Decision Support Systems (Ranked B). In addition, we conducted a survey among IS researchers (N = 110). Based on the findings from our sample we evaluate different potential causes that could explain why ML methods are rather underrepresented in top-tier journals and discuss how the IS discipline could successfully incorporate ML methods in research undertakings. Collapse

Topics: database system decision support system electronic market IS discipline accounting

Methods: literature study computational algorithm survey bibliometric analysis literature sample

Understanding the “Holiday Effect” in Online Restaurant Ratings

2021 | HICSS | Citations: 0

Authors: Deng, Lingfei; Xu, Dapeng; Ye, Qiang

Abstract: Plenty of studies have demonstrated the holiday effect in human decision-makings ... Expand

Abstract: Plenty of studies have demonstrated the holiday effect in human decision-makings. However, extant research fails to explore whether and how a holiday effect exists in online word-of-mouth generation. This work utilizes online restaurant reviews obtained from the most popular review platform in China to investigate this question with multiple empirical tests. The results suggest that diners are more likely to give a lower online rating on holidays, and this relationship is driven by a combination of restaurants’ specific reasons and diners’ specific factors. Specifically, the level of crowdedness and the quality of the restaurant can partly explain this relationship. Moreover, reviewers are found to be driven by cognitive mental processes instead of being carried away by emotions when they post online ratings on holidays. However, those who need to work overtime during holidays are found to be driven by bad mood when they post online ratings. Collapse

Topics: online review service quality user-generated content Python geographic information system

Methods: regression analysis method historical review statistical hypothesis test descriptive statistic natural language processing

Focus Within or On Others: The Impact of Reviewers’ Attentional Focus on Review Helpfulness

2021 | Information Systems Research | Citations: 0

Authors: Lei, Zhanfei; Yin, Dezhi; Zhang, Han

Abstract: When reviewers write online reviews, they differ in the focus of their attention ... Expand

Abstract: When reviewers write online reviews, they differ in the focus of their attention: some focus on their own experiences, whereas some direct their attention to others—prospective consumers who may read the reviews in the future. This paper explores how, why, and when reviewers’ attentional focus can influence the helpfulness evaluation of reviews beyond the impact of substantive review content. Drawing on the attentional focus and persuasion literatures, we develop a theoretical model proposing that reviewers’ attentional focus may influence consumers’ perception of review helpfulness through opposing processes, and that its overall effect is contingent on the review’s two-sidedness. Results of one archival analysis and five controlled experiments provide consistent support for our hypotheses. This work challenges the predominant view of the positive impact of other-focus (vs. self-focus), explores the interpersonal impact of a reviewer’s attentional focus on prospective consumers who are total strangers, and reveals an important, context-specific boundary condition. Collapse

Topics: mobile application online review decision making user-generated content digital distribution

Methods: experiment archival research analysis of covariance robustness check mediation analysis

Robust Action in Cross-Platform Communication by Ideological Groups: The Dark Side of Digital Activism

2021 | Americas Conference on Information Systems | Citations: 0

Authors: Miranda, Shaila M.; Sikhondze, Bachazile; Jensen, Matthew; Mery, Marina; Patel, Divya; Connelly, Shane

Abstract: Ideological groups, i.e., organizations articulating and promoting a set of val ... Expand

Abstract: Ideological groups, i.e., organizations articulating and promoting a set of values and beliefs, are adopting digital platforms to extend their reach. While many of these groups such as churches and civil rights groups are prosocial, others use the platforms to incite hate and violence. These groups often operate across several platforms. Such cross-platform messaging permits robust action, i.e., espousing diverse messages to different audiences. To understand the extent to which ideological groups pursue robust action, the costs of such action, and when and how groups undertake robust action, we conducted an inductive study of groups identified by the Southern Poverty Law Center and the Armed Conflict Location & Event Data. Our study sheds light on the prevalence and forms of robust action, its cognitive costs, and when and how groups pursue such action. From our findings, we distill implications for policy makers and for law enforcement. Collapse

Topics: Twitter website digital platform social media armed conflict

Methods: case study grounded theory historical review