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Online review sites and social media platforms have become crucial sources of information for consumers, greatly influencing their purchasing behavior and decision-making, especially in the food and beverage industry. However, not many studies have been carried out to understand how social media influences customers’ purchase intentions at fast-food restaurants. This paper examines the growing influence of social media attributes on purchase intentions, with a focus on the Malaysian context. Previous studies highlight the increasing importance of social media platforms as marketing tools and their impact on customers’ purchase intentions. The research explores the relationship between habit and informativeness and their influence on purchase intentions among young adults in fast-food restaurants. A purposive sampling method was utilized to gather data from 142 fast-food customers through a cross-sectional online survey. The research hypotheses were analyzed using the partial least square structural equation modeling. Key findings found that habit and informativeness have a significant positive impact on customers’ intentions to purchase at fast-food restaurants. The study contributes to the existing literature by highlighting the growing influence of social media on consumer behavior in the context of fast-food restaurants. It adds to our understanding of how social media platforms serve as effective marketing tools, particularly among young adults, and how they can influence purchase intentions. By understanding the interplay of habit, informativeness, and purchase intentions, businesses can develop more effective marketing strategies and foster stronger relationships with their customers.

The study evaluated the opportunities and challenges of e-learning for university students and investigated their experiences. A sequential exploratory mixed-methods approach (quantitative and qualitative) was used. In the quantitative phase, a survey was conducted to explore students' competencies in coping with e-learning attributes, involving 237 university students (46.9% male, 53.1% female). Descriptive and analytical tests were used to analyze the data. The results indicated the mean scores of students' perspectives on the opportunities and challenges of e-learning in university were 4.05 ± 0.49 out of 5. In the qualitative phase, data were collected through semi-structured interviews. To provide a richer context and better understanding and interpretation of the quantitative findings, the current research employed qualitative research methodologies, including focus group discussions with ten interviewees—five academic staff members and five students. Combining both student and academic staff perspectives provides a more comprehensive understanding of the research topic. Students and staff may have different viewpoints, experiences, and needs related to the subject matter. The qualitative analysis identified five significant themes: communication defects, technical challenges, personal-level challenges, curricular-level issues, and social challenges. The study's findings may be utilized to design better policies and strategies to enhance e-learning and address its issues among both instructors and students. Finally, the study provides implications for relevant stakeholders

Eco-friendly product usage is a defining trend in consumer purchase intentions during this decade. Younger, tech-savvy consumers are often more sensitive to environmental concerns when purchasing products due to the abundance of information available on social media platforms. In the food and beverage industry, this trend has led many younger consumers to prefer purchasing eco-friendly, sustainable cutlery for food and beverage consumption. With online social commerce platforms selling eco-friendly utensils readily available, a larger group of consumers, primarily young adults, seem to purchase these products impulsively due to various factors. The four independent variables proposed in this study that seem to influence the online impulse buying behavior of young adults toward these eco-friendly products are fear, serendipity, electronic word of mouth (eWOM), and website quality. Convenience sampling was utilized to gain a greater understanding of the targeted consumer group, using a 5-item Likert scale to collect data from respondents. The validity and reliability of the questionnaire items were further confirmed using statistical methods, namely average variance extracted (AVE), composite reliability (CR), and Cronbach’s alpha. The hypotheses were tested using AMOS software (version 24) through confirmatory factor analysis (CFA) to tabulate the results. Based on the study conducted, it is concluded that fear and eWOM do influence the online impulse buying of eco-friendly food utensils among young adults in Kuala Lumpur. It is further suggested that elements such as fear and eWOM play a significant role in online impulse purchases of eco-friendly utensils, as young adults rely heavily on social media marketing. This reliance contributes to these consumers’ growing concern for their future, prompting them to be more environmentally conscious.

The finance sector is experiencing substantial technological disruption as Quantum Computing and Artificial Intelligence (AI) continue to advance at a rapid pace. This study employs bibliometric analysis, specifically VOS Viewer, to investigate the academic environment at the intersection of financial risk, AI, and quantum computation. From 2014 to 2023, a comprehensive bibliometric analysis was performed on a total of 145 journal articles that were published in Scopus and Web of Sciences (WoS). Articles are categorized based on their homogeneity in the disciplines of Quantum Computing, Financial Risk, and AI, as well as their interdisciplinary compositions. The results, which include authorship trends, keyword dynamics, and linked works, are analyzed and presented. This extensive bibliometric analysis offers critical insights into contemporary research and pinpointing areas necessitating further exploration. As quantum computers and AI algorithms become more sophisticated, this paper investigates the potential weaknesses and issues that financial institutions may encounter. By analyzing the intersection of two transformative technologies, the report offers critical insights into the discourse surrounding the safeguarding of financial systems in the quantum era. The analysis not only enhances the quality of the review but also directs researchers to significant papers and identifies regions of publications, thereby facilitating a more comprehensive understanding of the research environment.

