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The paper examines whether, along with the financial performance, the disclosure of research and development (R&D) expenses, patent portfolios, patent citations and innovation activities affect the market capitalization of Russian companies.

The paper opted for a set of techniques including bag-of-words (BoW) to retrieve additional innovation-related data from companies' annual reports, self-organizing maps (SOM) to perform visual exploratory analysis and panel data regression (PDR) to conduct confirmatory analysis using data on 74 Russian publicly traded companies for the period 2013–2019.

The paper observes that the disclosure of nonfinancial data on R&D, patents and primarily product and marketing innovations positively affects the market capitalization of the largest Russian companies, which are mainly focused on energy, raw materials and utilities and are operating on international markets. The study suggests that these companies are financially well-resourced to innovate at risk and thus to provide positive signals to stakeholders and external agents.

Our findings are important to management, investors, financial analysts, regulators and various agencies providing guidance on corporate governance and sustainability reporting. However, the authors acknowledge that the research results may lack generalizability due to the sample covering a single national context. Researchers are encouraged to test the proposed approach further on other countries' data by using the compiled lexicons.

The study aims to expand the domains of signaling theory and market valuation by providing new insights into the impact that companies' reporting on R&D, patents and innovation activities has on market capitalization. New nonfinancial factors that previous research does not investigate – innovation disclosure indicators (IDI) – are tested.

With the rapid development of Internet technology, cybersecurity threats such as security loopholes, data leaks, network fraud, and ransomware have become increasingly prominent, and organized and purposeful cyberattacks have increased, posing more challenges to cybersecurity protection. Therefore, reliable network risk assessment methods and effective network security protection schemes are urgently needed.

Based on the dynamic behavior patterns of attackers and defenders, a Bayesian network attack graph is constructed, and a multitarget risk dynamic assessment model is proposed based on network availability, network utilization impact and vulnerability attack possibility. Then, the self-organizing multiobjective evolutionary algorithm based on grey wolf optimization is proposed. And the authors use this algorithm to solve the multiobjective risk assessment model, and a variety of different attack strategies are obtained.

The experimental results demonstrate that the method yields 29 distinct attack strategies, and then attacker's preferences can be obtained according to these attack strategies. Furthermore, the method efficiently addresses the security assessment problem involving multiple decision variables, thereby providing constructive guidance for the construction of security network, security reinforcement and active defense.

A method for network risk assessment methods is given. And this study proposed a multiobjective risk dynamic assessment model based on network availability, network utilization impact and the possibility of vulnerability attacks. The example demonstrates the effectiveness of the method in addressing network security risks.

This study aims to discuss the state-of-the-art digital factory (DF) development combining digital twins (DTs), sensing devices, laser additive manufacturing (LAM) and subtractive manufacturing (SM) processes. The current shortcomings and outlook of the DF also have been highlighted. A DF is a state-of-the-art manufacturing facility that uses innovative technologies, including automation, artificial intelligence (AI), the Internet of Things, additive manufacturing (AM), SM, hybrid manufacturing (HM), sensors for real-time feedback and control, and a DT, to streamline and improve manufacturing operations.

This study presents a novel perspective on DF development using laser-based AM, SM, sensors and DTs. Recent developments in laser-based AM, SM, sensors and DTs have been compiled. This study has been developed using systematic reviews and meta-analyses (PRISMA) guidelines, discussing literature on the DTs for laser-based AM, particularly laser powder bed fusion and direct energy deposition, in-situ monitoring and control equipment, SM and HM. The principal goal of this study is to highlight the aspects of DF and its development using existing techniques.

A comprehensive literature review finds a substantial lack of complete techniques that incorporate cyber-physical systems, advanced data analytics, AI, standardized interoperability, human–machine cooperation and scalable adaptability. The suggested DF effectively fills this void by integrating cyber-physical system components, including DT, AM, SM and sensors into the manufacturing process. Using sophisticated data analytics and AI algorithms, the DF facilitates real-time data analysis, predictive maintenance, quality control and optimal resource allocation. In addition, the suggested DF ensures interoperability between diverse devices and systems by emphasizing standardized communication protocols and interfaces. The modular and adaptable architecture of the DF enables scalability and adaptation, allowing for rapid reaction to market conditions.

Based on the need of DF, this review presents a comprehensive approach to DF development using DTs, sensing devices, LAM and SM processes and provides current progress in this domain.

The main goal of this research work is the optimization of the purchasing business process in the Moroccan public sector in terms of transparency and budgetary optimization. The authors have worked on the public university as an implementation field.

The design of the research work followed the design science research (DSR) methodology for information systems. DSR is a research paradigm wherein a designer answers questions relevant to human problems through the creation of innovative artifacts, thereby contributing new knowledge to the body of scientific evidence. The authors have adopted a techno-functional approach. The technical part consists of the development of an intelligent recommendation system that supports the choice of optimal information technology (IT) equipment for decision-makers. This intelligent recommendation system relies on a set of functional and business concepts, namely the Moroccan normative laws and Control Objectives for Information and Related Technology's (COBIT) guidelines in information system governance.

