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In an era where digital transformation (DT) reshapes industries, the role of Knowledge Transfer Partnerships (KTP) in bridging academic insights with industrial innovation becomes crucial. This study conducts a comprehensive scientometric analysis of 360 academic papers spanning from 2013 to 2023 to map the evolving landscape of KTP in the context of DT. By employing advanced visualization tools including generative visual networks and keyword co-occurrence analysis through CiteSpace, critical research gaps and trends, particularly addressing how KTP can mitigate technological obsolescence and enhance innovation management within enterprises are identified. The findings reveal a significant escalation in research output related to KTP, with a pronounced focus on integrating cutting-edge technologies of Artificial Intelligence, machine learning and virtual reality and fostering market adaptability. This study charts the exponential growth of literature and highlights strategic areas of research entities, global geographical coverage, and advanced digital trends where KTPs are pivotal in enhancing organizational resilience and competitive advantage in a rapidly digitizing world. This paper contributes to the existing knowledge of KTP by identifying the patterns and trends of on-going research and offering an evolutionary model to guide theoretical development and practical business operations and policy making.

This study aims to understand the heterogeneity of corporate social responsibility (CSR) approaches across subsidiaries of the same multinational corporations (MNCs) and determine the co-evolutionary dynamics that enhance the global cohesiveness of MNCs' CSR strategies and performance. The study draws on co-evolutionary theory and adopts an explanatory research design based on embedded multiple case study methodology. Semi-structured interviews with top management and employees responsible for CSR or related areas were conducted, and secondary data collection was done for triangulation and validation. The findings suggest that MNCs experience heterogeneity in their CSR, particularly in the implementation approaches, philanthropic and environmental CSR dimensions, and that MNCs with significant heterogeneity at the strategic level have less cohesive strategic CSR practices. The study also found that MNCs that encourage and facilitate active collaboration between subsidiaries have more cohesive and strategic CSR, and each MNC's CSR is driven by different co-evolutionary dynamics. The study contributes to the expansion of co-evolutionary theory to the study of CSR and offers a dynamic CSR co-evolution model explaining MNC CSR heterogeneity in real-world situations, along with potential levers to achieve more globally cohesive strategic CSR.

In the era of banking 5.0, providing electronic banking service value to the users is a key to maintaining customer satisfaction which results in high customer retention. So far, a wider utilization of e-banking services such as internet banking (e.g., cardless ATM and smartphone banking) and e-banking cards with microchip sensors (e.g., master, credit, and visa cards) by many users persuades banks to develop and promote e-banking service value to the users. Thus, banks must continue promoting their e-banking service value to their customers, and this requires all banks to evolve and update their current e-banking services constantly. This research aims to explore how e-banking service value is developed by constructing an integrative framework consisting of perceived risk (financial and performance risk), convenience (access and transaction), perceived quality, and perceived quality with the e-banking experience as a moderator. Researchers collected data from 700 Cambodian e-banking users via a survey questionnaire to accomplish this objective. In addition, this study employed a path analysis method to analyze the data. Results showed that perceived quality was significantly influenced by transaction convenience, access convenience, and performance risk, except financial risk. Next, the perceived value was significantly influenced by perceived quality, financial risk, transaction convenience, performance risk, and access convenience. Finally, e-banking experience significantly moderated the relationship between perceived quality and perceived value.

The term intangible assets embraces a wide array of assets, resources and capabilities that contribute to the productive potential of firms, but which do not have physical substance. Most empirical studies simply use an aggregate measure of intangibles derived from data collated from firms’ financial statements (we refer here to recognized or written intangibles). But such a measure includes only some of the valuable intangibles that firms possess, and typically does not capture the value of many intangibles (e.g. managerial and organizational capabilities, market knowledge, trusted relationships) that are internally generated by the company. Yet it is likely that such unrecognized or non-written intangibles will have at least as important an impact on corporate performance as that of the written intangibles. We show empirically that the growth of both categories of intangible assets have significant effects on sales growth (our chosen measure of firm performance), and that the elasticity of firm performance with respect to the growth of written intangibles is significantly larger than the elasticity with respect to the growth of non-written intangibles. However, our analysis also reveals that the mean stock of non-written intangibles is three times larger than the stock of written intangibles, and that the mean growth of the non-written intangibles is three times faster than that of written intangibles.

This study introduces a new adoption model for assessing the quality of experience (QoE) of radiologists using AI-based medical imaging technology. While AI has increasingly been used by radiologists for screening, diagnosis, and classification of medical images, previous investigations have primarily focused on metrics such as effectiveness, efficiency, and satisfaction. This research expands the evaluation criteria to include user experience and user interface (UX/UI) factors, integrating them within the broader QoE. QoE is conceptualized as a multifaceted construct influenced by both human and system factors, which affect cognitive perception, including hedonic and pragmatic aspects. Data were collected from 159 hospital radiologists with prior experience in using AI technology in medical imaging systems through a structured questionnaire. The data were then analyzed using structural equation modeling principles. The findings suggest that contextual content, human factors, and system characteristics significantly influence cognitive perception, which in turn affects the adoption and utilization of AI in medical imaging. This adoption model captures the radiologists' integration of AI throughout various stages of radiological procedures, including scheduling, scanning, acquisition, interpretation, reporting, and communication. The study also highlights the importance of data collection, storage, and sharing practices in compliance with privacy policies.

In the digital era, e-government services have become essential tools for fostering open innovation and enhancing societal welfare. This study extends SocioCitizenry theory, a novel framework that redefines e-government adoption by positioning citizens as active co-creators. Unlike traditional models, this theory emphasizes citizens' roles in interpreting e-government cues, thereby influencing relationship quality and shaping future behaviors across both adoption and post-adoption stages. Specifically, our analysis of 595 valid responses reveals that strong relational bonds (financial, social, and structural) and high relationship quality (trust and satisfaction) significantly enhance e-government adoption and promote proactive citizenship behaviors, such as advocacy and information sharing. Moreover, the study uncovers the varying effects of citizen involvement with online government services, offering fresh insights into the dynamics of citizen engagement. Consequently, the SocioCitizenry theory provides a transformative, citizen-centric approach that can redefine e-government services by emphasizing the importance of relationship quality and citizen engagement. Ultimately, this framework opens new avenues for developing inclusive e-government services that align with public needs globally, ensuring enhanced accountability and responsiveness of public institutions.

