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Although few studies indicate that financial concerns matter to social entrepreneurs, the literature is unclear about the extent to which a financial motive affects the intention to start a new social enterprise. Moreover, prior research suggests that the intention to start a new enterprise heavily depends on the societal context in which the enterprise operates. Therefore, this study aims to examine the seminal model of social entrepreneurial intention (SEI) developed by Hockerts (2017) in a different social context; additionally, it proposes a new antecedent of SEI – perceived financial security.

This study used two different measurement scales and samples (n = 436 and 241) in a developing country to validate the model and propose a new antecedent, i.e. the perceived financial security, of SEI. Furthermore, the authors employed the partial least square-structural equation model to test the hypotheses.

The results demonstrate that social entrepreneurial self-efficacy, perceived social support and perceived financial security directly predict SEI; they further mediate the relationship between prior experience and SEI. Consequently, the model by Hockerts is extended.

This study established perceived financial security as a strong antecedent of SEI, thereby offering a novel insight that a social entrepreneur can be motivated by potential financial concerns.

Researcher agreed that job performance has a positive effect on productivity as well as an organisation’s efficiency. Thus, this study aims to investigate the impact of mindfulness skill, inclusive leadership (IL), employee work engagement and self-compassion on the overall job performance of secondary school teachers in Hong Kong. It then evaluates the mediating effect of employee work engagement between the relationships of mindfulness skill, IL and job performance, as well as the moderate effect of self-compassion between the relationships of mindfulness skill, IL and employee work engagement.

The sample comprised 263 teachers working from three secondary schools in Sha Tin, Hong Kong. The data was then analysed using Smart PLS version 4.0.9.

The results showed significant positive relationships between mindfulness skill and IL towards employee work engagement and between employee work engagement and job performance; meanwhile, there emerged a significant effect on the relationship between mindfulness skill and IL towards job performance. Furthermore, this research has confirmed that self-compassion did not moderate the relationship between mindfulness skill, IL and employee work engagement, but employee work engagement plays a mediating effect on the relationship between mindfulness skill, IL and job performance.

This research has helped to fill the literature gap by examining the mediating roles of employee work engagement and mediator role of self-compassion in the integrated relationship of multi-factor and job performance. Examining the mediating role of employee work engagement has helped to enhance the understanding of the underlying principle of the indirect influence of mindfulness skill, IL and job performance. The result of this research shows that self-compassion plays a vital role in influencing the employees’ work engagement. Hence, it is important that companies design human resource management policy that enables self-compassion to be used as a consideration psychological-related strategy when structing organisation or teams. It is also crucial for top management and policymakers to define and communicate the organisation’s operating principle, value and goals.

Credit risk evaluation is a crucial task for banks and non-bank financial institutions to support decision-making on granting loans. Most of the current credit risk methods rely solely on expert knowledge or large amounts of data, which causes some problems like variable interactions hard to be identified, models lack interpretability, etc. To address these issues, the authors propose a new approach.

First, the authors improve interpretive structural model (ISM) to better capture and utilize expert knowledge, then combine expert knowledge with big data and the proposed fuzzy interpretive structural model (FISM) and K2 are used for expert knowledge acquisition and big data learning, respectively. The Bayesian network (BN) obtained is used for forward inference and backward inference. Data from Lending Club demonstrates the effectiveness of the proposed model.

Compared with the mainstream risk evaluation methods, the authors’ approach not only has higher accuracy and better presents the interaction between risk variables but also provide decision-makers with the best possible interventions in advance to avoid defaults in the financial field. The credit risk assessment framework based on the proposed method can serve as an effective tool for relevant policymakers.

The authors propose a novel credit risk evaluation approach, namely FISM-K2. It is a decision support method that can improve the ability of decision makers to predict risks and intervene in advance. As an attempt to combine expert knowledge and big data, the authors’ work enriches the research on financial risk.

Blockchain technology (BCT) is one of the latest disruptive innovations, brought along with-it immense scope of diverse applications contributing towards sustainable development (SD). The consistent increase in the publications reveal that the application of BCT for SD has become popular among researchers and practitioners in past few years, in turn, urged for a systematic literature review (SLR) to get an insight into the research journey travelled so far and setting directions for future research in this area. Therefore, this study aims to identify, map and synthesize the available literature on application of BCT for SD.

