All External Reference Prices Are Not the Same: How Magnitude, Source, and Fairness Shape Payment for Digital Goods

2023 | Information Systems Research | Citations: 0

Authors: Bassellier, Geneviève; Ramaprasad, Jui

Abstract: External reference prices (ERPs) are recognized to influence consumers’ payment ... Expand

Abstract: External reference prices (ERPs) are recognized to influence consumers’ payment for physical goods. We examine the role of ERPs in a digital goods context, emphasizing the importance of disentangling the effects of two components of ERPs: the magnitude and the source. We first examine the impact of the price magnitude and then investigate how the impact of ERP magnitude is moderated by its source―the specific context in which the reference price is set—and by its perceived fairness. Using a laboratory experiment with 471 participants, we first establish that as an ERP increases, payment for digital songs also increases. Furthermore, the source of the ERP motivates payment differently. Indeed, consumers’ willingness to pay is more strongly influenced by a price recommended by the site than by information on the price paid by others. Last, we show that the perceived fairness of the ERP matters. Results indicate that when the ERP is perceived to be high, the marginal effect of an increase in ERP on payment is smaller than when it is perceived to be fair. We contribute to the understanding of the effect of ERPs in digital context, where the source of an ERP can be varied, and perception of fairness can be heterogenous across consumers. This has implications for the design of digital platforms seeking to sell digital goods using a flexible pricing scheme. History: Anandasivam Gopal, Senior Editor; Sunil Wattal, Associate Editor. Funding: This work was supported by the Social Science and Humanities Research Council of Canada [Insight Development Grant 430-2017-01055]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2023.1206. Collapse

Topics: enterprise resource planning digital good website price management digital platform

Methods: experiment Student's t-test survey regression analysis method laboratory experiment

Theories: behavioral economics

Trust and deception with high stakes: Evidence from the friend or foe dataset

2023 | Decision Support Systems | Citations: 0

Authors: Chen, Xunyu; Wang, Xinran; Spitzley, Lee; Nunamaker, Jay

Abstract: Many social interactions rely on the premise of mutual trust, but deception viol ... Expand

Abstract: Many social interactions rely on the premise of mutual trust, but deception violates trust and poses risk. Empirically examining trust and deception, particularly in high-stakes situations, is challenging but essential for improving the research realism and generalizability. To address this difficulty, we study trusting and deceptive behaviors in a high-stakes situation by using a novel dataset created from an American game show, Friend or Foe (FoF). In the show, a contestant's reward was determined through a trust game modified from the prisoner's dilemma. We explore how numerous human behaviors including facial expressions, gaze, head pose, body motion, language, and socio-demographic attributes, were related to a contestant's trusting or deceptive decision. Using a data-driven approach, we find that the deceivers' (contestants who chose Foe) behavior featured a neutralized face, negative facial emotions, enhanced upper body motion, and language with a lower sense of immediacy and agreeableness. The contestants who chose to trust (chose Friend) exhibited opposite behavioral patterns. Socio-demographic factors such as age, height, and facial attractiveness were also associated with a contestant's choice. Combining multimodal information, machine learning classifiers could predict the contes­ tant's choice with an accuracy about 25% greater than earlier reported human accuracy. We contribute to both trust and deception literature by examining the generalizability of trusting and deceptive behaviors to a new high-stakes scenario. We also add to the decision support literature by showing the superior predictive perfor­ mances of combining behavioral and socio-demographic features. Furthermore, we contribute to the academic community by introducing the FoF dataset. Collapse

Topics: decision support website YouTube Python decision making

Methods: meta analysis support vector machine correlation analysis descriptive statistic random forest classification

The Value of Social Media Customer Support During the Pandemic

2023 | HICSS | Citations: 0

Authors: Cheng, Huai-Tzu; Pan, Yang; Hirschheim, Rudy

Abstract: Many companies are utilizing social media as the primary avenue for customer ser ... Expand

Abstract: Many companies are utilizing social media as the primary avenue for customer service during the pandemic. However, how customers’ behaviors and interactions with customer service agents on social media are impacted by the lockdowns has not been well understood. In this study, we examine the impact of lockdowns and physical distancing on changes in customers’ behaviors, such as emotional expressions in tweets and customers’ satisfaction with social media customer service. Using a difference-in-differences research design, we find that with the lockdowns and physical distancing, customers expressed more negative emotions when tweeting the company they were having issues with. Surprisingly, compared to before the pandemic period, customers’ emotional expressions became more positive and they were more likely to express their satisfaction after interacting with customer service agents. Interestingly, our findings reveal that gender differences exist in these scenarios. We also discuss the theoretical and practical implications of these findings. Collapse