Business failure prediction has become crucially important for today's firms, enabling them to reduce financial risks and make informed decisions. This study uses a dataset of 6819 companies and 96 financial and macroeconomic variables to present a comparative analysis of machine learning (ML) models for predicting corporate bankruptcies. Behind this research is to improve the accuracy of bankruptcy prediction, which can help companies make more informed decisions and reduce financial risks. This study aims to evaluate the effectiveness of 16 ML algorithms in terms of accuracy, sensitivity, and other relevant metrics. The work uses methodologies that include data collection and cleaning, exploratory data analysis, model preprocessing and training, and model performance evaluation. Data preprocessing and hyperparameter optimization techniques were used to improve model performance. The evaluated algorithms include Classifiers such as Stacking Classifier (SCC), Randomized Search Classifier (RCV), Historical Gradient Boosting Classifier (HGBC), MLP Classifier (MLPC), K-Neighbors Classifier (KNC), Decision Tree Classifier (DTC), XGBRF Classifier (XGBRFC), Support Vector Classifier (SVC), Logistic Regression Classifier (LR), Linear SVC Classifier (LSVC). With an accuracy of 97.63 %, recall of 97.63 %, and F1-score of 97.63 %, the results show that the SCC algorithm was the best. Other models, such as RCV and DTC, also showed good results, with accuracies above 97 %. However, models such as PAC and BNB had lower performance and accuracy below 90 %. Finally, this study compares the results of ML models in predicting business failures and highlights their effectiveness. The SCC algorithm is considered the most suitable model for this task, as it suggests that it can help economic actors make more informed decisions and reduce financial risks in the context of firms.

The rate of biodiversity loss and forest depletion has surged significantly, primarily driven by the substantial increase in worldwide environmental deterioration. This alarming development is nearing the critical temperature limit of 2 °C, necessitating increased scrutiny. This study therefore adopts the Load Capacity Curve framework that amalgamates these concepts, offering a guide for implementing a robust and sustainable adaptation plan in the North African region. The research utilized data spanning from 1991 to 2022 and employed advanced quantitative methods, including quantile regression (QR), augmented mean group (AMG), and the Half-Panel Jackknife (HPJ) Wald-type test, to explore causality. The empirical findings challenge the notion of a U-shaped relationship between per capita GDP and ecological load capacity, revealing instead an inverted U-shaped pattern at the 25th and 50th quantiles. Additionally, the study identifies a negative and significant association between natural resource rents and globalization with the ecological load capacity factor, indicating a detrimental effect. On the other hand, Fintech demonstrates a statistically significant and positive impact on environmental sustainability, indicating a mitigating effect. Causality tests further reveal bidirectional causality between ecological load capacity and per capita GDP, GDP squared, and Fintech, while a unidirectional positive relationship exists between globalization and ecological load capacity. Based on these findings, the study puts forth several policy recommendations to support informed decision-making.

Countries should focus on the improvements to achieve sustainable development goals. However, taking these actions create some operational costs. Thus, it is appropriate for rising nations to prioritize sustainable development objectives. Due to this condition, more critical sustainable development goals should be identified. Accordingly, this study evaluates green energy-oriented sustainable development goals for emerging economies. Within this context, a model is generated by considering the golden cut and bipolar q-rung orthopair fuzzy sets (q-ROFSs). The novelty of this study is to identify key green energy-oriented sustainable development goals for the improvement of emerging economies by an original model based on the golden cut and bipolar q-ROF decision making approach. All results are similar, indicating the proposed model's validity and coherency. It is concluded that increasing renewable energy is the most significant target with a weight of 0.235 regarding green energy-oriented sustainable development. Cooperation and efficiency are other significant issues for this condition, with weights of 0.198 and 0.196. It is also identified that Turkey and Mexico are the most successful countries in reaching green energy-oriented sustainable development goals. Increasing the share of renewable energy in total energy production and consumption is important for emerging countries. For this situation, governments should provide subsidies for renewable energy investors to provide a cost advantage. In this context, issues such as tax cuts and loans with low-interest rates can increase the motivation of clean energy investors.

Objective: The objective of this article is to prioritize technologies, innovations, and new businesses related to the leather agroindustrial chain in the department of Quindío (Colombia) for the year 2035. Method: The prospective scenario and Delphi methods were used to prioritize key variables, technologies, actors, innovations, objectives, and scenarios in the leather agroindustry. As a previous phase, the Scopus database was used with the critical factors of leather, tanning, tanneries, tannery, leather transformation, cowhide, and suede, and the results were used as input for structured, anonymous and reiterative consultations with national and international experts on topics related to the chain and with a time horizon of 2035. The questionnaire was posed by groupings, including product and by-product innovation, sustainability, technology, and genetics, and, for the first round of the Delphi method, was sent to more than 70 national and industry experts/stakeholders, representatives of universities, the public and private sectors, and organizations related to the chain. Findings: After conducting a bibliometric analysis, key terms related to the leather sector were visualized and a validation was made for MICMAC methods, with 44 variables found; with topics ranging from raw materials to current regulations. For the MACTOR method, future objectives were determined for clean technologies, the creation of new product development, and an R&D&I Center for productive transformation. Meanwhile, for the Delphi method, 44 technologies and innovations were prioritized through rounds with complementary analysis using Python and natural language processing. Conclusions: Topics such as vegan leather made, from mushrooms; bio-leather or leather made, from mango peel; nanofibers, nanocomposites, biogas production, nanofiltration, 4D Printing, Artificial Intelligence to improve leather quality; augmented reality to improve customer experience; genetic improvement of animals to improve leather quality, among others, in which innovation challenges and open innovation strategies can be generated, as well as in R&D&I projects, were identified.

The study aims to determine if organizational flexibility and support generate innovation in IT solutions, providing firms with enhanced competitiveness. It also explores the mediating role of organizational learning in its flexibility and innovation dynamics and the moderating influence of employees' perceived organizational support on its flexibility and learning. The paper argues that the inimitable internal resources of flexibility, learning capabilities, and a supportive climate can help service-oriented firms innovate to enhance their competitive advantage. A quantitative approach was adopted to conduct a cross-sectional survey of 1350 IT solution-providing companies concentrating on Software Export zones based in India. The structural equation modeling technique was adopted to test the validity and reliability of the measurement models, followed by the proposed direct, mediating, and moderating relationships among organizational flexibility, learning, support, and service innovation. The findings illuminate the significant positive influence of firm-level resources of organizational flexibility on learning, leading to amplified innovation in its services. Additionally, perceived support from the organization's side towards the employees mitigates the positive relationship between organizational flexibility and organizational learning, indicating a negative association. This study also highlighted the untested influence of organizational support as a mitigator of learning in flexible organizations.

Supply chain competitiveness and agility are matured areas in supply chain management. While there is an ongoing evolution in digital technology alongside supply chain competitiveness and agility, the literature appears to have limited bibliometric reviews on how digital technology impacts these aspects. This study examines supply chain competitiveness with bibliometric analysis, focusing on the critical elements of supply chain agility and the impact of rapidly advancing digital technologies. The study bridges the gap between management and technology disciplines. Employing the PRISMA methodology, 147 scholarly articles were meticulously selected and analysed, adopting a multifaceted analytical approach that combines bibliometric and descriptive analyses. This thorough literature synthesis reveals a profound and intricate connection between supply chain agility and digital technology, underscoring their joint significance in fostering competitive advantage within the dynamic business landscape. This investigation contributes to the existing body of knowledge by identifying seven distinct clusters, offering a detailed map of the current research landscape. This study charts a course for future academic inquiries into this critical area and provides valuable insights for practitioners. It underscores the importance of integrating agile supply chain practices and digital technologies to maintain and enhance competitive positioning in today’s fast-paced business environment.

