Search results

This study aims to extend and explore patterns and trends of research in the linkage of big data and knowledge management (KM) by identifying growth in terms of numbers of papers and current and emerging themes and to propose areas of future research.

The study was conducted by systematically extracting, analysing and synthesizing the literature related to linkage between big data and KM published in top-tier journals in Web of Science (WOS) and Scopus databases by exploiting bibliometric techniques along with theory, context, characteristics, methodology (TCCM) analysis.

The study unfolds four major themes of linkage between big data and KM research, namely (1) conceptual understanding of big data as an enabler for KM, (2) big data–based models and frameworks for KM, (3) big data as a predictor variable in KM context and (4) big data applications and capabilities. It also highlights TCCM of big data and KM research through which it integrates a few previously reported themes and suggests some new themes.

This study extends advances in the previous reviews by adding a new time line, identifying new themes and helping in the understanding of complex and emerging field of linkage between big data and KM. The study outlines a holistic view of the research area and suggests future directions for flourishing in this research area.

This study highlights the role of big data in KM context resulting in enhancement of organizational performance and efficiency. A summary of existing literature and future avenues in this direction will help, guide and motivate managers to think beyond traditional data and incorporate big data into organizational knowledge infrastructure in order to get competitive advantage.

To the best of authors’ knowledge, the present study is the first study to go deeper into understanding of big data and KM research using bibliometric and TCCM analysis and thus adds a new theoretical perspective to existing literature.

This paper aims to derive a model that explores how the interplay between knowledge integration capability and innovation impacts strategic orientation, leading to the attainment of sustainable competitive advantage. The study considers the constituents of strategic orientation, namely, customer orientation, competitor orientation and technology orientation, as the basis for achieving sustainable competitive advantage. The study suggests that the firm’s capacity for integrating external and internal knowledge shapes how strategic orientation influences sustainable competitive advantage through service innovation.

This empirical research relies on qualitative and quantitative data gathered from telecom professionals to assess how knowledge integration and service innovation influence sustained competitive advantage. Structured equation modeling is used to examine the model and its interrelationships.

The research establishes significant relationships between strategic orientations, knowledge integration capability, service innovation and sustainable competitive advantage. Knowledge integration capability and service innovation are found to mediate the relationship between strategic orientations and the achievement of sustainable competitive advantage.

The study highlights the significant contribution of a firm’s knowledge integration capability in driving service innovation, especially in technology-intensive service industries facing hypercompetition. It also advocates prioritizing technology orientation and integrating knowledge from internal and external sources for competitive advantage.

To the best of the authors’ knowledge, this study is the first to model the effect of knowledge integration capability and service innovation on strategic orientation-led sustainable competitive advantage.

This study explores the implementation of personalised information access, driven by machine learning, in Nigerian public libraries. The purpose of this paper is to address existing challenges, enhance the user experience and bridge the digital divide by leveraging advanced technologies.

This study assesses the current state of Nigerian public libraries, emphasising challenges such as underfunding and lack of technology adoption. It proposes the integration of machine learning to provide personalised recommendations, predictive analytics for collection development and improved information retrieval processes.

The findings underscore the transformative potential of machine learning in Nigerian public libraries, offering tailored services, optimising resource allocation and fostering inclusivity. Challenges, including financial constraints and ethical considerations, are acknowledged.

This study contributes to the literature by outlining strategies for responsible implementation and emphasising transparency, user consent and diversity. The research highlights future directions, anticipating advancements in recommendation systems and collaborative efforts for impactful solutions.

This study uses a comprehensive theoretical framework that combines social cognitive theory and neighborhood effect to investigate the influence of neighborhood effects on farmers’ outcome expectations, observational learning and self-efficacy. This study aims is to analyze the mechanisms that underlie the adoption of social media by farmers for knowledge exchange in the agricultural context. Specifically, this research explores the role of neighborhood effects, outcome expectations, observational learning and self-efficacy in shaping farmers’ decision-making process regarding the use of social media platforms for exchanging agricultural knowledge.

The study data was collected through a sample survey conducted among 570 agricultural households residing in the provinces of Thai Nguyen, Cao Bang, Bac Kan and Phu Tho, located in the northern region of Vietnam. To analyze the data, structural equation modeling was used as the statistical technique of choice.

The findings of the study indicate a significant influence of neighborhood effects on outcome expectations, observational learning and self-efficacy. These factors, derived from social cognitive theory, also exhibit a positive association with farmers’ adoption of social media for knowledge exchange. Additionally, the study highlights that neighborhood contribute to a favorable adoption of social media among farmers via outcome expectations, observational learning, and self-efficacy.

The study is limited in examining farmers’ social media adoption for agriculture knowledge exchange in Northern mountainous area of Vietnam. This study could be replicated across various regions or nations, providing comparative insights into the adoption of social media among farmers for knowledge exchange.

