This study aims to test an integrative model incorporating the impact of organizational learning culture on knowledge sharing and job satisfaction and examine their influence on innovation capabilities and employee retention. Moreover, this study investigates the effect of innovation capabilities and employee retention on organizational sustainability performance. Hypotheses are tested through partial least squares (PLS) analysis of survey data collected from 250 employees in call centers within Jordan’s banking and telecommunications sectors. The quantitative data analysis confirms the hypotheses and reveals positive correlations between organizational learning culture, knowledge sharing, and job satisfaction. Moreover, knowledge sharing positively influences job satisfaction and innovation capability. Job satisfaction, in turn, enhances innovation capability and employee retention. Notably, innovation capability and employee retention directly improve sustainability performance. The study underscores the importance of fostering an organizational learning culture, facilitating knowledge sharing, and ensuring employee satisfaction to retain employees, sustain innovative capabilities, and drive sustainability performance.

This study investigates the impact of pre-COVID-19 organizational ambidexterity, digital capabilities, and organizational resilience on firms’ innovation behavior post the second COVID-19 wave. Utilizing World Bank business surveys from 2019 and COVID follow-up surveys in 2020 and 2021 across 21 countries (8,928 firms), we employ partial least squares structural equation modeling and necessary condition analysis. Findings reveal that organizational ambidexterity and digital capabilities positively influence innovation. Organizational ambidexterity indirectly impacts innovation through digital capabilities. The study emphasizes the crucial role of pre-COVID-19 organizational ambidexterity in catalyzing innovation, highlighting its synergy with digital capabilities. Recognizing and cultivating organizational ambidexterity, along with enhancing digital capabilities, are recommended for firms seeking competitive advantages. The study underscores the importance of leaders fostering ambidexterity and knowledge-sharing practices to drive innovation.

This study aims to identify emerging topics, themes, and potential areas for applying large language models (LLMs) in supply chain management through data triangulation. This study involved the synthesis of 33 published articles and a total of 3421 social media documents, including tweets, posts, expert opinions, and industry reports on utilizing LLMs in supply chain management. By employing BERT models, four core themes were derived: Supply chain optimization, supply chain risk and security management, supply chain knowledge management, and automated contract intelligence, which provides the present status of LLM in the supply chain. The results of this study will empower managers to identify prospective applications and areas for improvement, affording them a comprehensive understanding of the antecedents, decisions, and outcomes detailed in the framework. The insights garnered from this study are highly valuable to both researchers and managers, equipping them to harness the latest advancements in LLM technology and its role within supply chain management.

Every year brings numerous security breaches that lead to highly destructive ransomware attacks, data leaks, and reputational damage to governments, companies, and other organizations around the world. As a result, there is a growing need to ensure that workers comply with critical policies put in place to avoid such incidents. This study investigated how factors from social bond theory and involvement theory affected compliance with information security policies and procedures. All of the factors examined were found to have a significant influence on attitudes about compliance, and attitude had a significant impact on intention to comply. The findings of this study revealed that it is vital to raise employees' awareness about compliance with security policies by improving their information security behavior. Moreover, all the factors were found to have a significant influence on the attitude of employees towards compliance with their organizational information security policies and procedures.

The rapid development of Fintech (financial technology) urges Fintech platforms to better understand users’ perceptions and behaviors toward Fintech use. Based on the theoretical framework of trust, risk perception, platform governance, and information system continuous use theories, we construct a model to explore how users’ different trust perceptions on Fintech platforms affect their risk perception, platform governance perception, and continuance intention of using Fintech. Our findings indicate users’ institutional trust, technology trust, and interpersonal trust all impact their risk perception, perception of Fintech platform governance, and continuance intention to use Fintech in different ways and magnitudes. Furthermore, platform governance perception mediates between institutional trust, interpersonal trust, and continuance intention. Our research helps the practitioners of Fintech platforms attract and retain users in the highly competitive market and provides a reference for the governance of their platforms.

This paper comparatively studies people’s opinions on AI privacy between the US and China. Based on data collected from Twitter and Weibo, we perform text clustering and content analysis to classify opinion types and analyze the symptoms of opinion polarization and drivers by regression analysis. Results show that US people express more concerns about AI privacy, focusing more on privacy disclosure by AI applications. In contrast, Chinese people are more optimistic about AI’s role in promoting privacy protection. Security, economics, and application are driving factors leading to the polarization of the US people, while technologies and algorithms influence the polarization of Chinese people. This study offers methodological guidance for examining the relationship between AI and user privacy. It also guides the government agencies and other practitioners in developing the policies on AI regulations for privacy protection.

The acquisition and sharing of reviews have significant ramifications for the selection of crowdsourcing designs before mass production. This article studies the optimal decision of a brand enterprise regarding the acquisition/sharing of crowdsourcing design reviews in a supply chain. The authors consider an analytical model where the brand enterprise can privately acquire the manufacturer's review (MR) of crowdsourcing product designs and choose one of two information-sharing schemes—optional or mandatory sharing—to disclose MR to the key opinion leaders (KOLs), which help them to produce fans' reviews (FR). MR and FR integrate into the joint reviews (JR) that impact prospective consumers' purchase intention. The authors find that mandatory sharing significantly harms the brand enterprise's motivation to obtain MR, yet optional sharing is conducive to boosting JR on crowdsourcing designs. In addition, JR has a ceiling value, implying that excessively high FR and MR could not always enhance the effect of JR on crowdsourcing designs.