Topics: Twitter customer service social media pandemic customer satisfaction

Methods: experimental group two sample t-test with equal variance propensity score matching parametric test robustness check

A Theory-based Deep-Learning Approach to Detecting Disinformation in Financial Social Media

2023 | Information Systems Frontiers | Citations: 0

Authors: Chung, Wingyan; Zhang, Yinqiang; Pan, Jia

Abstract: The spreading of disinformation in social media threatens cybersecurity and unde ... Expand

Abstract: The spreading of disinformation in social media threatens cybersecurity and undermines market efficiency. Detecting disinformation is challenging due to large volumes of social media content and a rapidly changing environment. This research developed and validated a theory-based, novel deep-learning approach (called TRNN) to disinformation detection. Grounded in social and psychological theories, TRNN uses deep-learning and data-centric augmentation to enhance disinformation detection in financial social media. Temporal and contextual information is encoded as specific knowledge about human-validated disinformation, which was identified from our unique collection of 745,139 financial social media messages about four U.S. high-tech company stocks and their fine-grained trading data. TRNN uses multiple series of long short-term memory (LSTM) recurrent neurons to learn dynamic and hidden patterns to support disinformation detection. Our experimental findings show that TRNN significantly outperformed widely-used machine learning techniques in terms of precision, recall, F-score and accuracy, achieving consistently better classification performance in disinformation detection. A case study of Apple Inc.’s stock price movement demonstrates the potential usability of TRNN for secure knowledge management. The research contributes to developing novel approach and model, producing new information systems artifacts and dataset, and providing empirical findings of detecting online disinformation. Collapse

Topics: disinformation social media Twitter fake news usability

Methods: machine learning experiment design artifact deep learning recurrent neural network

Theories: psychological theory

Understanding Employee Perspectives During an Acquisition – Case of Twitter

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Dwivedi, Rahul

Abstract: Employee reviews offer valuable insights into the organizational culture of the ... Expand

Abstract: Employee reviews offer valuable insights into the organizational culture of the employer. Two important facets of such reviews are the topics addressed by the employees and the underlying sentiments. For this research, one year of employee reviews for Twitter was collected from Glassdoor. The goal is to extract the topics and underlying employee sentiments across three time periods (before the acquisition, after the acquisition announcement, and after the acquisition) in view of the recent privatization. Based on the findings from topic models, variations in topics discussed by the employees were found across different time periods. Statistically significant differences were also found in the sentiments of Twitter employees across the three periods as expressed in the reviews. The study can help business organizations to understand the employee's perspectives towards them during a drastic contextual event such as acquisition, thus helping them to identify areas of improvement. Collapse

Topics: Twitter website mergers and acquisition human resource management pandemic

Methods: product review sentiment analysis longitudinal survey longitudinal research two sample t-test with equal variance

The Power of Identity Cues in Text-Based Customer Service: Evidence from Twitter

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Gao, Yang; Rui, Huaxia; Sun, Shujing

Abstract: Text-based customer service is emerging as an important channel through which c ... Expand

Abstract: Text-based customer service is emerging as an important channel through which companies can assist customers. However, the use of few identity cues may cause customers to feel limited social presence and even suspect the human identity of agents, especially in the current age of advanced algorithms. Does such a lack of social presence affect service interactions? We studied this timely question by evaluating the impact of customers' perceived social presence on service outcomes and customers' attitudes toward agents. Our identification strategy hinged on Southwest Airlines' sudden requirement to include a first name in response to service requests on Twitter, which enhanced customers' perceived level of social presence. This change led customers to become more willing to engage and more likely to reach a resolution upon engagement. We further conducted a randomized experiment to understand the underlying mechanisms. We found that the effects were mainly driven by customers who were ex ante uncertain or suspicious about the human identity of agents, and the presence of identity cues improved service outcomes by enhancing customers' perceived levels of trust and empathy. Additionally, we found no evidence of elevated verbal aggression from customers toward agents with identity cues, although a mechanism test revealed the moderating role of customers' emotional states. Our study highlights the importance of social presence in text-based customer service and suggests a readily available and almost costless strategy for firms: signal humanization through identity cues. Collapse

Topics: customer service Twitter social presence social media customer satisfaction

Methods: experiment computational algorithm synthetic control method survey support vector machine