The study findings suggest practical and innovative means to promote farmers’ social media adoption for agriculture knowledge exchange.

This study presents a pioneering approach by integrating social cognitive theory and neighborhood effect to elucidate the factors influencing farmers’ adoption of social media for the purpose of agriculture knowledge exchange.

The Scottish economy was experiencing steady growth prior the hit of the COVID-19, with the pandemic causing the government to announce extreme lockdown measures with unprecedented impact on small and medium-sized enterprises (SMEs). Whilst some of the industry sectors in Scotland took a lighter hit, a large part of the organizations had to either adapt or completely disrupt their business. An essential aspect of their survival, risk management (RM) was among the areas requiring the most significant acceleration. This study compared the RM practices implemented by Scottish SMEs prior and after the outbreak as well as examined the attitudes of key decision-makers in the SMEs in relation to risk, including their perceived readiness for another crisis of similar significance.

An online survey based on ISO 31000 RM guidelines was distributed to 232 Scottish SMEs. Based on the official government reports and existing knowledge on how SMEs around the world have handled crisis events within the past 20 years, the authors developed the hypothesis that crisis events significantly accelerate SMEs' RM implementation. Around 13 items were tested in relation to the hypothesis and responses were tested via two-tailed T-test to establish significant statistical difference.

The research provides insight into the current state of risk management practices implemented by Scottish SMEs. As expected, SMEs showed significant difference in their RM implementation prior and after the COVID-19 outbreak. Whilst this has been viewed as a positive, motivations, priorities and approaches in managing risk demonstrated by the SMEs is questionable with views to their sustainable long-term recovery. The study highlights the lack of confidence instilled within the SMEs that they can handle another crisis of similar significance and provides directions for further investigation and improvements with the aim of helping the SMEs prepare better to mitigate the consequences of future crisis events.

In academic sense, the study offers a tested universal framework and a detailed questionnaire for assessment of RM strategy, applicable to organisations of various type, size and geography. Several implications with regards to managerial practices have been highlighted, including the neglect of the SMEs’ own internal environments and its significance in their risk strategies, the predominantly reactive approach to RM displayed by most Scottish SMEs as well as the neglect of compliance risk leading to potential quality and customer satisfaction issues preventing SMEs from full post-crisis recovery.

In order to estimate the value of semi-automated subject indexing in operative library catalogues, the study aimed to investigate five different automated implementations of an open source software package on a large set of Swedish union catalogue metadata records, with Dewey Decimal Classification (DDC) as the target classification system. It also aimed to contribute to the body of research on aboutness and related challenges in automated subject indexing and evaluation.

On a sample of over 230,000 records with close to 12,000 distinct DDC classes, an open source tool Annif, developed by the National Library of Finland, was applied in the following implementations: lexical algorithm, support vector classifier, fastText, Omikuji Bonsai and an ensemble approach combing the former four. A qualitative study involving two senior catalogue librarians and three students of library and information studies was also conducted to investigate the value and inter-rater agreement of automatically assigned classes, on a sample of 60 records.

The best results were achieved using the ensemble approach that achieved 66.82% accuracy on the three-digit DDC classification task. The qualitative study confirmed earlier studies reporting low inter-rater agreement but also pointed to the potential value of automatically assigned classes as additional access points in information retrieval.

The paper presents an extensive study of automated classification in an operative library catalogue, accompanied by a qualitative study of automated classes. It demonstrates the value of applying semi-automated indexing in operative information retrieval systems.

The identification of network user relationship in Fancircle contributes to quantifying the violence index of user text, mining the internal correlation of network behaviors among users, which provides necessary data support for the construction of knowledge graph.

A correlation identification method based on sentiment analysis (CRDM-SA) is put forward by extracting user semantic information, as well as introducing violent sentiment membership. To be specific, the topic of the implementation of topology mapping in the community can be obtained based on self-built field of violent sentiment dictionary (VSD) by extracting user text information. Afterward, the violence index of the user text is calculated to quantify the fuzzy sentiment representation between the user and the topic. Finally, the multi-granularity violence association rules mining of user text is realized by constructing violence fuzzy concept lattice.

It is helpful to reveal the internal relationship of online violence under complex network environment. In that case, the sentiment dependence of users can be characterized from a granular perspective.

The membership degree of violent sentiment into user relationship recognition in Fancircle community is introduced, and a text sentiment association recognition method based on VSD is proposed. By calculating the value of violent sentiment in the user text, the annotation of violent sentiment in the topic dimension of the text is achieved, and the partial order relation between fuzzy concepts of violence under the effective confidence threshold is utilized to obtain the association relation.

Forcibly displaced refugee entrepreneurs face extra challenges that are beyond the barriers that face immigrants and hosts in their journey towards starting up, growing and developing their businesses. This study aims to critically examine the opportunity identification (OI) of forcibly displaced refugee minority women entrepreneurs in Glasgow.

