International Journal of Information Management Data Insights最新文献

The research conducted in Thailand has illuminated the intricate interplay among digital literacy, an agile mindset, design thinking skills, and management control competency among young accountants. Employing a robust questionnaire constructed based on an extensive literature review and validated through a pilot test, the study engaged 450 participants, with a predominant representation of females (79.8 %) and males (18.7 %). Leveraging PLS-SEM analysis to examine the relationships between these variables, the research elucidates that both digital literacy and an agile mindset exert substantial influence over the cultivation of design thinking skills and management control competencies. Accountants who possess a high level of digital knowledge and embrace agile thinking demonstrate a remarkable capacity to effectively apply these proficiencies. Consequently, the study emphasizes the critical imperative of fostering and nurturing digital knowledge and agility among the emerging generation of accountants, equipping them to adeptly confront the multifaceted challenges posed by the digital era. It is worth noting that while the study reveals close relationships among Digital Literacy, Agile Mindset, Design Thinking Skill, and Management Control Competency, the strength of each relationship exhibits variations. Additionally, design thinking skills also play a partial mediating role when establishing the indirect effect on the digital literacy-management control competency and agile mindset-management control competency nexus—a facet that merits further exploration and investigation in future research endeavours.

Environmental stewardship and sustainability have become critical priorities in the contemporary business environment. Corporations are integrating sustainable practices at the business process level via Sustainable Enterprise Resource Planning (S-ERP). However, a recognised shortfall of S-ERP systems lies in their potential inability to integrate sustainability metrics across all business functions holistically. To navigate this limitation, our study introduces the application of Transaction Cost Theory (TCT). By treating business processes as input-output systems, we apply this theory to quantify sustainability likelihood and overall losses. This novel approach bridges the gap, allowing for the comprehensive integration of sustainability metrics across all processes. The essence of our methodology is to leverage static input data collected by S-ERP regarding environmental impact and to extrapolate this data to provide a broader understanding of potential losses and gains. We've tested and validated this approach through two comprehensive case studies; one is about sustainable product design and development, and the other is about the sustainability evaluation of modular versus conventional construction methods. The results inform the formulation of robust sustainability policies at the business process level for systems akin to S-ERP, paving the way for more sustainable business practices.

Utilizing the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework, this research delved into the behavioral intentions and actual usage of fantasy sports gaming apps. The moderating influence of celebrity trustworthiness and risk propensity is also explored. The proposed hypotheses were tested using responses from a substantial sample of 460 sports fantasy gaming app users. The research deployed a mixed-method approach, combining both covariance-based and partial-least square structural equation modeling tech- niques. The investigation findings revealed an influence on behavioral intentions and actual use. Specifically, performance expectancy, hedonic motivation, habit, and social influence emerged as significant determinants affecting users’ intentions and behaviors. The study indicates a significant moderating role of celebrity trustworthiness on the relationship be- tween performance expectancy, hedonic motivation, habit, and social influence concerning behavioral intentions. Risk propensity moderated the relationship between behavioral inten- tions and actual usage behavior. As a contribution to the expanding literature, the research extended the application of the UTAUT2 framework to the context of sports fantasy gaming apps. The research also provides valuable insights for researchers and professionals associ- ated with sports fantasy gaming apps and future research directions.

Opinion summarization and sentiment classification are key processes for understanding, analyzing, and leveraging information from customer opinions. The rapid and ceaseless increase in big data of reviews on e-commerce platforms, social media, or review portals becomes a stimulus for the automation of these processes. In recent years, deep transfer learning has opted to solve many challenging tasks in Natural Language Processing (NLP) relieving the hassles of exhaustive training and the requirement of extensive labelled datasets. In this work, we propose frameworks for Abstractive Summarization (ABS) and Sentiment Analysis (SA) of airline reviews using Pretrained Language Models (PLM). The abstractive summarization model goes through two finetuning stages, the first one, for domain adaptation and the second one, for final task learning. Several studies in the literature empirically demonstrate that review rating has a positive correlation with sentiment valence. For the sentiment classification framework, we used the rating value as a signal to determine the review sentiment, and the model is built on top of BERT (Bidirectional Encoder Representations from Transformers) architecture. We evaluated our models comprehensively with multiple metrics. Our results indicate competitive performance of the models in terms of most of the evaluation metrics.

In this era of Big Data and the advancement of sophisticated analytical techniques, the financial industry has the capacity to implement innovative technologies within their systems to derive crucial insights about their clientele and vigilantly monitor their activities. This landscape has seen the emergence and rise of two significant applications, namely, customer segmentation systems and early warning systems. Therefore, this study presents a systematic literature review on the automation of customer segmentation and early warning techniques with a focus on managing credit portfolio entities. The research delves into a multitude of scholarly articles from three distinct perspectives: charting the dominant trends within the literature, unpacking the overarching themes, and critically examining the integration of early warning signals within customer clustering applications. Furthermore, the review reveals a noticeable dearth of studies probing the synergistic application of these two systems. Despite their independent effectiveness in risk management and targeted marketing strategies respectively, an integrated approach holds potential for bolstering financial stability and tailoring customer service. Thus, this review stands as a significant academic contribution, advocating an integrated application of these systems within the financial industry. The findings provide a novel foundation for future research and practical applications, potentially redefining strategies within the financial sector.