Theories: social presence theory big five model

Addressing Learners' Heterogeneity in Higher Education: An Explainable AI-based Feedback Artifact for Digital Learning Environments

2023 | International Conference on Business Informatics | Citations: 0

Authors: Haag, Felix; Günther, Sebastian A.; Hopf, Konstantin; Handschuh, Philipp; Klose, Maria; Staake, Thorsten

Abstract: ... Expand

Abstract: Collapse

Topics: explainable artificial intelligence online learning system analytical information system project management IT project management

Methods: design science survey design principle design artifact two sample t-test with equal variance

Market Valuation of Artificial Intelligence Implementation Announcements

2023 | ACM SIGMIS Database | Citations: 0

Authors: Huang, Cheng-Kui (Tony); Lee, Yu-Tsung

Abstract: With the advent of artificial intelligence (AI), more and more enterprises have ... Expand

Abstract: With the advent of artificial intelligence (AI), more and more enterprises have begun developing AI to promote their own competitive advantage and increase their business value. In order to reveal the value-added results of AI implementation, this study adopts an event study methodology to capture the abnormal returns resulting from the announcement of AI implementation. Based on the empirical results of this study, in response to the announcement of AI implementation, we find significant positive abnormal returns on the event day. With respect to the grouping analysis, the results show significant differences in average cumulative abnormal returns between announcements with detailed information and those without detailed information. In addition, our findings present significant differences in average cumulative abnormal returns between IT-companies and non-IT companies on the event day. Ultimately, according to the content analysis, only one characteristic, the frequency of negative words, is modestly and negatively correlated with average cumulative abnormal returns. Collapse

Topics: artificial intelligence organizational value robotic information management Java

Methods: regression analysis method event study longitudinal research qualitative content analysis descriptive statistic

Theories: signaling theory

Artificial intelligence (AI)-enabled CRM capability in healthcare: The impact on service innovation

2023 | International Journal of Information Management | Citations: 0

Authors: Kumar, Pradeep; Sharma, Sujeet Kumar; Dutot, Vincent

Abstract: Although AI-enabled customer relationship management (CRM) systems have gained m ... Expand

Abstract: Although AI-enabled customer relationship management (CRM) systems have gained momentum in healthcare to enhance performance, there is a striking dearth of knowledge on how such capabilities are formed and affect service innovation. The study adopted a mixed-method approach to investigate the underlying phenomena. This research infused resource-based theory, dynamic capability theory, and theory of productivity paradox to investigate how healthcare in India acquires AI-enabled CRM capabilities and enhances service innovation. We identified the facets of AI-enabled CRM capabilities using a case study and developed a framework for AI-enabled CRM capability and service innovation. This study noticed that customer service flexibility (CSF) is a missing link in this relationship. The findings of the quantitative study employing PLS-SEM reveal the linear relationships between AI-enabled CRM capability, CSF, and service innovation. This study explains the formation of AIenabled CRM capabilities to fill the research gap and direct innovative performance in healthcare, which is an immediate need to sustain in a volatile environment. This study provides theoretical implications to enhance the research stream and practical implications for decision-makers. Collapse

Topics: artificial intelligence innovation management business process innovation customer service productivity

Methods: partial least squares regression survey qualitative interview survey design mixed method

Theories: dynamic capabilities theory resource based view of the firm

All Together Now: Fuzzy-set Qualitative Comparative Analysis of Personality Profiles and their Coping with Technostress

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Peters, Leonore; Feste, Jasmin Maria

Abstract: The Big Five personality inventory is applied in various research contexts to p ... Expand

Abstract: The Big Five personality inventory is applied in various research contexts to provide explanations for individuals' behavior. Nevertheless, approaches that consider personality profiles as a combination of different personality dimensions are rare. Drawing on configuration theory, we aim to address these research gaps by providing insights on configurations of personality profiles and how they affect coping behavior in response to experiencing technostress. This study uses survey data from 233 respondents and conducts a fuzzy-set qualitative comparative analysis to identify personality profiles that pursue a proactive and reactive coping strategy respectively in response to experiencing technostress. We reveal four configurations in the analysis per coping strategy representing distinct personality profiles. Thereby, we contribute to personality and coping research within the context of technostress in e-commerce. We extend the research by taking a holistic perspective focusing on the interplay of the Big Five dimensions. Collapse

Topics: technostress personality electronic commerce fuzzy logic accounting

Methods: experiment survey fuzzy set qualitative comparative analysis experimental design experimental group

Theories: big five model fuzzy set theory