Trust in Online Ride-Sharing Transactions: Impacts of Heterogeneous Order Features

2023 | Journal of Management Information Systems | Citations: 0

Authors: Cheng, Xusen; Fu, Shixuan; Sun, Jianshan; Zuo, Meiyun; Meng, Xiangsong

Abstract: With the development of the sharing economy, online ride-sharing has become a pr ... Expand

Abstract: With the development of the sharing economy, online ride-sharing has become a primary form of commuting. Using secondary transaction data, this study investigates the associations between the heterogeneous features and mutual trust in sharing economy-driven online ride-sharing transactions. Based on an examination of 12,404 ride-sharing orders in Beijing, we propose a set of trust distribution maps using order location data to reveal heterogeneous spatial patterns of the relationship between online ride-sharing transactions and mutual trust. The results show that the historical order completion rate and order distance are positively associated with mutual trust in ride-sharing transactions, whereas order time and departure density negatively and significantly influence mutual trust. Furthermore, we use machine learning algorithms to predict trust. The implications for theory and practice and future research directions are discussed. Collapse

Topics: ride sharing platform sharing platform logistics management geographic information system online review

Methods: machine learning computational algorithm regression analysis method experiment exploratory data analysis

Theories: signaling theory

DIGITAL TECHNOLOGY-ENABLED DYNAMIC CAPABILITIES, ACTION SPEED, AND ACTION VOLUME: THE MEDIATING EFFECT OF STRATEGIC RENEWAL

2023 | European Conference On Information Systems | Citations: 0

Authors: Hüneke, Jacqueline; Greven, Andrea

Abstract: The information systems research stream on dynamic capabilities (DCs) considers ... Expand

Abstract: The information systems research stream on dynamic capabilities (DCs) considers how technology impacts strategic management at the organizational level in the turbulent environment evoked by digital transformation. However, the underlying mechanisms of how organizations use technologies effectively to increase their competitive advantage through competitive actions is underexplored. This paper seeks to empirically investigate the impact of digital technology-enabled DCs on action speed and action volume, two indicators of competitive aggressiveness. Furthermore, we propose the mediating role of strategic renewal. Our PLS-SEM analysis based on survey data from 174 German business units indicates that digital technology-enabled DCs directly enhance strategic renewal, action speed, and action volume. We also detect a partial mediating effect of strategic renewal on the relationship between digital technology-enabled DCs and action volume. This study offers insights into the underlying mechanisms that may allow organizations to enhance their competitive aggressiveness through the application of digital technologies. Collapse

Topics: digital transformation competitive advantage strategic management IT productivity technological change

Methods: survey PLS tool instrumental variables estimation partial least squares path modeling survey design

Theories: learning theory

Impact of irritation and negative emotions on the performance of voice assistants: Netting dissatisfied customers’ perspectives

2023 | International Journal of Information Management | Citations: 0

Authors: Jain, Shilpi; Basu, Sriparna; Ray, Arghya; Das, Ronnie

Abstract: With the rising popularity of voice assistants like Alexa, Siri, etc., understan ... Expand

Abstract: With the rising popularity of voice assistants like Alexa, Siri, etc., understanding the service gaps experienced by customers has become imperative. In unison, limited studies are focusing on the perspectives of dissatisfied customers. This research aims to address this gap by suggesting the antecedents of technology irritation and its impact on the overall performance of voice assistants. The study uses a mixed-methods approach by analyzing the data gathered from user interviews, online reviews from users, and also empirical surveys. The qualitative study, capturing both etic and emic perspectives, found problems related to responsiveness, usability, platform, connectivity, and compatibility of VAs. While the empirical study revealed that utility gratification and service quality have a significant impact on technology irritation. The findings also show that customers from negative high and low arousal emotion groups have varying perceptions about utility gratifications. Collapse

Topics: service quality online review usability system quality data quality

Methods: qualitative interview survey conceptual modelling partial least squares regression qualitative content analysis

Theories: information systems success model technology acceptance model uses and gratification theory

sDTM: A Supervised Bayesian Deep Topic Model for Text Analytics

2023 | Information Systems Research | Citations: 0

Authors: Yang, Yi; Zhang, Kunpeng; Fan, Yangyang

Abstract: Topic modeling methods such as latent Dirichlet allocation (LDA) are powerful to ... Expand