Circular Supply Chain Management (CSCM) is gaining prominence among diverse stakeholders, practitioners, and scholars. However, its adoption remains limited, particularly within Small and Medium Enterprises (SMEs). This study employs Interpretative Structural Modeling (ISM), specifically tailored for SMEs, to elucidate the contextual relationships among CSCM indicators. Furthermore, it employs the Matrice d’Impacts Croisés Multiplication Appliqué à un Classement (MICMAC) analysis to categorize these indicators into driving- dependence power quadrants. Thirteen CSCM indicators are identified and classified into three sustainability dimensions: economic, environmental, and social. The ISM model comprises four levels, with employees’ exposure to hazardous materials at level one, followed by ten indicators at level two, one at level three (reuse, remanufacturing, recycling complexity), and one at level four (eco-material). MICMAC analysis reveals that none of the indicators falls into the autonomous quadrant. Employees’ exposure to hazardous materials is categorized in the dependent indicators’ quadrant, while ten indicators belong to the linkage quadrant. The independent quadrant includes two indicators: eco-material and reuse, remanufacturing, and recycling complexity. SMEs can utilize these CSCM indicators as an initial step toward circularity implementation. The recommended implementation sequence follows the ISM model hierarchy, starting with level four indicators and progressing through levels three, two, and one, acknowledging the influence of higher-level indicators on lower-level ones.

Digital Twins (DTs) developments are still in the pilot stages of deployment in supply chain management (SCM), and their full integration with real-time synchronization and autonomous decision-making poses many challenges. This paper aims to identify these common challenges and provide a conceptual framework for establishing a Digital Twin (DT) system to improve supply chain management performance. The paper presents a systematic literature review of 129 research papers on DT applications for SCM improvement. The selected papers were reviewed and classified into three categories: manufacturing and production, supply chain, and logistics. The development of digital technologies such as the Internet of Things (IoT), Radio Frequency Identification (RFID) devices, cloud computing, cyber-physical systems (CPSs), cybersecurity (CS), and simulation modeling has increased the opportunities to explore the creation of supply chain DTs. However, there are limitations and various challenges due to the complexity of most systems. The results indicate that DT for SCM should include external links (i.e. suppliers, distributors) and internal links (i.e. procurement, production, logistics) to deal with any disruption through data-driven modeling with real-time synchronization. Based on the review findings, this study proposes a three-layered conceptual framework to improve supply chain management performance. The proposed framework provides future directions for DT research in SCM. It provides a holistic and integrated approach to DT implementation, the common DT technologies, and data analytics techniques for improved supply chain performance.

Our paper empirically confirms that collaborative governance in social startups affects social performance through mediating economic performance. While there is a growing literature on the sustainable growth and performance of social economy organizations, empirical research on social startups is very scarce. Therefore, this study can contribute to the knowledge of social economy and entrepreneurship research through an empirical study that identifies factors for social startups' performance. This study utilized data from 399 social startups that are commercial companies such as corporations, which are the legal form of startups in South Korea, and the results of the analysis are as follows. First, cooperative governance of social startups had a significant positive effect on economic performance, and social performance also had a significant positive effect. Second, the economic performance of social startups had a significant positive impact on social performance. Finally, collaborative governance in social startups affected social performance through the mediation effect of economic performance. The implications and insights of this study are as follows: First, we find that collaborative governance has a positive impact on both economic and social performance of social startups, and thus is a key factor in the success of social startups. Second, this study represents the first empirical study on the performance improvement of social startups and suggests collaborative governance as an important success factor for social startups.

Objective: The objective of this article is to generate an innovation management model for organizations in the functional food ingredients and additives sector, based on prioritization of variables, stakeholder consultation, technology surveillance, HypeCycle for emerging technologies and S-curves. Method and analysis: A literature review was conducted on innovation management models and associated articles were selected from which the relevant variables. Three surveys were used; one for the statistical analysis of 97 initial variables, prioritizing a total of 32 variables that were the input for survey II; survey III validated a graphical conceptual model of innovation management; the HypeCycle and S-curves Were used to analyze the emergence component as input for open innovation strategies. three possible innovation management models for the sector were validated by stakeholders of an organization in the sector, from a statistical point of view, mode, modal frequency and consensus percentages were used in the 3 surveys and non-linear regression models, T value, P value and Durwin Watson in the calculations of S curves. Findings: From the literature review, 97 variables were identified, prioritizing 32 of them by stakeholders in survey one. Excellent, good, average and poor states were generated on these variables in survey two, including analysis of relevance and congruence for each question, the Survey 3 presented three graphic concept models for prioritization, obtaining 1 MGI prioritized for the sector, in the Hypecycle methodology 13 emergency components related to nanoencapsulation were identified, in S curves 13 nonlinear regression models were applied in nanoencapsulation, mixing and drying obtaining inflection points in the years 2016 – 2020 for nanoencapsulation technology, 2020–2030 for mixing and 2033 – 2042 for drying technology. Finally, 7 open innovation platforms with previous innovation challenges in food and agribusiness were identified. Conclusions: The MGI proposed for the sector under analysis corresponds to a structured one that facilitates the identification, development and commercialization of new ingredients and functional food additives, improving competitiveness and response to market and user demands.Furthermore, the alignment with the Hypecycle or over-expectation cycles with the proposed MGI allowed us to capture how this methodology helps manage the adoption of emerging technologies in nanoencapsulation, mixing and drying. Regarding the analysis variables, the research showed the importance of integrating these variables with external collaborations, such as collective collaboration, and with open innovation platforms, to promote the co-creation and expansion of knowledge in the food ingredients and additives industry. functional in Colombia, the complementary S-curve methodology allowed us to discover that the life cycle of the mixing and drying technology has not reached its inflection point, which constitutes an opportunity for organizations in the sector. This could be validated through of Python libraries and code to validate turning point calculations.This article is relevant to explore new alternatives for the co-development of innovation management models - MGI -, applied to specific sectors taking into account open innovation variables. The novelty of the research lies in the fact that the results obtained, aligned with the prioritization of variables, consultation with stakeholders, technological surveillance, Hypecycle and S-curves, become inputs for specific strategies and challenges of Open Innovation for ingredient and additive industries. functional foods and opportunities for R&D&i activities and projects in this sector.

