Sanjay Kaushal, Austin Milward Nyoni and Aarti Sharma
The purpose of the present study is to establish the emerging trend of studies on knowledge management (KM) strategy from 2007 to 2021 and identify the most studied constructs…
Abstract
Purpose
The purpose of the present study is to establish the emerging trend of studies on knowledge management (KM) strategy from 2007 to 2021 and identify the most studied constructs, methodologies used and gaps, thereby suggesting future directions.
Design/methodology/approach
Guided by items on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, the study analyzed 46 articles published within the 15 years under review.
Findings
An upward trend in KM strategy studies published from 2007 to 2021 emerged, indicating researchers' growing interest in the topic. Further, the studies reaffirmed the essence of having a KM strategy alongside other functional strategies for an organization's outstanding performance. Key KM strategy antecedents were identified: resource availability, communication, business environment, stakeholder participation, organizational culture and incentives. The need to align the KM strategy and other functional strategies with the overall business strategy was also established as critical. Finally, gaps in study methodologies and extant literature were identified, leading to suggestions for future directions.
Originality/value
The study provides valuable insights regarding the emerging trend of studies on KM strategy over the 15 years, identification of methodologies used in the studies and the most studied constructs. To this effect, the study's uniqueness lies in the identified gaps and recommendations made for future research directions as it strives to bridge the identified gaps.
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Aida Khakimova, Oleg Zolotarev and Sanjay Kaushal
Effective communication is crucial in the medical field where different stakeholders use various terminologies to describe and classify healthcare concepts such as ICD, SNOMED CT…
Abstract
Purpose
Effective communication is crucial in the medical field where different stakeholders use various terminologies to describe and classify healthcare concepts such as ICD, SNOMED CT, UMLS and MeSH, but the problem of polysemy can make natural language processing difficult. This study explores the contextual meanings of the term “pattern” in the biomedical literature, compares them to existing definitions, annotates a corpus for use in machine learning and proposes new definitions of terms such as “Syndrome, feature” and “pattern recognition.”
Design/methodology/approach
Entrez API was used to retrieve articles form PubMed for the study which assembled a corpus of 398 articles using a search query for the ambiguous term “pattern” in the titles or abstracts. The python NLTK library was used to extract the terms and their contexts, and an expert check was carried out. To understand the various meanings of the term, the contextual environment was analyzed by extracting the surrounding words of the term. The expert determined the appropriate size of the context for analysis to gain a more nuanced understanding of the different meanings of the term pattern.
Findings
The study found that the categories of meanings of the term “pattern” are broader in biomedical publications than in common definitions, and new categories have been emerging from the term's use in the biomedical field. The study highlights the importance of annotated corpora in advancing natural language processing techniques and provides valuable insights into the nuances of biomedical language.
Originality/value
The study's findings demonstrate the importance of exploring contextual meanings and proposing new definitions of terms in the biomedical field to improve natural language processing techniques.
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Sanjay Kaushal and Austin Milward Nyoni
This study aims to investigate the factors that lead to the failure of some rewards to induce knowledge sharing behavior among employees, with much focus on employees’ attitudes…
Abstract
Purpose
This study aims to investigate the factors that lead to the failure of some rewards to induce knowledge sharing behavior among employees, with much focus on employees’ attitudes and leadership’s knowledge of employees’ preferences, and presents a model that depicts the linkages.
Design/methodology/approach
To investigate why the provision of some rewards fails to induce knowledge sharing behavior among employees, this study uses the preferred reporting items for systematic reviews and meta-analyses framework to identify and analyze 56 articles published from 2000 to 2021.
Findings
Knowledge sharing is positively linked to organizational performance. Further, employees’ negative attitudes toward a reward system negatively relate to knowledge sharing behavior. Furthermore, management’s lack of knowledge of employees’ preferences regarding rewards leads to the provision of incorrect rewards that do not enhance knowledge sharing behavior. Finally, a conceptual model depicting the linkages among the variables under consideration has been presented.
Research limitations/implications
Through the present study, employees’ attitudes toward rewards and leadership’s knowledge of employees’ preferences have been presented as critical factors that can lead to the failure of some rewards to induce knowledge sharing behavior. Further, the conceptual framework that can guide managers and leaders in strategizing on how best to develop and implement correct reward systems has been presented.
Originality/value
The present study is a significant contribution to the literature by focusing on the negative side of rewards toward knowledge sharing behavior with a focus on employees’ attitudes and leadership’s awareness of employees’ preferences regarding rewards.