Abstract: Topic modeling methods such as latent Dirichlet allocation (LDA) are powerful tools for analyzing massive amounts of textual data. They have been used extensively in information systems (IS) and business discipline research to identify latent topics for data exploration and as a feature engineering mechanism to derive new variables for analyses. However, existing topic modeling approaches are mostly unsupervised and only leverage textual data, while ignoring additional useful metadata often associated with text, such as star ratings in customer reviews or categories of posts in online forums. As a result, the identified topics and variables derived based on the learned topic model may not be accurate, which could lead to incorrect estimations that affect subsequent empirical analysis and to inferior performance on predictive tasks. In this study, we propose a novel supervised deep topic modeling approach called sDTM, which combines a neural variational autoencoder model and a recurrent neural network. sDTM leverages the auxiliary data associated with text to enhance the topic modeling capability. We conduct empirical case studies and predictive analytics on an online consumer review data set and an online knowledge community data set. Experimental results show that in comparison with benchmark methods, sDTM can enhance both the empirical estimation and predictive performance. sDTM makes methodological contributions to the IS literature and has direct relevance for research using text analytics.History: Alok Gupta, Senior Editor; Huimin Zhao, Associate Editor.Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1124. Collapse

Topics: online review open source user-generated content Wikipedia accounting

Methods: topic model deep learning machine learning BERT data transformation

Usage Continuance in Software-as-a-Service

2022 | Information Systems Frontiers | Citations: 0

Authors: Baumann, Elias; Kern, Jana; Lessmann, Stefan

Abstract: Software-as-a-service applications are experiencing immense growth as their comp ... Expand

Abstract: Software-as-a-service applications are experiencing immense growth as their comparatively low cost makes them an important alternative to traditional software. Following the initial adoption phase, vendors are now concerned with the continued usage of their software. To analyze the influence of different measures to improve continued usage over time, a longitudinal study approach using data from a SaaS vendor was implemented. Employing a linear mixed model, the study finds several measures to have a positive effect on a software’s usage penetration. In addition to these activation measures performed by the SaaS vendor, software as well as client characteristics were also examined, but did not display significant estimates. The findings emphasize the need for proactive activation initiatives to raise usage penetration. More generally, the study contributes novel insights into the scarcely researched field of influencing factors on SaaS usage continuance. Collapse

Topics: software as a service knowledge management usage intention single sign on intranet

Methods: longitudinal research hierarchical linear modeling qualitative interview survey literature study

Opening the Black-Box of AI: Challenging Pattern Robustness and Improving Theorizing through Explainable AI Methods

2022 | International Conference on Information Systems | Citations: 0

Authors: Stoffels, Dominik; Faltermaier, Stefan; Strunk, Kim; Fiedler, Marina

Abstract: Machine Learning (ML) algorithms, as approach to Artificial Intelligence (AI), ... Expand

Abstract: Machine Learning (ML) algorithms, as approach to Artificial Intelligence (AI), show unprecedented analytical capabilities and tremendous potential for pattern detection in large data sets. Despite researchers showing great interest in these methodologies, ML remains largely underutilized, because the algorithms are a black-box, preventing the interpretation of learned models. Recent research on explainable artificial intelligence (XAI) sheds light on these models by allowing researchers to identify the main determinants of a prediction through post-hoc analyses. Thereby, XAI affords the opportunity to critically reflect on identified patterns, offering the opportunity to enhance decision making and theorizing based on these patterns. Based on two large and publicly available data sets, we show that different variables within the same data set can generate models with similar predictive accuracy. In exploring this issue, we develop guidelines and recommendations for the effective use of XAI in research and particularly for theorizing from identified patterns. Collapse

Topics: explainable artificial intelligence shapley additive explanation sensemaking artificial intelligence data quality

Methods: theory development machine learning computational algorithm support vector machine survey

Theories: information systems theory information theory

Orchestrating big data analytics capability for sustainability: A study of air pollution management in China

2022 | Information & Management | Citations: 0

Authors: Zhang, Dan; Pan, Shan L.; Yu, Jiaxin; Liu, Wenyuan

Abstract: Under rapid urbanization, cities are facing many societal challenges that impede ... Expand

Abstract: Under rapid urbanization, cities are facing many societal challenges that impede sustainability. Big data analytics (BDA) gives cities unprecedented potential to address these issues. As BDA is still a new concept, there is limited knowledge on how to apply BDA in a sustainability context. Thus, this study investigates a case using BDA for sustainability, adopting the resource orchestration perspective. A process model is generated, which provides novel insights into three aspects: data resource orchestration, BDA capability development, and big data value creation. This study benefits both researchers and practitioners by contributing to theoretical developments as well as by providing practical insights. Collapse

Topics: big data value creation data quality usability analytical information system

Methods: case study qualitative interview machine learning business process modeling qualitative coding

Theories: resource based view of the firm

Engaging Gen Y Customers in Online Brand Communities: A Cross-National Assessment