The automatic and manual search resulted into 1,277 studies from Scopus and Web of Science database. Further applying inclusion and exclusion criterion resulted in bringing out total of 157 studies, which were termed as primary studies. Based on the results of descriptive analysis, conducted through Bibliometric and VOSviewer software, the characteristics of BCT and its key capabilities, contributing towards shaping the recent SD literature, were critically examined. Identified research themes for clustering primary studies were aligned with United Nations Sustainability Development Goals (UNSDG). A mind-map was also prepared on the basis of thematic classification of primary studies.

The research themes “business practice and economic sustainability”, “agriculture and food security” and “business practice and environment sustainability” were found to be the focal points of scholarly attention. Synthesis and analysis of primary studies resulted into classification of research gaps under four categories – theoretical foundation, methodological limitation, research themes and technology implementation challenges. The study was concluded by sensitizing and sanitizing the concrete research questions for future research.

The research findings shall be a roadmap for research scholars, academicians and practitioners to comprehend the present state of knowledge in the domain of “BCT application for SD” and decide upon adopting the future course of action to attain the UNSDGs by the year 2030.

To the best of the authors’ knowledge, the current study is the first attempt in its own sense to analyse and synthesize the available literature on “attaining SD through BCT” using SLR approach.

This study aims to extract knowledge of ancient Chinese scientific and technological documents bibliographic summaries (STDBS) and provide the knowledge graph (KG) comprehensively and systematically. By presenting the relationship among content, discipline, and author, this study focuses on providing services for knowledge discovery of ancient Chinese scientific and technological documents.

This study compiles ancient Chinese STDBS and designs a knowledge mining and graph visualization framework. The authors define the summaries' entities, attributes, and relationships for knowledge representation, use deep learning techniques such as BERT-BiLSTM-CRF models and rules for knowledge extraction, unify the representation of entities for knowledge fusion, and use Neo4j and other visualization techniques for KG construction and application. This study presents the generation, distribution, and evolution of ancient Chinese agricultural scientific and technological knowledge in visualization graphs.

The knowledge mining and graph visualization framework is feasible and effective. The BERT-BiLSTM-CRF model has domain adaptability and accuracy. The knowledge generation of ancient Chinese agricultural scientific and technological documents has distinctive time features. The knowledge distribution is uneven and concentrated, mainly concentrated on C1-Planting and cultivation, C2-Silkworm, and C3-Mulberry and water conservancy. The knowledge evolution is apparent, and differentiation and integration coexist.

This study is the first to visually present the knowledge connotation and association of ancient Chinese STDBS. It solves the problems of the lack of in-depth knowledge mining and connotation visualization of ancient Chinese STDBS.

This study aims to explore the impact of supply chain governance (SCG) on supply chain resilience (SCR) in China, as well as the mediating role of supply chain finance (SCF) and the moderating role of digital technology adoption (DTA).

Based on resource orchestration theory (ROT), this paper constructs a theoretical model to examine the influence of SCG on SCR. Employing data collected from 312 Chinese firms, multiple regression is conducted to test the theoretical hypotheses.

The results indicate that both relational governance (RG) and contractual governance (CG) significantly enhance SCR and SCF. In addition, SCF plays a mediating role in the relationship between SCG and SCR. The study also finds that DTA has a positive moderating effect on the relationship between SCG and SCF.

First, based on ROT, this paper clarifies the nuanced driving effects of RG and CG on SCR. Second, SCF is introduced as an intermediary process between SCG and SCR, which reveals the inner mechanisms between SCG and SCR. Moreover, this study contributes to investigating the contingent role of DTA in the relationship between SCG and SCF.

New business practices and the globalization of markets force firms to take innovation as the fundamental pillar of their competitive strategy. Research and Development (R&D) plays a vital role in innovation. As technology advances and product life cycles become shorter, firms rely on R&D as a strategy to invigorate innovation. R&D project portfolio selection is a complex and challenging task. Despite the management's efforts to implement the best project portfolio selection practices, many projects continue to fail or miss their target. The problem is that selecting R&D projects requires a deep understanding of strategic vision and technical capabilities. However, many decision-makers lack technological insight or strategic vision. This article aims to provide a method to capitalize on the expertise of R&D professionals to assist managers in making informed and effective decisions. It also provides a framework for aligning the portfolio of R&D projects with the organizational vision and mission.

This article proposes a new strategic approach for R&D project portfolio selection using efficiency-uncertainty maps.