This paper investigates the success of the EHR system and its impacts on the knowledge-sharing process by healthcare professionals in a public medical complex in Saudi Arabia. It aims to reexamine and evaluate the usefulness of the updated DeLone & McLean IS success model considering IS in the healthcare context. The study is based on a quantitative methodology conducted at a public medical complex in Saudi Arabia. The data analysis was performed by combining descriptive and exploratory analysis. SPSS was used to test the constructs' validity and scale reliability. Additionally, using AMOS to test the model fit and examine the direct and indirect relationships among dependent and independent variables, structural equation modeling was performed. The results support the role of EHR use in enhancing knowledge-sharing practices within the medical care complex. The findings show that EHR users appreciate knowledge transfer and collaboration between medical staff. The findings suggest that the EHR’s characteristics of information quality, system quality, and service quality promote medical care knowledge-sharing through system satisfaction and use. This study helps medical staff and health decision-makers to understand the EHR benefits and the medical digital innovations that may contribute to improving the work conditions at care organizations. It shows that healthcare organizations can identify various benefits from the use of the EHR**,** especially in terms of knowledge management and sharing to propose better medical services. The paper contributes to the existing empirical literature by demonstrating the confirmation of the D&M Model as a relevant instrument for IS success within the healthcare sector.

The banking industry is undergoing a significant transformation driven by digital technologies, evolving customer behaviors, and increasing regulatory pressures. To remain competitive, banks must adopt governance frameworks that integrate digital innovations to enhance operational efficiency and improve environmental, social, and governance (ESG) performance. This study identifies governance framework archetypes essential to digital transformation in banks through a comprehensive research methodology, including a literature review of digital governance frameworks, a comparative analysis of 11 leading digital banks worldwide, and latent class analysis to uncover key archetypes. Our findings reveal nine distinct governance archetypes, categorized into three dimensions: structural, including Centralized, Semi-centralized, and Open Innovation-oriented banks; dedicated processes, comprising Continuous Improvement, Vanguard, and Fast Follower banks; and relational mechanisms, featuring Self-empowering, Explorer, and Relationship-oriented banks. This classification advances the understanding of governance approaches that effectively support banks in their digital transformation journeys. The implications of these archetypes are substantial, offering a framework for banks to align their strategies with digital transformation initiatives. By adopting these governance structures, banks can better navigate the complexities of the digital landscape, foster innovation, and ultimately enhance their service offerings while addressing the evolving demands of customers. This research contributes to the growing body of knowledge on digital governance in banking and provides guidance for financial institutions striving to succeed in an increasingly digital world.

Technology innovation affects both the public and private non-financial sectors, the zakat institution (Islamic social finance) included. The institution uses an online system to communicate with zakat payers and zakat recipients to ensure its effectiveness in terms of collection and distribution. This research focuses on the adoption of a technique known as AD/AC and ZP/ZR, which is mainly utilized by partially and non-privatized State zakat institutions in Malaysia. To this end, three (3) techniques pertaining to appraising the performance of Malaysia’s Zakat institutions in managing Zakat funds were established. The analysis also includes the adverse impact of Covid-19 pandemic on the performance of zakat collection of the respective zakat institutions in Malaysia. In this relation, a quantitative approach was adopted using the primary and secondary data collected from JAWHAR and various states’ zakat institutions. By utilizing a technique of online data collection and applying the AD/AC as well as ZP/ZR methods, the performance of Malaysia’s zakat institutions is appraised. Furthermore, with the help two (2) ratios and eight (8) scenarios the performance of the innovative technique of AD/AC and ZP/ZR for zakat collection and disbursement, efficiency and sustainability of zakat institutions in Malaysia were evaluated. The results show that most zakat institutions, particularly those that deal with zakat payments, have begun to use the technique AD/AC and ZP/ZR within their organizations. The performance of zakat institutions in all states in terms of zakat distribution and disbursement do not have a consistent trend for the period 2016-2020. However, there is still a dearth of technology being used for zakat disbursements and fund reporting. From the observed data, with 2016 being the exception, for each remaining year, a state in Malaysia is ranked as the best technology performer or the most efficient. Selangor - fully privatized state (2017), Pulau Pinang - fully privatized state (2018), Federal Territory - partially privatized state (2019) and Negeri Sembilan-partially privatized state (2020) are the most efficient for the respective year. The sustainability of each zakat institution in Malaysia in the presence of Covid-19 pandemic was ascertained as well. Interestingly, contrary to the efficiency result, all non-privatized states (Kedah, Sabah and Kelantan) and one partially privatized state (Negeri Sembilan) were sustainable in coping with Covid-19 pandemic, while all fully privatized states (Selangor and Pulau Pinang) were not. Overall, it was conjectured that the technique AD/AC as well as ZP/ZR would help to improve the operations of zakat institutions to become more efficient and successful in distributing zakat fund to the poor. However, as zakat collecting and distribution involved millions of records, its management is still debatable by all parties.