This research aimed at examining factors influencing college students to adopt mobile learning (m-learning) services. An integrated model combined the information systems success (ISS) and Unified Theory of Acceptance and Use of Technology (UTAUT2), was developed to identify m-learning determinants. A sample of 552 was recruited to test hypotheses using structural equation modeling (SEM). The significant factors explained 70 % of the variance toward Behavioral Intention (BI) based on SEM results. While price value (PV), effort expectancy (EE), performance expectancy (PE), and privacy (PR) were not significant predictors of BI, the results of the neural network model ranked the predictive power of the factors in the following order: information quality, habit (HB), system quality (SYQ), hedonic motivation (HM), facilitating condition (FC), and social influence (SI), positively influenced m-learning adoption. The findings of this study helps the policy makers at higher educational institutions to formulate strategies to enhance students’ learning experience in upcoming crises and place a focus on sustainable mobile learning environment.

The study aims to investigate the factors shape digital entrepreneurial intention based on the application of the Social Cognitive Career theory. A sample of 345 engineering students and the structural equation modeling method are used to provides a better understanding of the factors (attitude toward digital entrepreneurship, digital entrepreneurial self-efficacy, digital entrepreneurial knowledge, and digital entrepreneurial education) that motivate engineering students to engage in digital entrepreneurship. The results indicate that attitude toward digital entrepreneurship significantly and positively impacts the digital entrepreneurship intention. Additionally, digital entrepreneurial self-efficacy plays a crucial role in fostering a positive attitude toward digital entrepreneurship. The study highlights the importance of digital entrepreneurial knowledge, which positively affects both self-efficacy and attitude toward digital entrepreneurship. The finding emphasizes the significance of digital entrepreneurial education in enhancing knowledge and digital entrepreneurial self-efficacy. The results are anticipated to offer both theoretical and practical insights into the factors encouraging digital entrepreneurial intentions.

The purpose of this paper is twofold: (i) to identify the thematic categories and central topics connecting digitization and operational efficiency in the banking sector; and (ii) to develop a research agenda to guide future research endeavors to advance knowledge related to digital operational efficiency (DOE). Employing a two-stage content-centric review approach, this study comprehensively outlines overarching themes and subsequently delves into specific dimensions. The first stage aimed to provide an overview of research connecting operational efficiency and digitization in the banking sector and to refine the search criteria for content analysis. The second stage employed content analysis concentrated on journal articles published between 2018 and 2023. Central topics include enhancing banking industry performance through industry 4.0 technology and partnerships, the impact of the COVID-19 pandemic on bank digital transformation, organizational adaptations for embracing new digital business models, and elevating customer experience through novel operational paradigms. The research agenda proposed include the following: DOE conceptual development; digital bank break-even analysis; optimization of the product and service portfolio offered by digital banks; and consumer experience and level of service of digital banks. By delving into the impact of industry 4.0 technologies, pandemic, and digital business models, this paper enriches our understanding of the complex interplay between digitization and operational efficiency. The proposed research agenda offers a roadmap for future scholarly endeavors, contributing to the evolution of theoretical frameworks in this field. This paper offers insights relevant for banking industry professionals, providing them with valuable guidance on how to enhance their performance, optimize their digital product and service portfolio, and elevate the overall customer experience in the rapidly evolving landscape of digital banking.

Large language models (LLMs) have received considerable interest in the field of natural language processing (NLP) owing to their remarkable ability to generate clear, consistent, and contextually relevant materials. Among the numerous LLMs, ChatGPT (Generative Pre-trained Transformer for Chatbots) is emerging as a prominent prospective tool for developing conversational agents such as chatbots. However, there is a need for a clear conceptual understanding of ChatGPT's potential implications for the industry and its role in marketing. This study explores the adoption of ChatGPT in marketing and examines theories that may influence its adoption by marketers and consumers, as well as its implications for marketers. This study discusses how ChatGPT may allow for more personalized and engaging content, better customer experience, and improved ROI. However, adoption also brings challenges, including ethical considerations and the need for new skill development. This study also discusses future research opportunities for the adoption of ChatGPT and other generative artificial intelligence technologies in marketing. The goal is to provide insights for organizations that consider implementing these technologies, and to contribute to the literature on the adoption of Artificial Intelligence (AI) and the use of Generative AI in marketing.

Bayesian network is a probabilistic graphical model within machine learning that supports decision-making under conditions of uncertainty in different domains. Although the scientific literature has increasingly addressed the implementation of Bayesian networks to support management practices (BNM), a thorough review is currently lacking. This bibliometric review investigates the transformative potential of Bayesian networks in reshaping decision-making paradigms across multidisciplinary domains. The knowledge set findings reveal a predominant focus on risk management within the Engineering domain; the scientific openings involve significant progress in both theoretical frameworks and practical applications across Computer Science, Engineering, Medicine, and Environmental Science; and research trends indicate a progressive BNM within Engineering and Medicine, contrasting with a decline in innovative studies related to Computer Science. This study acts as a catalyst, propelling inventive BNM applications and fostering interdisciplinary advancements. It lays a foundation for pioneering BNM strategies.