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Kaushal Kishore Mishra, Pawan Pant, Harvinder Singh and Sunil Kant Mishra
Implementing big data analytics and client customization programs is causing a significant revolution in the insurance sector. This study examines how big data analytics may…
Abstract
Purpose
Implementing big data analytics and client customization programs is causing a significant revolution in the insurance sector. This study examines how big data analytics may revolutionize the insurance industry, emphasizing how consumer customization can improve customer experiences, maximize risk assessment, and spur company expansion.
Design/Methodology
An empirical study with statistical analysis using tools like correlation and regression was carried out to ascertain the relationships between the various sets of variables—personalized customer experiences and customer satisfaction and customer profiling leads to more effective targeting of marketing efforts. We explore essential ideas like client segmentation, profiling, and retention via a thorough analysis of the literature and case studies, showcasing best practices and inspirational tales from top insurers.
Findings
The empirical study found that there is a very high correlation between transparency in data and stakeholders' trust. The study found that insurers may preserve their innovation-driven culture, strengthen customer relationships, and achieve sustainable development in a competitive market by embracing future technological innovations and resolving current challenges.
Practical Implication
Insurance companies may seize new chances for individualized client experiences and long-term success in a market that is becoming increasingly competitive by utilizing cutting-edge technology like artificial intelligence and the Internet of Things. To effectively manage the changing terrain of consumer customization in the digital age, insurance professionals, academics, and legislators will find this study highly insightful.
Originality/Value
The study is an original contribution based on literature and case studies analysis, showcasing best practices and inspirational tales from top insurers.
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Azmee Zaheer, Animesh Singh, Kaushal Kishore Mishra, Reepu and Luan Vardari
The present study delves into the incorporation of the Metaverse in the insurance industry, with an emphasis on augmenting consumer experiences via virtual interaction.
Abstract
Purpose
The present study delves into the incorporation of the Metaverse in the insurance industry, with an emphasis on augmenting consumer experiences via virtual interaction.
Design/Methodology
We explored secondary data sources on metaverse in the insurance industry and went through a thorough analysis of the literature and case studies, showcasing best practices and inspirational tales from top insurers.
Findings
The study found that insurers are ready to capitalize on the convergence of the digital and physical realms embracing the “phygital” environment. By making investments in the Metaverse, insurance companies can reduce new risks, enhance customer satisfaction, and streamline operations. But it also brings up issues with user privacy and security. The efficient application of metaverse solutions may be hampered by problematic areas including malware, cyberbullying, identity theft, cyber hacking, and cyberattacks. User privacy and data security are complicated issues that need the cooperation and accountability of several stakeholders.
Practical Implication
Insurers may revolutionize traditional insurance interactions by utilizing cutting-edge technology like virtual reality (VR) and augmented reality (AR) to create personalized, interactive, and instructive experiences for their consumers. For insurers, the Metaverse has ushered in a new era of digital transformation by giving them a powerful arsenal of technological resources to engage with customers and develop creative business plans.
Originality/Value
The study on the Metaverse in insurance—a virtual customer experience is an original contribution based on literature and case studies on virtual experiences. The ultimate goal of this study is to offer insights into the optimization of virtual client experiences in the digital age by examining the possible advantages, difficulties, and ramifications of applying Metaverse technologies in the insurance industry.
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Goknur Arzu Akyuz and Dursun Balkan
Smart technologies in today's Internet of Things (IoT) era cover a wide spectrum, including smart identification technologies, robotics, virtual reality (VR), augmented reality…
Abstract
Smart technologies in today's Internet of Things (IoT) era cover a wide spectrum, including smart identification technologies, robotics, virtual reality (VR), augmented reality (AR), wearable technologies, cloud, artificial intelligence (AI), machine learning (ML), and blockchain. This study aims at revealing the role and importance of smart technologies in service systems which encompass a wide spectrum of sectors. The scope of this study covers the main applications in transportation and logistics, retail, health care, hospitality, and financial services sectors. A broad overview is provided for the potentials and benefits of smart technology utilization in these service areas, and future research agenda is suggested. Findings reveal that simultaneous use of smart technologies bring tremendous opportunities in terms of efficiencies and automation, reshaping and transforming operations and business processes of the service enterprises. The study highlights that although core proven applications of smart technologies are in manufacturing, generally lagging service sector applications promise radical productivity improvements, performance enhancements, and increased service quality. By radically upgrading the customer satisfaction to entirely new levels and providing unique customer experiences, smart technologies lead to bottom line profitability improvements as well competitiveness.