Background: In today’s rapidly changing business landscape, innovation is recognized as a critical driver of growth and competitiveness for organizations. As businesses strive to remain agile and respond to evolving customer demands, the need for effective tools to assess innovation performance has become increasingly vital.Problem Statement: To better understand Research, Development, and Innovation (RD&I) projects, it might be valuable to investigate and implement new approaches to improve the current innovation indexes. Such exploration could allow for a more detailed and nuanced analysis, enabling a comprehensive evaluation of various aspects of the projects. It may be beneficial to explore ways to enhance the current innovation indexes to provide more nuanced and comprehensive insights into RD&I projects. Traditional indexes are not designed to account for the uncertainty, non-linear progression, and the necessity for long-term impact assessment of RD&I projects. Moreover, projects that involve both hardware and software components introduce additional complexities due to their interdisciplinary nature, integration challenges, and differing lifecycle speeds, making their assessment particularly challenging. Goal: This paper describes the conceptualization and evaluation of the InnoVation Index (IVI), underscoring its role in the innovation assessment within the complex landscape of RD&I projects, focusing on those involving hardware and software components. Method: The methodology comprised a two-fold process. Initially, a literature review was conducted to comprehensively understand existing innovation indexes and their shortcomings, especially in hardware and software projects. Drawing on the insights from this review and leveraging the expertise of innovation specialists, we formulated the conceptual framework of the IVI. Subsequently, the IVI was implemented in an RD&I Centre affiliated with a public university in Brazil. Results: The IVI evaluation encompassed over 40 projects from collaborations with about 20 global IT companies. Rigorous data collection procedures were employed, involving at least three data collection sessions for different phases of each project. More than 150 data collection sessions engage project managers and business professionals to provide a comprehensive and in-depth understanding of the project activities and their innovation levels. Using the IVI measurement, we successfully identified “disruptive”, “potentially disruptive”, “potentially innovative”, and “non-innovative” projects, allowing stakeholders to tailor their strategies and resource allocation accordingly. Conclusion: The application of the IVI has proven instrumental in aiding stakeholders in decision-making, resource allocation, and project activity realignment. These results underscore the pivotal role of the IVI in overcoming the limitations inherent in traditional indexes, offering a tailored and efficient approach to assess and enhance innovation efforts within the RD&I context, especially in projects that bridge hardware and software domains.

Sustaining the environment is essential for Saudi Arabia, especially given the country’s high dependence on oil rents. While these rents have historically fueled economic growth, they have also contributed significantly to environmental degradation. Sustainable resource use and a transition from conventional to renewable energy sources are essential for achieving carbon neutrality and keeping the upsurge in global temperature below 2°C. In addition, sustainable practices help to preserve ecosystems, biodiversity, and natural resources, which are essential for the survival of all living organisms. Consequently, this study fills a significant empirical gap by investigating the moderating effects of technological innovation, environmental innovation, and green energy technologies in the oil rents and environmental sustainability nexus in Saudi Arabia from 1990 to 2022. Although there are significant direct associations between oil rents and environmental sustainability, the existing literature has not investigated empirical research on the moderating effects of technological innovation, environmental innovation, and green energy technologies in the oil rents and environmental sustainability nexus. To do this, the study implements the model of dynamic AutoRegressive Distributed Lag (ARDL) simulations. The results indicate that rising oil rents, economic growth, trade, and urbanization in Saudi Arabia worsen environmental sustainability while increasing green energy technologies, technological innovation, and environmental innovation enhances environmental sustainability. Interestingly, the findings of the moderating effects of technological innovation, environmental innovation, and green energy technologies in the oil rents and environmental sustainability nexus show that the predicted coefficients of the multiplicative interaction terms are both positive and statistically significant. This demonstrates that these technologies play crucial roles in mitigating the negative effects of oil rents on environmental sustainability in Saudi Arabia. Based on the outcomes, promoting technological innovation, prioritizing investments in green sources and reducing dependence on natural resource extraction are key policy recommendations that can significantly bolster environmental sustainability in Saudi Arabia.

Government entities face unique challenges in public service innovation, often overlooked in research focused on the private and broader public sectors. To address this gap, an empirical study was conducted to identify and rank these challenges and assess the extent to which these challenges are impacted by organizational size and the adoption of the Government Excellence Model (GEM). Through expert consultation and questionnaires, 22 challenges were identified and categorized, with ten highlighted as critical, half of which relate to innovation culture. The study found that organizational size has no significant impact, while GEM-adopting entities perceive six challenges as less significant. These findings provide valuable guidance for policymakers in the UAE and similar contexts to overcome the challenges of public service innovation.

This study aims to examine the determinants of brand loyalty in emerging markets, focusing on smartphone consumers in Bangladesh. The study focuses on identifying the key elements that contribute to brand loyalty and the extent of their impact on customers' commitment to their preferred smartphone manufacturers. A theoretical framework will be employed to establish the relationship between the independent and dependent variables. Data for the study was collected from 390 participants through a Google form explicitly designed for this purpose. In this study Simultaneous Regression Analysis has been used to synchronize the findings of the Structural Equation Models (SEM) and also is used to emphasize the robustness of the study. The data analysis used PSS to calculate the mean and standard deviation. Structural Equation Modeling (SEM), reliability, and convergent validity were accomplished using Amos. The findings indicate that consumer attitudes, service quality, public self-consciousness, and behavioural brand experience all significantly influence brand loyalty. Furthermore, brand trust plays a crucial and favourable mediating role between consumer perceptions, brand loyalty, and service excellence. The model suggested in this study has a big effect, and Bangladesh policymakers should consider this model when planning new factors affecting brand loyalty. The results of this research can be valuable for Bangladeshi smartphone companies as they gain insights into the factors affecting brand loyalty among consumers. The study discusses the limitations of its generalizability and provides practical implications for businesses and their market offerings.

This study explores whether coopetition enhances scale determinants for SMEs, focusing on a case study in the Portuguese Ornamental Stone sector. The findings show that transitioning to coopetition networks positively impacts scale determinants in this sector. These improvements highlight coopetition's potential to drive operational efficiency, resource optimization, and market responsiveness, aligning with Sustainable Development Goals (SDGs) 8, 9, and 12. However, the study's limitations include a specific focus on Portuguese SMEs and a limited data collection period, indicating the need for broader and longer-term research. Future research should extend to different sectors and regions, incorporate a broader range of KPIs, and consider external factors affecting coopetition outcomes. This would provide a more comprehensive understanding of coopetition's role in fostering sustainable growth and innovation in SMEs. This research contributes to theory by leveraging S-D Logic and open innovation to enhance coopetition, showcasing their practical application in SMEs.

The Environmental Sustainability Performance (ESP) in Agricultural Activity (AA) is a topic of great interest due to its essential role in the future of society. Efficient resource management and capacity building among people engaged in AA are essential for reducing environmental impact and achieving long-term sustainability. In this regard, a model was developed based on AA and its relationship with ESP by reviewing the relevant scientific literature. The model was then empirically validated by surveying coffee crops in the Valle del Cauca region of Colombia. Structural equation modeling (SEM) was used to analyze the causal relationships between the theoretical constructs, measured using a 28-item survey instrument grouped into five blocks. The survey was administered to 692 administrators, managers, and owners of coffee Agricultural Producing Units (APUs), and the data were validated and analyzed using the partial least squares technique (PLS-SEM) and SmartPLS Version 4.0 software. The results indicate that Business Associativity (BA), Agricultural Management (AM), Citizen Participation (CP), and Information and Communication Technologies (ICTs) have a direct and positive relationship with ESP. Moreover, this study found that the relationships between AM and BA, ICT and AM, and ICT and CP generate significant indirect effects associated with ESP. Finally, the study discusses the impact of agricultural management on ESP and its effects on other variables.