2021 | International Journal of Information Management | Citations: 0

Authors: Carlson, Jamie; Rahman, Syed Mahmudur; Rahman, Mohammad M.; Wyllie, Jessica; Voola, Ranjit

Abstract: Global retail brands find it vital to engage Generation Y (Gen Y) customers thro ... Expand

Abstract: Global retail brands find it vital to engage Generation Y (Gen Y) customers through firm-hosted online brand communities (OBCs) for generating actionable insights; this study advances customer engagement (CE) frame­ works in this setting. First, we draw on the stimulus–organism–response (S–O–R) theory to introduce OBC website quality (termed OBC-SiteQUAL), alongside brand involvement as a customer resource input, to shape an expanded quadripartite CE conceptualisation. Second, using multi-country data, we test the validity of the OBCSiteQUAL and CE measurement models, and the theoretical framework. Results indicate that the newly conceived OBC-SiteQUAL construct, underpinned by various web-based cues related to affective elements, brand interaction and customer-to-customer interaction, positively influences CE, whereas brand involvement partially influences CE. Third, we examine how these variables translate to behavioural loyalty outcomes. Empirically, a consistent pattern across the country samples indicates that OBC-SiteQUAL can affect behavioural loyalty by enhancing CE. Thus, we reliably confirm the mediating effect of CE (O) between OBC-SiteQUAL (S) and behavioural loyalty (R). Fourth, we found that the impact of OBC-SiteQUAL and brand involvement on CE differs between younger and older Gen Y consumers. These findings inform the optimisation of precise digital content marketing activities to enhance customer-brand relationships. Collapse

Topics: social media marketing management website service quality Facebook

Methods: partial least squares regression PLS tool partial least squares path modeling SEM tool structural equation modeling

Theories: involvement theory

Neighbor-aware review helpfulness prediction

2021 | Decision Support Systems | Citations: 0

Authors: Du, Jiahua; Rong, Jia; Wang, Hua; Zhang, Yanchun

Abstract: Helpfulness prediction techniques have been widely incorporated into online deci ... Expand

Abstract: Helpfulness prediction techniques have been widely incorporated into online decision support systems to identify high-quality reviews. Most current studies on helpfulness prediction assume that a review’s helpfulness only relies on information from itself. In practice, however, consumers hardly process reviews independently because reviews are displayed in sequence; a review is more likely to be affected by its adjacent neighbors in the sequence, which is largely understudied. In this paper, we proposed the first end-to-end neural architecture to capture the missing interaction between reviews and their neighbors. Our model allows for a total of 12 (three selection × four aggregation) schemes that contextualize a review into the context clues learned from its neighbors. We evaluated our model on six domains of real-world online reviews against a series of state-of-the-art baselines. Experimental results confirm the influence of sequential neighbors on reviews and show that our model significantly outperforms the baselines by 1% to 5%. We further revealed how reviews are influenced by their neighbors during helpfulness perception via extensive analysis. The results and findings of our work provide theoretical contributions to the field of review helpfulness prediction and offer insights into practical decision support system design. Collapse

Topics: online review decision support system user-generated content online dating data quality

Methods: experiment GloVe deep learning ablation analysis survey

High-Performance Detection of Mild Cognitive Impairment Using Resting-State EEG Signals Located in Broca’s Area: A Machine Learning Approach

2021 | Americas Conference on Information Systems | Citations: 0

Authors: Gross, Jan; Groiss, Niklas; Rieg, Thilo; Baumgartl, Hermann; Buettner, Ricardo

Abstract: Dementia and Alzheimer's disease represent one of the biggest medical challenge ... Expand

Abstract: Dementia and Alzheimer's disease represent one of the biggest medical challenges of our century, manifesting the risk to individuals of losing their language or selfmanagement skills. It is estimated that by 2050 around 1.2% of the world's population will suffer from these diseases. Since there are no effective treatment options available, it is of great impor tance to detect dementia tendencies at the earliest possible stage. Mild cognitive impairment represents a preclinical stage of Alzheimer's disease, allowing detecting dementia tendencies prematurely. As related work either relies on sen sitive test results, does not manifest a sophisticated level of accuracy, or relies on costly techniques, further research is required. In this study, we propose a machine learning approach using restingstate EEG recordings of channels located in the Broca's area. We achieved a benchmark accuracy of 90.91% in differentiating nonMCI and MCI individuals based on the assessment of phonemic verbal fluency. Collapse

Topics: personality database system health information system data quality

Methods: electroencephalography computational algorithm machine learning magnetic resonance imaging descriptive statistic