The modeling of business processes in public universities is established using business process model and notation (BPMN) in accordance with official regulations. The set of BPMN models constitute a powerful repository not only for business process execution but also for further optimization. Governance generally aims to reduce budgetary wastes, and the authors' recommendation system demonstrates a technical and methodological approach enabling this feature. Implementation of artificial intelligence techniques can bring great value in terms of transparency and fluidity in purchasing business process execution.

Business limitations: First, the proposed system was modeled to handle one type products, which are computer-related equipment. Hence, the authors intend to extend the model to other types of products in future works. Conversely, the system proposes optimal purchasing order and assumes that decision makers will rely on this optimal purchasing order to choose between offers. In fact, as a perspective, the authors plan to work on a complete automation of the workflow to also include vendor selection and offer validation. Technical limitations: Natural language processing (NLP) is a widely used sentiment analysis (SA) technique that enabled the authors to validate the proposed system. Even working on samples of datasets, the authors noticed NLP dependency on huge computing power. The authors intend to experiment with learning and knowledge-based SA and assess the' computing power consumption and accuracy of the analysis compared to NLP. Another technical limitation is related to the web scraping technique; in fact, the users' reviews are crucial for the authors' system. To guarantee timeliness and reliable reviews, the system has to look automatically in websites, which confront the authors with the limitations of the web scraping like the permanent changing of website structure and scraping restrictions.

The modeling of business processes in public universities is established using BPMN in accordance with official regulations. The set of BPMN models constitute a powerful repository not only for business process execution but also for further optimization. Governance generally aims to reduce budgetary wastes, and the authors' recommendation system demonstrates a technical and methodological approach enabling this feature.

The adopted techno-functional approach enabled the authors to bring information system governance from a highly abstract level to a practical implementation where the theoretical best practices and guidelines are transformed to a tangible application.

Nowadays, a prominent research area is the development of competitive advantages in companies, due to their environmental commitment and orientation. Based on resource-based view (RBV) and institutional theory (InT), this paper aims to investigate the influence of internal and external orientation on businesses' sustainable performance while considering the effect of sustainable supply chain management (SSCM) practices.

Data from 351 manufacturing companies in the Kingdom of Saudi Arabia have been collected and analysed through structural equation modelling (SEM) using the partial least squares (PLS) method.

The results indicated that both internal and external environmental orientation have important effects on SSCM practices, which in turn have a considerable beneficial effect on environmental, social and economic performance.

Although SSCM is constantly gaining ground in the literature, most SSCM research and models examine its effects, antecedents or motivation, mainly adopting a qualitative approach. Research on the topic adopting a large-scale empirical approach is still limited. In this context, this study contributes to the SSCM management literature by exploring the role of environmental orientation in facilitating the adoption of SSCM practices and improving companies' performance.

This study aims to propose a more precise method for robust design optimization of mechanical structures with black-box problems, while also considering the efficiency of uncertainty analysis.

The method first introduces a dual adaptive chaotic flower pollination algorithm (DACFPA) to overcome the shortcomings of the original flower pollination algorithm (FPA), such as its susceptibility to poor accuracy and convergence efficiency when dealing with complex optimization problems. Furthermore, a DACFPA-Kriging model is developed by optimizing the relevant parameter of Kriging model via DACFPA. Finally, the dual Kriging model is constructed to improve the efficiency of uncertainty analysis, and a robust design optimization method based on DACFPA-Dual-Kriging is proposed.

The DACFPA outperforms the FPA, particle swarm optimization and gray wolf optimization algorithms in terms of solution accuracy, convergence speed and capacity to avoid local optimal solutions. Additionally, the DACFPA-Kriging model exhibits superior prediction accuracy and robustness contrasted with the original Kriging and FPA-Kriging. The proposed method for robust design optimization based on DACFPA-Dual-Kriging is applied to the motor hanger of the electric multiple units as an engineering case study, and the results confirm a significant reduction in the fluctuation of the maximum equivalent stress.

This study represents the initial attempt to enhance the prediction accuracy of the Kriging model using the improved FPA and to combine the dual Kriging model for uncertainty analysis, providing an idea for the robust optimization design of mechanical structure with black-box problem.

In recent years, the proliferation of social commerce (s-commerce) has attracted many researchers to investigate the drivers of individuals' intentions. However, the empirical results reported in these studies were fragmented and inconsistent. This has led various meta-analyses to synthesize these findings, but without including a large number of s-commerce studies. In addition, investigating meta-analytically the effects of moderators such as the six dimensions of Hofstede's national culture is still lacking.