Over the years, industrial safety management in Peru's metallurgical mining sector has become a business problem with no acceptable solution. Annual statistics reveal an average of 60–70 fatal accidents in companies in this sector. This paper proposes using the Systemic Methodology to Develop and Maintain a Dynamic Balanced Scorecard MSDBSC-EM) to be used in the industrial safety management of the safety department of a Peruvian company in the metallurgical mining sector. This methodology was created at the Instituto Andino de Sistemas - IAS of Peru and combines the criteria of the Balanced Scorecard with the modeling of System Dynamics and contributions of the Soft System Methodology. This proposal demonstrates that dynamic simulation facilitates organizational learning and allows the designing of action plans in the present to manage the future. Also, modeling and computer tools facilitate the development and implementation of prototypes, which can be used to analyze required scenarios through pleasant and easy-to-use environments. In this sense, a dynamic model of key performance indicators of objectives, goals and initiatives was developed and four scenarios (A, B, C and D) were analyzed, from which it can be observed that the degree of commitment and leadership with the Industrial safety affects the increase in the level of preventive culture, which in turn favors the prevention of risks and the achievement of tangible objectives in industrial safety, evident in the drastic decrease in the occurrence of accidents and occupational diseases, and this is achieved to the extent that the personnel have the necessary competencies, which in turn is obtained through constant comprehensive training and the appropriate selection of personnel. On the other hand, the operating budget of the security department is a limiting factor in achieving these criteria. Another important aspect is the degree of existing communication, which affects the increase in the level of preventive culture.

The study was conducted on 125 US based high-tech firms from software engineering, hardware production, biotechnology, and telecommunications. Senior-level executives, including CEOs, board members, and CTOs, provided insights through structured questionnaires. Key findings indicate that AI adoption significantly enhances organizational capabilities in terms of employees’ innovation, creativity, and experimentation. Moreover, AI adaptation positively impacts decision making thus yielding more accurate and timely valuable decisions. These findings contribute to both theoretical understanding and managerial practice by guiding strategic investments in AI technologies, fostering innovation, and advocating for ethical AI deployment practices. Future study should examine longitudinal impacts across industries and regions to optimize benefits and minimize risks in digital transformation efforts. It should also integrate qualitative methods for deeper insights and appropriate AI governance systems.

Precise logistic support is essential after a disaster occurs. It must be timely, accurate, targeted, and based on existing needs. However, obtaining sufficient and accurate information related to logistic distribution locations remains a key problem. Therefore, implementing Named Entity Recognition (NER) can address this issue. In recent years, news coverage through Indonesian digital news media and social media accounts has emerged as a promising source for building a disaster data corpus. This study implemented NER to extract and identify named entities from text-based information, particularly from Indonesian digital news media. In addition to using regular entities from the NER standard, this study introduced new entities specialized for disaster-related information, including DISASTER, SCALE, SUPPLIES, CASUALTIES, and OUTSIDE. The new disaster corpus in the Indonesian language for the NER model was obtained with an imbalanced dataset composition. To overcome this problem, random oversampling was applied. This study also utilized the BiLSTM model to recognize each entity in new textual information, evaluating its performance when the proposed Indonesian disaster corpus was used as a training reference in the deep learning model. Several optimization algorithms applied in BiLSTM were evaluated. The results showed improved BiLSTM performance using Adam optimization and a balanced corpus. Performance indicators achieved were 93.4 %, 82.4 %, and 87.5 % for precision, recall, and F1-score, respectively. The BiLSTM network captured long-range dependencies in sequential data provided by NER. Oversampling ensured that the proposed NER model could precisely recognize all entities and reduce biased results. Thus, the BiLSTM method can better identify entities in the textual corpus of Indonesian disaster-related online news.

Open innovation shapes how organizations innovate and compete, particularly in the dynamic tourism sector. Despite its potential, the theoretical framework for open innovation in tourism is still developing, and there is a need for further understanding of its applications in the tourism sector. This research aims to define the dimensions of open innovation in tourism to understand how these practices can be tailored to meet the sector's unique challenges. Through a systematic literature review of 17 articles from Scopus and Web of Science databases (2016–2023), the study confirms the critical role of open innovation in enhancing tourism competitiveness by enabling adaptation to market changes. Key findings include the importance of collaboration and co-creation among tourism industry stakeholders, which is crucial due to the sector's constant evolution driven by consumer preferences, technological advancements, and competition. Thirteen of the reviewed studies emphasize this aspect, while others highlight the role of digital technologies in modernizing and improving tourism processes, thereby creating innovative services for digitally connected consumers. Additionally, fostering an innovation culture and sustainability is essential for integrating innovative practices that consider future environmental and societal impacts. The research identifies four dimensions of open innovation crucial for tourism: digital technologies, collaboration and co-creation, innovation culture, and sustainability. These dimensions help guide strategies for developing competitive and memorable tourist experiences. The study underscores the complexity and relevance of open innovation as a strategy for tourism development, with the identified themes paving the way for innovation and sustainable success in the sector.

Home care services have a significant role in lowering healthcare expenditure. Supply chain management in home healthcare (HHC) ensures efficient delivery of medical supplies and equipment to patients' homes, improving overall quality of care and patient outcomes. This study proposes a routing and scheduling optimization model for HHC by prioritizing patients, developing an effective delivery strategy, and considering home care logistics and services. The model primarily concerns reducing logistics activities’ overall expenses while considering patients’ priorities. A bi-objective optimization model for a multi-period HHC problem is developed by prioritizing patients with urgent critical needs. The best-worst method (BWM) and technique for order of preference by similarity to ideal solution (TOPSIS) are used to prioritize patients using a linear programming metric (Lp-metric). The BWM and TOPSIS have been uniquely used in this study for routing and scheduling in HHC. Eventually, the applicability of the proposed method is demonstrated through a real-life case study with a series of numerical examples and sensitivity analysis. For instance, by analyzing privilege, we see patients are carefully matched with caregivers possessing advanced skills, leading to increased patient satisfaction. Based on assigned routes, caregivers prioritize patients with higher weight and emergency conditions at the start of each path, followed by patients with less urgent conditions. This ensures that patients with more severe conditions are serviced first.

In the context of international trade with information asymmetries, business certifications can serve as signals of product quality and reliability for customers and stakeholders. Recognizing this, the current study unbraces “signalling theory” to explore the impact of digital transformation strategy (DTS) on firm’s international quality certification status and sales performance in the global economy. To this end, treating quality certification endogenously, the study relies on bivariate copula regression modeling of 121429 firms’ responses data extracted from the World Bank’s Enterprise Surveys. Consistent with the hypothesized theoretical predictions, the findings revealed that DTS, in the form of website ownership, has a significantly positive impact on sales performance due to increasing firm’s reach, visibility, engagement, conversion, and retention of customers across different channels and touchpoints. As theoretically conjectured, quality certification is also found to positively impact sales performance by enhancing the firm’s reputation, trustworthiness, and attractiveness in the global marketplace. Most importantly, the study found empirical evidence that DTS significantly moderates the relationship between quality certification and sales performance, such that quality certification has a stronger effect on sales performance for firms with a well-defined DTS (web=1) than those without (web=0); leading therefore to the conclusion that DTS can amplify the signal of quality certification by making it more visible, transparent, and consistent across different digital channels and touchpoints. Hence, the study provides valuable empirical and theoretical insights that inform current business practices, future research, and potential policy measures to support business growth, and enhanced competitiveness in the global market place.