Artificial intelligence-powered chatbot usage continues to grow worldwide, and there is ongoing research to identify features that maximize the utility of chatbots. This study uses the multi-criteria decision-making (MCDM) method to find the best available alternative chatbot for task completion. We identify chatbot evaluation criteria from literature followed by inputs from experts using the Delphi method. We apply CRITIC to evaluate the relative importance of the specified criteria. Finally, we list popular alternatives of chatbots and features offered and apply WASPAS and EDAS techniques to rank the available alternatives. The alternatives explored in this study include YOU, ChatGPT, PerplexityAI, ChatSonic, and CharacterAI. Both methods yield identical results in ranking, with ChatGPT emerging as the most preferred alternative based on the criteria identified.

Outlier detection is currently applied in many fields, where existing research focuses on improving imbalanced data or enhancing classification accuracy. In the financial area, financial fraud detection puts higher demands on real-time and interpretability. This paper attempts to develop a credit risk model for financial fraud detection based on an extreme gradient boosting tree (XGBoost). SMOTE is adopted to deal with imbalanced data. AUC is the assessment indicator, and the running time is taken as the reference to compare with other frequently used classification algorithms. The results indicate that the method proposed by this paper performs better than others. At the same time, XGBoost can obtain a ranking of important features that impact the classification results when performing classification tasks, making the evaluation results of the model interpretable. The above shows that the model proposed in the paper is more practical in solving credit risk assessment problems. It has faster response times, reduced costs, and better interpretability.

From the perspective of the inhibition of privacy computing theory, taking user satisfaction as risk perception, this paper discusses how the readability of financial APP privacy policies inhibits user satisfaction, thus affecting enterprise performance. Firstly, this paper measures the readability of privacy policies by constructing a professional vocabulary and an improved Fog Index formula. Secondly, this paper collects 615 APP privacy policies and corresponding enterprise data and empirically analyzes the impact of the readability of financial APP privacy policies on enterprise performance. This paper enriches research in the field of financial technology and empirically finds that the readability of financial technology APP privacy policies has a negative impact on enterprise performance, while user satisfaction has a positive impact on enterprise performance. In practice, this paper provides a reference for financial technology enterprises to improve the readability of APP privacy policies to improve user satisfaction and enterprise performance.

Using four types of publicly available datasets and ArcGIS software, the authors identify the spatial characteristics of postgraduate education in China at three scales: comprehensive economic zone, provincial, and city. They also employ geographically weighted regression and ordinary least squares to study the factors influencing the spatial pattern of postgraduate education in Gin at the city scale. The findings show that the number of postgraduate education institutions increases as the longitude of a city increases, but the number decreases from coast to inland. Second, postgraduate education institutions tend to group together in provincial capitals and megacities. Finally, GDP, per capita GDP, population size, local income, and total retail sales of consumer goods significantly impact postgraduate education development. The study contributes to the literature and provides insights for practitioners in promoting urban planning and infrastructure development.

The COVID-19 pandemic has heightened the necessity for pervasive data and system interoperability to manage healthcare information and knowledge. There is an urgent need to better understand the role of interoperability in improving the societal responses to the pandemic. This paper explores data and system interoperability, a very specific area that could contribute to fighting COVID-19. Specifically, the authors propose a unified health information system framework to connect data, systems, and devices to increase interoperability and manage healthcare information and knowledge. A blockchain-based solution is also provided as a recommendation for improving the data and system interoperability in healthcare.

The fast growth in online word of mouth (online WOM) reviews has witnessed their wide applications in assisting customers in their purchases. While many positive factors about these reviews have been identified, biases may result from reviewers’ self-selection behavior. To examine whether online WOM ratings reflect authentic customer purchase feelings, this study focuses on the relation between customer dissatisfaction and negative reviews. First, the reviews are fine grained into feature-opinion pairs, after which three explanatory variables are designed to represent the individual, collective, and comprehensive (individual and collective) complaints, with the online rating being considered as the explained variable. Finally, a simple linear regression model is built to determine the relation between customer dissatisfaction (represented by discounting marks of ratings) and negative reviews. Our analysis finds that personal complaints have a positive effect on rating and the most frequent customer complaints have no significant effect on customer ratings.

A new type of coronavirus (COVID-19), detected at the end of December 2019 in Wuhan, China, can pass from person to person, spreading very quickly. The COVID-19 outbreak has created stress among societies. This study aims to evaluate the usability of Google Trends data in predicting and modeling the COVID-19 outbreak and the attitudes of different societies to it by using an infodemiological method. The authors collected the search words related to coronavirus and their relative search volume (RSV) from 11 different countries affected by the COVID-19 outbreak from Google Trends. A positive correlation was found between the trend rate of the words searched on the internet and the number of COVID-19 cases in countries related to the COVID-19 outbreak (p<0.05). There was a significant difference between 11 country societies in the daily RSV for the COVID-19 outbreak (p<0.05). The Turkish, South Korean, Iranian, and Swiss society have searched more intensely on the internet for COVID-19 than others. The research shows that Google Trends data can be used to build the forecast model for case numbers in the COVID-19 outbreak. Besides, Google Trends data provides information about different societies' attitudes in the COVID-19 outbreak.