This article identifies the factors for managing the behavioral resilience of a firm in the face of exogenous shocks in the economy. Its main hypothesis is that each enterprise has certain resilience competencies that need to be strengthened and developed in the future. The paper identifies 17 key competencies that determine the behavioral resilience of an enterprise. Using the method of factor analysis, a model of behavioral resilience is built, which is used to support management decision-making.  The factor model of behavioral resilience SELF&IRR includes 7 competencies: S – Speed of response to processes and events; E – Endurance; L – Leadership; F – Flexibility; I – Innovation, ideas, ingenuity; R – Responsibility; R – Resource capabilities. This model can be used to determine the level of behavioral resilience, based on which a decision is made on the choice of the enterprise's strategy. Depending on the level of behavioral resilience, the management staff decides on the choice of a certain strategy (systemic transformation; structural transformation; local changes in the firm's competencies; adaptation of competencies to changes), which is aimed at strengthening the firm's viability and development. The successful execution of a chosen strategy enhances the firm's capacity to withstand current and future threats while actively seeking or purposefully creating new opportunities for development.

Purpose: The purpose of this research was to provide a model for choosing the best content for the activity of service guilds.Method: In inferential statistics, the K-S test is used for the normality of research hypotheses. For this purpose, Pearson's correlation coefficient and linear regression tests have been used through SPSS 21 software, and the best content generated using genetic algorithms and forbidden search were introduced.Findings: Analysis of research and implementation results with two collective intelligence algorithms shows that Instagram has a positive and significant effect on all four dimensions and thus leads to the success of the service classes that have used Instagram.Conclusion: In this article, a combination model of genetic algorithm and forbidden search algorithm was chosen for users so that the best content, which of course does not contain malicious ads and cookies, etc., is introduced for the continuation of the service industry.

This research aims to examine the impact of Big Data Quality (BDQ) components, including completeness, accuracy, format, and currency, on Big Data-driven Human Resources (BDHRP) management practices and Decision-Making Quality (DMQ) from the viewpoint of HR managers. It also seeks to identify the most impactful components among completeness, accuracy, format, and currency in the context of BDHRP and DMQ. A survey of HR professionals in 108 French organizations deploying Big Data Analytics systems revealed positive relationships between BDQ, BDHRP, and DMQ. Statistical analyses conducted with the Partial Least Squares Structural Equation Modeling (PLSEM) method showed a positive relationship between BDQ components and BDHRP, with currency and accuracy emerging as the most influential factors. Additionally, a strong positive relationship was found between BDQ components and DMQ, with currency and accuracy leading the way. The research also found a significant connection between BDHRP and DMQ, further underscoring the importance of effective HRM practices in enhancing decision-making quality. These findings contribute significantly to understanding the crucial role played by big data quality in BDHRP and decision-making, highlighting the potential for organizations to improve outcomes by focusing on currency and accuracy-related concerns. In practical terms, this research offers insights that can guide data quality practices, resource allocation, and strategic decision-making within organizations.