Policymakers and academics are interested in identifying mechanisms that promote environmental sustainability due to their relationship with climate change. This research evaluates the channels that transmit the effect of technology and financial efficiency on environmental quality. The relationship is moderated by real production per capita, foreign direct investment, natural resource rents, and institutional quality in the context of the Environmental Kuznets Curve. The research covers the 1996–2021 period for a sample of 88 economies classified into three groups according to the World Bank Atlas Method. We use second-generation cointegration techniques with structural breaks and quantile regression models. The findings offer sufficient evidence to conclude that the impact of technology and financial efficiency on environmental quality is heterogeneous throughout the distribution. Our findings suggest that technology is more associated with maximizing production than mitigating or restoring environmental deterioration. A policy implication derived from our research is to encourage the generation of carbon-free technology and promote financial efficiency in upper-middle-income countries to achieve environmental sustainability.

This study aims to assess the extent to which blockchain technology (BCT) may constitute an alternative to the conventional stock trading system and emphasize the changing roles of the key parties. It is expected that BCT would enhance the performance of the process across the three stages (i.e. trading, clearing and settlement within the stock exchange environment). A thorough literature review is conducted to understand the BCT performance modeling techniques and approaches (empirical and analytical) and to examine the theoretical potentials and capabilities of BCT in the financial markets. The case study and simulation methods are used to evaluate the impact of BCT implementation in optimizing the process of trading, clearing and settlement in Abu Dhabi Securities Exchange (ADX) stock-trading activities. This paper presents a simulation analysis comparing a blockchain system with a traditional trading system in the context of stock market. The simulation procedures involve modeling processes over different durations and transaction volumes, using metrics such as process time and cycle time to evaluate performance. The performance index combines these metrics with weights to ensure accurate and consistent measurements. Simulation results reveal that the blockchain system significantly outperforms the current trading system, especially at higher transaction volumes, highlighting its scalability and efficiency. A threshold of 30,000 transactions is identified as the point where blockchain’s benefits become apparent. The analysis shows that blockchain significantly elevate the process efficiency. It reduces both cycle time and process time across varying transaction volumes, maintaining consistency and reliability. Additionally, a simple simulation using the Hyperledger Fabric platform demonstrates the practical implementation of a permissioned blockchain for clearing transactions, emphasizing the system's capability to manage high transaction volumes efficiently and securely. The use of blockchain network for handling seamless transactions using pre-defined smart contracts significantly improves the performance of the stock trading processes, specifically in the clearing phase. Interestingly, the BCT system drops the need for a “third party” (i.e. stock custodian) across the three stages. At the end of the paper, we propose a thereat mitigating model for stock trading with a new blockchain system.

Many educators across U.S. high schools face information quality and system quality challenges when using learning management systems (LMS). These challenges impact both the individual and organizational levels, affecting overall success. The purpose of this study aims to evaluate the effectiveness of e-learning tools using the IS-Impact model. This quantitative research assesses the importance and performance factors of educators' use of LMS tools. Using PLS-SEM and IPMA in SmartPLS 4, the effects of Information Quality, System Quality, Individual Impact, and Organizational Impact on the Overall Impact were analyzed based on data from 237 respondents through an online survey across U.S. high schools. Two conceptual models were developed and assessed based on the original IS-Impact Model. One model examined information quality and system quality as having direct effects on overall impact, while the other considered these variables to have indirect effects. Comparing Model-A and Model-B, path analysis, total effects, importance, and performance of latent variables were analyzed in detail against the Overall Impact dependent variable. Findings emphasize Information Quality and Organizational Impact as critical factors influencing the Overall Impact of successful LMS implementation. The IPMA results from this study offer actionable insights by identifying performance areas needing improvement based on importance. This study reveals a complex interplay of direct and indirect effects, highlighting the need for a holistic approach to enhance the impact of e-learning tools within educational institutions. This study further fosters a culture of innovation and competitive advantage by leveraging information systems to improve organizational processes and outcomes.

This research work examines how consumer behavior and purchasing pattern are affected by recycled cotton-polyester blended clothing, with a particular emphasis on sustainable apparel merchandising. The use of recycled materials has drawn a lot of attention as environmental sustainability in the fashion business is growing attention. In order to investigate customer behavior, perceptions, preferences and purchasing decisions regarding recycled cotton-polyester blended clothing.This research employed a mixed-methods design that included both qualitative and quantitative methods. For a deeper understanding of the customer attitudes, opinions and motives towards sustainable fashion recycled materials in particular, this phase qualitatively involves a one-on-one interview and focus group discussions based on age, gender social class as well as the level of education. In addition, the study considers aspects such as longevity in use, wear resistance, quality, ease of use and clothing comfort that influence buying decisions. Using data from the qualitative phase as a foundation, the quantitative phase uses surveys given to a broad consumer sample to measure consumer preferences and purchasing pattern for recycled cotton-polyester clothing. This research aims at identifying the key factors that impact the preference of customers and buying behavior for recycled cotton and polyester blends based apparel. Additionally, it aims to assess how well sustainable clothing merchandising techniques work to promote these goods. This study indicates that individuals aged 25–45 from higher social strata and with higher educational attainment exhibit greater awareness and preference for recycled apparel with more than 60 % demonstrating a marked inclination towards these products.

Blockchain technology has promising benefits and provides robust solutions for managing business processes. While prior studies have primarily explored its potential in supporting logistics and supply chain management, many practitioners still lack a clear understanding of how to leverage blockchain technology, hindering its adoption within the industry. Bridging this gap and addressing critical barriers requires further empirical research. This study adopts a comprehensive approach, identifying potential barriers through a literature review and validating their significance through the Index of Item-Objective Congruence (IOC). The study then delves into the interrelationships among the significant barriers, utilising Interpretive Structural Modelling (ISM) and MICMAC methods. The results highlight seven significant barriers within the logistics sector, encompassing a lack of government support, operational standards, top management support, limited public awareness, trust issues, technical challenges, and network collaboration difficulties. Notably, the lack of public awareness and inadequate governmental support form fundamental obstacles that drive various challenges. This research offers insights into the barriers that hinder the successful adoption of blockchain technology in logistics, proposing several mitigation strategies that are in line with the principles of open innovation.