The proposed strategy plane helps decision-makers align R&D project portfolios with their strategies to combine a strategic view and numerical analysis in this research. The proposed strategy plane consists of four areas: Exploitation Zone, Challenge Zone, Desperation Zone and Discretion Zone. Mapping the project into this strategic plane would help decision-makers align their project portfolio according to the corporate perspectives.

The new approach combines the efficiency and uncertainty dimensions in portfolio selection into an integrated framework that: (i) provides a complete representation of the stochastic decision-making processes, (ii) models the endogenous uncertainty inherent in the project selection process and (iii) proposes a computationally practical and visually unique solution procedure for classifying desirable and undesirable R&D projects.

The objectives of the study were to identify the effects of blockchain technology (BT) on the university librarians, the impact of BT on the university library services and to reveal the challenges to adopt BT in the university libraries.

A systematic literature review was applied to address the objectives of the study. Around 25 studies published in peer-reviewed journals were selected to conduct the study.

The findings of the study revealed that blockchain technology (BT) has positive effects on the university librarians as it assists them in digital resources management, provision of integrated library services, effective records management and continued professional development. The study also displayed that BT has a positive impact on the university libraries through effective information management, user privacy, collaboration, technological innovation and access control. Results also revealed that technical issues, financial constraints, security problems, skill issues and sociocultural issues created challenges to adopt BT in the university libraries.

The study has offered theoretical implications for future investigators through the provision of innovative literature on the prospectus and challenges associated with blockchain in the context of librarianship. The study has also provided practical implications for management bodies by offering recommendations for the successful adoption of blockchain in the university libraries. Additionally, a framework has been developed to adopt BT successfully in the university libraries for the delivery of smart library services to library patrons.

Logistics sector is recognized as one of the core enablers of the economic development of a nation. However, inefficiency in logistics operations impedes the achievement of intended targets by increasing the cost of doing business. Also, it is difficult to improve the efficiency of a country’s logistics operations without a metric for evaluating and understanding logistics capabilities and efficiency. Therefore, the present study has developed In-country Logistics Performance Index (ILP Index) to propose a benchmarking tool to measure the in-country logistics competitiveness, particularly in the setting of emerging economies, i.e. India.

This study has developed a unified index using principal component analysis and quintile approach. In addition, the proposed index relies on several dimensions that are developed and illustrated using quantitative secondary panel data.

The findings of this study reveal that the quality of infrastructure, economy, and telecommunications are the three most important dimensions that may significantly support the growth of the transportation and logistics sector. The results reveal that Gujarat, Tamil Nadu, and Maharashtra are the top performers whereas, Bihar, Jharkhand, and Jammu and Kashmir scores the least due to the insufficient logistics infrastructure as compared to other Indian states.

Given the extensive focus on international-level logistics index (like World Bank’s LPI) in the existing literature, this study intends to develop in-country logistics index to evaluate the logistics capabilities at the regional and state level. In addition, unlike prior studies, this study utilizes quantitative secondary data to eliminate cognitive and opinion bias. Moreover, this benchmarking tool would assist decision-makers in idealizing standard practices toward sustainable logistics operations. Additionally, the ILP index could serve the international investors in crucial decision-making, as it provides valuable insights into a country’s logistics readiness, influencing their investment choices and trade preferences. Finally, the proposed approach is adaptable to measuring the overall performance of any other industry/economy.

The public distribution system (PDS) is an Indian food security system established to manage the distribution of food grains at affordable prices. As a result of the population explosion, the long-established PDS system finds it challenging to maintain operational efficiency, quality, trust and transparency. This paper explores the possibility of leveraging blockchain technology to overcome these operational hurdles.

Through a literature review and expert interactions, the present research identifies critical success factors in terms of enablers and barriers that influence the adoption of blockchain technology in PDS. Furthermore, we propose two independent interpretive structural models (ISM) and MICMAC to characterize these attributes.

The research identifies 15 distinct enablers and ten barriers that influence the diffusion of the latest technology in the sector at focus. The analyses disclose the interrelationships/dependencies among these enablers and between barriers, along with their individual driving power and dependence power.

The research showcases the importance of automating the system and illustrates how the features of blockchain technology can assist in augmenting stakeholder satisfaction levels. However, poor or nonexistent government regulations and patronage are found to be the major impediments to adoption. The research also delineates the cost implications of this barrier through its interrelationships with other barriers.

Interesting inferences are drawn from the models that offer actionable insights for the industry, government and technologists for improving PDS performance. Such interventions will ensure national food security through enhanced trust and transparency, which can further improve efficiency and effectiveness.