Qualitative semi-structured face-to-face interviews with 10 participants were conducted, including 6 forcibly displaced women entrepreneurs and 4 stakeholders. A six-step thematic framework for data analysis with NVivo 11 was used.

Findings revealed that forcibly displaced refugee women’s entrepreneurial opportunities occur through a dynamic identification, discovery, recognition and exploitation of opportunities. Women-led businesses remain underdeveloped due to restrictions imposed by the Scottish Government, Westminster Government and local council authorities.

This study was only conducted in Glasgow areas, although this is where most of the forcibly displaced groups live. The research team suggests that further studies with a large sample cutting across more local authorities beyond Glasgow should be conducted.

The empirical findings serve practitioners, local authorities business managers, researchers and policymakers. The study reveals a gap and lack of support and specific training and programmes that could positively benefit forcibly displaced refugee women entrepreneurs within the host communities.

The novelty of this study lies in the research topic which is still understudied. This is together with the new empirical findings of entrepreneurial OI and the provision of insights related to challenges facing forcibly displaced refugee women entrepreneurs in Glasgow.

In recent times, the field of corporate intelligence has gained substantial prominence, employing advanced data analysis techniques to yield pivotal insights for instantaneous strategic and tactical decision-making. Expanding beyond rudimentary post observation and analysis, social media analytics unfolds a comprehensive exploration of diverse data streams encompassing social media platforms and blogs, thereby facilitating an all-encompassing understanding of the dynamic social customer landscape. During an extensive evaluation of social media presence, various indicators such as popularity, impressions, user engagement, content flow, and brand references undergo meticulous scrutiny. Invaluable intelligence lies within user-generated data stemming from social media platforms, encompassing valuable customer perspectives, feedback, and recommendations that have the potential to revolutionize numerous operational facets, including supply chain management. Despite its intrinsic worth, the actual business value of social media data is frequently overshadowed due to the pervasive abundance of content saturating the digital realm. In response to this concern, the present study introduces a cutting-edge system known as the Enterprise Just-in-time Decision Support System (EJDSS).

Leveraging deep learning techniques and advanced analytics of social media data, the EJDSS aims to propel business operations forward. Specifically tailored to the domain of marketing, the framework delineates a practical methodology for extracting invaluable insights from the vast expanse of social data. This scholarly work offers a comprehensive overview of fundamental principles, pertinent challenges, functional aspects, and significant advancements in the realm of extensive social data analysis. Moreover, it presents compelling real-world scenarios that vividly illustrate the tangible advantages companies stand to gain by incorporating social data analytics into their decision-making processes and capitalizing on emerging investment prospects.

To substantiate the efficacy of the EJDSS, a detailed case study centered around reverse logistics resource recycling is presented, accompanied by experimental findings that underscore the system’s exceptional performance. The study showcases remarkable precision, robustness, F1 score, and variance statistics, attaining impressive figures of 83.62%, 78.44%, 83.67%, and 3.79%, respectively.

This scholarly work offers a comprehensive overview of fundamental principles, pertinent challenges, functional aspects, and significant advancements in the realm of extensive social data analysis. Moreover, it presents compelling real-world scenarios that vividly illustrate the tangible advantages companies stand to gain by incorporating social data analytics into their decision-making processes and capitalizing on emerging investment prospects.

The primary objective of this paper is to explore how space organizations can incorporate children and imagination in their accountability-based accounting and decision-making processes.

This study centers on stakeholder engagement with children, specifically examining the drawing competition associated with the CHaracterising ExOPlanet Satellite (CHEOPS) space exploration mission. We employ a multidimensional research design consisting of both an interpretive approach to the 2,748 space-related drawings submitted by children across Europe to the CHEOPS drawing competition in 2015 and a content analysis of 46 media releases published by ESA and the University of Bern, the key partners of the CHEOPS mission.

Our analysis of the children’s drawings and the CHEOPS media releases indicates that the related organizations account for some of the children’s visions and imaginations, but shortcomings exist in addressing the ethical and space environmental concerns related to space exploration. We explore implications for the space accounting agenda by applying the critical dialogic accountability framework proposed by Dillard and Vinnari (2019), which allows for a discussion on an outline for action by incorporating intergenerational equity (Thomson et al., 2018) and moral imagination (Werhane, 1999).

This study offers a novel exploration of a largely overlooked yet crucial stakeholder group: children. By focusing on their unique perspectives and imaginative capabilities, the paper brings forth the voices of those who will inherit the future of space exploration. Employing children’s drawings as a medium of symbolic communication, this research study offers fresh insights into their perceptions, particularly relevant to space accounting. This innovative approach not only enriches the literature on stakeholder engagement and accountability but also provides space organizations with valuable guidance on fostering inclusivity and ensuring that the interests of future generations are considered in decision-making processes.