Financial distress occurs when individuals or companies struggle to meet financial obligations, often due to factors like high fixed costs, illiquidity, or revenue sensitivity to economic downturns. While various machine learning and deep learning approaches have been developed for predicting financial distress by analyzing financial data to assess the likelihood of a company facing financial difficulties, challenges persist in achieving enhanced prediction accuracy and managing time complexity. To address this, a novel method known as the Chinese Whisper Clustered Stochastic Gradient Descent Federated Learning method (CWCSGDFL) is introduced. It aims to improve the efficiency of financial distress prediction by grouping data samples using the Hamann Similarity Indexed Chinese Whispers clustering process, validating them with a matching coefficient, and addressing data imbalance. Also, Kaiser-Meyer-Olkin correlative targeted projection model is employed to choose the pertinent features for prediction. By achieving balanced clustering results with these selected features, CWCSGDFL reduces time complexity in financial distress prediction. Stochastic Gradient Gaussian Kernel Federated Learning approach is employed to preserve privacy while predicting distress, aided by kriging regression for model analysis. Stochastic gradient descent momentum optimizes the global objective function, ensuring accurate classification with minimal loss. Experimental results demonstrate that CWCSGDFL achieves an average accuracy of 96%, precision of 94%, recall of 98%, F-measure of 98%, and a time complexity of 36 ms, outperforming the existing techniques.

Analyzing the effects of knowledge management in higher education is challenging because of various aspects of tasks such as teaching effectiveness, administrative services, and research communications. Employees' job performance is crucial in higher education institutions in order to disseminate innovations, knowledge, and collaboration. Thus, studying the relationship between knowledge management practices and job performance is essential. Therefore, this literature review and topic analysis aims to provide a concept-driven approach for synthesizing and analyzing numerous available literature bases. Collected journal articles and conference papers were scrutinized based on this research work's above-mentioned aim. Finally, 41 relevant papers have been studied in this work. The presented work advances the understanding of the role of knowledge management processes in higher education institutions in increasing job performance. Findings suggest that individual, national, and professional teams, language issues, and trust can impact knowledge-sharing practices and job performance of higher education institutes' employees.

Despite abundant research on the application of artificial intelligence (AI) in reverse logistics, no comprehensive study with bibliometric and network analysis has been conducted. This study uses bibliometric analysis to derive the prominent research statistics in AI-centric reverse logistics, considering 2929 articles from the last three decades. The most impactful contributors and countries that employ AI in reverse logistics are identified using various bibliometric tools. Also, network analysis is performed to reveal the most influential articles and emerging trends and map the relationships via clustering. The results of keyword co-occurrence and co-citation analyses reveal that machine learning and deep learning techniques have been commonly used for addressing reverse logistics challenges with higher frequency in recent years. Furthermore, a systematic review is carried out, considering the influential articles from recent years. The review is conducted following the systematic literature review framework, and 79 articles are chosen to be studied thoroughly. Subsequently, the articles are divided based on various reverse logistics processes, and the most frequently used AI techniques are identified and categorized into five distinct groups. The comprehensive investigation of AI techniques reveals the use-case scenario of AI algorithms in the reverse logistics domain. This study concludes with implications and recommendations for prospects by addressing the shortcomings of the current studies and providing future researchers and practitioners with a robust roadmap to investigate reverse logistics in their research further.

The positive role played by micro and small enterprises in developing the economy and providing job opportunities requires further care and support to ensure maximum benefits from them. In this vein, this research attempts to understand how networking support (NS), training programmes (TPs), and entrepreneurial intention (EI) interact on one end and how EI and growth aspirations (GAs) interact together on the other. It also investigates the direct influence of NS and TPs on GAs and assesses whether EI can mediate the connection between NS, TPs, and GAs. Data collected from micro and small entrepreneurs operating in different places in Saudi Arabia through a self-administered questionnaire are analysed using partial least squares structural equation modelling and the testing of hypotheses. The findings confirm a positive and significant relationship between TPs and EI, while there is no relationship between NS and EI. Further, TPs and NS have a positive and significant direct connection with GAs. The results also reveal that EI can mediate the relationship between TPs and GAs but not the relationship between NS and GAs. The study concludes by providing the practical and theoretical implications of these findings and offering recommendations for policymakers and entrepreneurs.

Even though Indonesia faces significant gender disparities across various sectors, including education, health, employment, and political representation, there remains a dearth of research addressing the barriers and strategic solutions for women's empowerment, which is the aim of this study. A quantitative method driven by the Modified Penta Helix Model (MPM) and the Delphi Analytical Network Process (DANP) was used. On the one hand, MPM was used to establish renewal between the five strategic dimensions and other additional elements such as economic, socio-cultural, and education and training. On the other hand, D-ANP developed indicators based on Focus Group Discussions (FGD) outcomes, in-depth interviews, and structured questionnaires. According to this study, education and training are essential in empowering women. The results of this study indicate the main problems in empowering women, which are divided into eight main dimensions: economic, socio-cultural, education and training, government support, association support, academic support, media support, and private sector support. Education and training were the most critical problems in empowering women. Collaboration between academics, business, community, government, and media is essential to develop effective education and training programs to empower women in Indonesia. Through employing D-ANP and MPM methodologies, this study suggests a model of women's economic empowerment to improve the quality of Indonesian women and Indonesia's position in the Women's Empowerment Index.

This study investigates how citizens with characteristics aligned with the circular economy utilize internal and external knowledge networks to enhance their practices in the 5Rs—Repair, Reduce, Recycle, Reuse, and Rot—within emerging markets, specifically in Thailand. These 5R principles are essential for reducing waste and extending resource lifecycles, which are critical elements of sustainable environmental management. By integrating individual knowledge sources with external networks, this article identifies effective strategies for advancing awareness and engagement in waste management. The survey-based analysis reveals that Thai citizens predominantly rely on prior experience and peer advice to navigate waste management challenges. Importantly, researchers quantify the impact of such practices, showing that substantial experience with the 5Rs significantly correlates with improved waste management capabilities (β = 0.519, R² = 0.498, MSE = 0.446). This demonstrates that experience accounts for nearly half of the variance in waste management capability. However, the diversity of external knowledge sources alone, without deep technical competence and practical application, does not markedly enhance waste management outcomes. These findings emphasize the need for strategies that prioritize practical experience and skill development in waste management, suggesting that enhancing individual and collective experience with the 5Rs can significantly contribute to achieving sustainability goals. The results advocate for the scalability of the 5Rs across different community settings, offering a framework for other emerging markets striving to integrate circular economy principles into everyday practices.

The oil and gas industry is known for its rapid technological advancements and the complexity of its operations, increasingly relying on data-intensive management. As subsea projects—from exploration to decommissioning—become more complex and data-driven, integrating knowledge management (KM) and database management systems (DBMS) has become essential. The research specifically explores how KM and DBMS contribute to decision-making, utilizing a quantitative methodology through a questionnaire survey. Findings reveal that KM processes significantly improve the effectiveness of non-spatial data management, highlighting KM's crucial role in facilitating technology adoption and operational efficiency. However, the influence of KM on spatial data management and overall decision-making is found to be limited, indicating the necessity for more adaptive and integrated KM strategies that serve the unique requirements of database systems. This study underscores the critical but nuanced role of KM and DBMS in the oil and gas industry, advocating for tailored KM strategies to optimize database management. It suggests future research into the interplay of KM with emerging technologies like AI and machine learning to enhance database management. This investigation stresses the importance of holistic KM practices in effectively managing the intricate data landscapes of subsea project services, thereby contributing to the broader discourse on KM, DBMS, and decision-making in complex settings.