This paper investigates the success of the EHR system and its impacts on the knowledge-sharing process by healthcare professionals in a public medical complex in Saudi Arabia. It aims to reexamine and evaluate the usefulness of the updated DeLone & McLean IS success model considering IS in the healthcare context. The study is based on a quantitative methodology conducted at a public medical complex in Saudi Arabia. The data analysis was performed by combining descriptive and exploratory analysis. SPSS was used to test the constructs' validity and scale reliability. Additionally, using AMOS to test the model fit and examine the direct and indirect relationships among dependent and independent variables, structural equation modeling was performed. The results support the role of EHR use in enhancing knowledge-sharing practices within the medical care complex. The findings show that EHR users appreciate knowledge transfer and collaboration between medical staff. The findings suggest that the EHR’s characteristics of information quality, system quality, and service quality promote medical care knowledge-sharing through system satisfaction and use. This study helps medical staff and health decision-makers to understand the EHR benefits and the medical digital innovations that may contribute to improving the work conditions at care organizations. It shows that healthcare organizations can identify various benefits from the use of the EHR**, ** especially in terms of knowledge management and sharing to propose better medical services. The paper contributes to the existing empirical literature by demonstrating the confirmation of the D&M Model as a relevant instrument for IS success within the healthcare sector.

This study aims to analyze 45 definitions of digital transformation (DT) to identify key drivers and propose a conceptual framework to outline their impact on business performance. Through content analysis, 24 key drivers were identified, focusing on the frequency of occurrence across the definitions. The analysis highlights drivers such as IT technologies & innovation, business model, business performance, customer experience, and operational processes. The results show a significant emphasis placed on various drivers of DT, reflecting its multidimensional nature. Key drivers include technological innovation, organizational adaptation, customer-centric strategies, and change management practices. By conceptualizing the relationships between key drivers and performance outcomes, the proposed conceptual framework provides theoretical insights into the mechanisms underlying digital transformation and its impact on business performance. The proposed framework integrates technological, strategic, organizational, and cultural dimensions. The analysis underscores the complexity and multidimensional nature of DT as a strategic phenomenon and offers drivers on which the organizations should focus to face the challenges of digital disruption. This paper's original theoretical contribution lies in synthesizing various definitions of digital transformation from the past two decades to propose a comprehensive definition of Corporate Digital Transformation.

This study aims to apply automatic machine-learning approaches using genetic algorithms to enhance heart disease prediction. Heart disease has remained the major cause of mortality in the world, necessitating an effective and timely diagnosis. Most current diagnostic and assessment processes are lengthy and expensive, relying heavily on clinical expert knowledge. To help address these issues, machine learning approaches, which derive their utility from examining substantial datasets for the recognition of patterns, have emerged as a potential solution, providing solutions beyond those achievable by human recognition alone. Genetic algorithms are also suited to addressing these issues as they mimic natural evolution to perfect high-caliber machine-learning models, feature selection, and parameter selection in machine-learning applications. This study examines the utilization of genetic algorithms working alongside AutoML frameworks to improve accuracy in heart disease predictions. Reducing to the best combination of attributes and the optimum parameters for each attribute is a time-consuming task, so automating this aspect of the process allows for more accurate and prompt predictions, consequently reducing the manual work. The AutoML approach followed in this research is TPOT, which uses genetic algorithms to ascertain optimally designed machine-learning pipelines. The application of AutoML, together with genetic algorithms, is the most prominent finding that yields a significant improvement in the quality of the predictions for heart disease compared to the traditional assessment approaches, with an accuracy of 93.8%. This approach will enhance diagnostic accuracy and enable early diagnosis, thereby reducing the likelihood of misdiagnoses or ineffective treatments and ultimately lowering associated costs.

Extensive technological advancements have highlighted the importance of digital transformation in improving business performance. While prior research on this topic has been done in the information systems and business management domains, it has been limited to specific areas. Therefore, it is crucial to evaluate the impact of digital transformation comprehensively. This research aims to systematically identify critical themes, significant topics, main concepts, and trend priorities. The study involved the analysis of 474 research papers from 2015 to 2024 from reputable databases such as SCOPUS, Web of Science, and IEEE Xplore. First, thematic analysis identified the main themes and interpreted their relationships. Identified themes refer to technological changes at the operational and strategic levels through data analytics, digitalization, collaborative learning, and digital interaction. Realizing that digital transformation leads to value creation, improved service quality, customer experience, and long-term communication in digital ecosystems. These findings were related to dynamic capability theory concepts and compared with theory constructs like sensing, seizing, and transforming. Next, text mining techniques were used for deeper investigation, including word cloud, topic modeling (Latent Dirichlet Allocation), and text clustering (K-means). Findings were categorized into three perspectives: business, customer, and systemic, highlighting the influential role of digital technologies, particularly artificial intelligence (AI) capabilities. Moreover, trend analysis presented research priorities using VOSviewer. Finally, research innovation involved designing thematic networks and examining the relevance of significant topics as a research artifact with subtle differences compared to the conducted research. This novel approach provides five targeted propositions to audiences for future research.