Drawing on nine theories and models, this meta-analysis aims to summarize the findings reported in 109 s-commerce studies published between 2011 and 2021 and to examine the moderating role of national culture. The correlation coefficient (r) has been used as the main effect size for this study. Based on the random-effects method, the CMA V3 software has been employed to calculate the weighted mean effect sizes.

The meta-analysis results showed that all the 11 hypothesized direct relationships are positive and significant. The moderator results also revealed that five out of six cultural dimensions significantly moderate the examined associations.

This research serves to enrich the existing s-commerce literature by addressing contradictory and mixed results reported in the empirical studies. This study is one of the first of its kind to investigate the role of Hofstede's six cultural dimensions as moderators in the field of s-commerce using the meta-analytic techniques.

The purpose of this paper is to explore and analyze the context and concept of higher education for sustainable development (HESD) in Nepalese higher educational institutions (HEIs).

The research gathers facts and information both from primary and secondary sources. Five open-ended questions were developed to interview university high-ranking officials, such as VCs, registrars, deans and the chief of the planning division.

The inclusion of 17 sustainable development goals in the country’s Fifteenth Plan, national policy documents, and the University Grants Commission-led higher education reform programme provides sufficient context for HESD. In the absence of HESD literacy and a persuasive strategy, university leadership is less active in lobbying for SDG 4.7 with the government and funding agencies, university faculties and trade unions. In fact, both insights and initiatives to conceptualize HESD are lacking.

This study’s setting is distinct and the interpretation of the HESD concept is based on a small sample size. Thus, the generalization of its findings is intrinsically limited in the context of a country in the geographic region. However, the findings of this research provide practical insights to implement HESD in Nepalese HEIs.

This study is intended to inform and prepare the Nepalese academic community, urging HEIs to implement HESD with a well-defined plan of action. It contributes to the literature by considering how the current context and concept of HESD suites to promote sustainability in Nepalese HEIs, transforming the traditional academic structure and making academic leaders aware that HESD is only a means to an end.

This research is Nepal’s first study of its type with a broad understanding of the context and concept of HESD. It also provides information for HEI leaders on how to initiate the HESD acceleration process.

The research aims to identify the impacts of strategic knowledge (SK) and information technology capabilities (ITC) on innovation ambidexterity (IAM) through business process performance (BPP).

The research framework is developed based on the theoretical grounding of resource orchestration (RO) (SK and ITC) impacts on IAM. The structural equation modeling (SEM) technique was used to test the research framework on a sample of 441 responses from Brazilian firms.

The results suggest that SK and ITC facilitate BPP, resulting in IAM. The findings also suggested differences in path coefficients in the SK and ITC of the business value generation process framework under environmental turbulence (ET). Finally, a strong SK of ITC is especially important in enabling BPP and IAM in large firms. Another case of most manufacturing and service firms demonstrated that both SK and ITC are essential to impacting IAM through BPP mediation.

The findings provide insight into how professionals can think and plan carefully to align SK and ITC for achieving balanced innovation and improving BPP in the dynamic business environment.

The study establishes a relationship between SK, ITC, BPP and IAM. The study developed novel constructs of SK and ITC and tested them, which gives new insight and links among the constructs.

This research study aims to inquire into the technostress phenomenon at an organizational level from machine learning (ML) and artificial intelligence (AI) deployment. The authors investigated the role of ML and AI automation-augmentation paradox and the socio-technical systems as coping mechanisms for technostress management amongst managers.

The authors applied an exploratory qualitative method and conducted in-depth interviews based on a semi-structured interview questionnaire. Data were collected from 26 subject matter experts. The data transcripts were analyzed using thematic content analysis.

The study results indicated that role ambiguity, job insecurity and the technology environment contributed to technostress because of ML and AI technologies deployment. Complexity, uncertainty, reliability and usefulness were primary technology environment-related stress. The novel integration of ML and AI automation-augmentation interdependence, along with socio-technical systems, could be effectively used for technostress management at the organizational level.

This research study contributed to theoretical discourse regarding the technostress in organizations because of increased ML and AI technologies deployment. This study identified the main techno stressors and contributed critical and novel insights regarding the theorization of coping mechanisms for technostress management in organizations from ML and AI deployment.

The phenomenon of technostress because of ML and AI technologies could have restricting effects on organizational performance. Executives could follow the simultaneous deployment of ML and AI technologies-based automation-augmentation strategy along with socio-technical measures to cope with technostress. Managers could support the technical up-skilling of employees, the realization of ML and AI value, the implementation of technology-driven change management and strategic planning of ML and AI technologies deployment.

This research study was among the first few studies providing critical insights regarding the technostress at the organizational level because of ML and AI deployment. This research study integrated the novel theoretical paradigm of ML and AI automation-augmentation paradox and the socio-technical systems as coping mechanisms for technostress management.