This study aims to examine the consumer behavior with regards to the FinTech services in the context of Pakistan and identify the consumers’ driver and detractors to adopt FinTech firm. Tapping into an area yet un-explored, it seeks to propose a conceptual framework based on the UTAUT2 model and extend the FinTech adoption model by determining the effect of performance expectancy and effort expectancy based on educational segmentation. In a similar vein, it aims at examining the gaps in the adoption of FinTech services in both Islamic banking as well as the conventional competing banks of Pakistan. This paper employed a quantitative research design; data were collected from 382 subjects in Karachi, Pakistan Convenience sampling and random number generation were employed to minimize biased. This study sought participants’ responses to the survey questionnaire of 22 items, distinguishing the seven constructs of the UTAUT2 and a new one, education level, on a five-point Likert scale. Concerning the sample size and the research methods that were used, the rationale was given in line with the available guidelines and the results of empirical research in the field of behavioral sciences. As the research hypothesis postulated, the study revealed performance expectancy, effort expectancy, social influence, and facilitating conditions as independent factors influencing behavioral intention to use FinTech, although the moderating effect of education level only affected the relationship between the performance expectancy and behavioral intention to use FinTech influenced by it. They also found differences between Islamic and conventional banking users, and the enhancing/attenuating impacts of the constructs on the behavioral intention alertly. This research underscores the essential factors that influence the behavioral intention of the users towards FinTech services – performance expectancy, effort expectancy, social influence, and facilitating conditions – with implications for promoting positive service experiences. Financial technology firms must heed these insights to create simple and secure applications to increase the adoption of FinTech innovations that are sensitive to the anglophone African cultures. Besides, the focus on deploying rather diverse local initiatives for comprehending and constantly improving the FinTech services based on people’s feedback can increase consumers’ trust and expectations.

The technology commercialisation process at universities in developing countries often requires improvement owing to their tendency to adopt models commonly used in developed countries. Besides being less contextual, the existing technology commercialisation models is also not practical because it does not accommodate the interests of each stakeholder directly involved in the commercialisation process, such as laboratories, industry and intermediaries. This research proposes a more practical model of technology commercialisation for universities in the Indonesian context by emphasising the interests of such stakeholders, based on four commercialisation strategies, and four university intermediary organisations, namely the technology transfer office, an incubator, the science and technopark, and the university company focused on technology commercialisation.

This study uses transaction cost economics theory to explain the interplay of artificial intelligence readiness, frugal innovation, and business model innovation to achieve SMEs’ internationalization objectives. A survey is conducted among SMEs using cluster sampling to determine their readiness for artificial intelligence adoption. The results are analyzed using Smart PLS 4.1 software. The study explored the mediating role of business model innovation and artificial intelligence’s moderating role in promoting business model innovation and SMEs’ internationalization, respectively. Finally, the authors performed a necessary conditions analysis to identify the necessary conditions for SME internationalisation. The study has contextual implications for stakeholders and policymakers of an oil-rich nation.

Intelligence and creativity among school-going students are buzzwords. The exponential growth of studies has spurred on the influence of intelligence on creativity; however, the center of the debate is on the effect of the former on the latter. Surprisingly, very few studies have attempted to investigate the confounding effect which connects intelligence with creativity. Based on the notions of multi-theory, this study explores the impact of intelligence on creativity through the mechanism of a mediator and a moderator. The present study collected 307 cross-sectional data using the convenience sampling method. The collected data were analyzed using partial least square based structural equation modeling with the deduction reasoning method. The results reveal that students’ efficacious belief mediates the influence of intelligence on creativity; however, the moderating effect of creative identity is not demonstrated justified. Practical implications and directions for future research are presented in the final section.

The integration of Artificial Intelligence (AI) in business settings is rapidly increasing, yet significant limitations hinder its effective use and adoption. Understanding these limitations and their interrelationships is crucial for enhancing AI implementation. Despite growing research, there is a lack of a comprehensive model that systematically identifies and elucidates the factors influencing AI limitations in business environments. This study employs Interpretive Structural Modeling (ISM), combined with MICMAC analysis and an extensive literature review, to develop such a model. We identified 15 key factors and analyzed their driving and dependence powers to understand their interrelationships. Most factors, such as contextual understanding, transparency, intuition, emotional intelligence, ethics, bias, tacit knowledge, creativity, credibility, and reliability, were found to be autonomous. Accountability and privacy emerged as the strongest driving forces, while trust and adaptability exhibited the highest dependence and lowest driving power. This research offers a comprehensive understanding of AI limitations and their interrelationships, providing valuable insights for managers and businesses. The findings can aid in making more informed decisions about AI implementation and in developing strategies to mitigate these limitations. Furthermore, the study emphasizes the importance of combining AI with human insight to overcome these challenges. However, using the ISM technique could involve subjective judgment from the experts.

In today’s business world, digital technologies have become vital for implementing processes in any company, including the healthcare industry. By utilizing the latest technologies to collect, process, analyze, and transmit data through smart devices and IT infrastructure, it is possible to implement trends in medicine as a value approach, personalization, predictability, and participation. Considering these trends, it is necessary to develop a methodology for creating a management system for medical organizations that can comprehensively and interconnectively develop various elements of the system. This includes business process systems, information systems and application architectures, hardware, and ''smart'' medical equipment that can fully participate in hospital information exchange. These elements should be developed within a single model. An architectural approach can be used to create a model of a medical organization. This paper examines the integration of smart infrastructure in medical organizations, develops requirements for smart infrastructure services, and creates architectural models based on the findings and requirements. Architecture models were developed for the following elements of the Smart Infrastructure: smart wards, wearable devices and telemedicine stands as the most necessary elements in everyday healthcare. The key discovery of the study is the architecture models of the mentioned elements and their integration with the medical organization IT architecture, described in the form of visual models and data exchange tables. The proposed models can be used both for further research or for practical implementation as the reference models to check up and adopt to each individual case. The solutions proposed in the paper give an example of open innovation philosophy, as they are developed on the basis of open information about the innovation activities of various medical organizations, and, at the same, time the results themselves are openly published and open for adaptation by the scientific and practicing medical community.

Previous empirical research suggests that the characteristics of individuals in the board of directors or peak management teams should be examined. There is a significant gap related to the role of women in the upper level of management and disclosure of carbon emissions. Therefore, this study aims to review the impact of women in top management positions on carbon emission disclosure in the ASEAN banking sector. We analyzed data from 146 banks throughout ASEAN from 2018 to 2022, comprising 719 data of observation. Sample selection employed purposive sampling. Statistical analysis was conducted using Stata MP 17 software, with the fixed effect model identified as the appropriate regression model through Chow, LM, and Hausman tests. Our empirical findings reveal a significant influence of women in upper management, specifically on carbon emission disclosure, as assessed through both Board of Directors (BOD) and audit committee perspectives. These findings have implications for investors and policymakers which support ASEAN countries in achieving net zero pledge, so that sustainability goals related to climate change can be achieved.