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1 – 10 of over 2000Rajat Kumar Behera, Pradip Kumar Bala, Prabin Kumar Panigrahi and Shilpee A. Dasgupta
Despite technological advancements to enhance patient health, the risks of not discovering the correct interactions and trends in digital health are high. Hence, a careful policy…
Abstract
Purpose
Despite technological advancements to enhance patient health, the risks of not discovering the correct interactions and trends in digital health are high. Hence, a careful policy is required for health coverage tailored to needs and capacity. Therefore, this study aims to explore the adoption of a cognitive computing decision support system (CCDSS) in the assessment of health-care policymaking and validates it by extending the unified theory of acceptance and use of technology model.
Design/methodology/approach
A survey was conducted to collect data from different stakeholders, referred to as the 4Ps, namely, patients, providers, payors and policymakers. Structural equation modelling and one-way ANOVA were used to analyse the data.
Findings
The result reveals that the behavioural insight of policymakers towards the assessment of health-care policymaking is based on automatic and reflective systems. Investments in CCDSS for policymaking assessment have the potential to produce rational outcomes. CCDSS, built with quality procedures, can validate whether breastfeeding-supporting policies are mother-friendly.
Research limitations/implications
Health-care policies are used by lawmakers to safeguard and improve public health, but it has always been a challenge. With the adoption of CCDSS, the overall goal of health-care policymaking can achieve better quality standards and improve the design of policymaking.
Originality/value
This study drew attention to how CCDSS as a technology enabler can drive health-care policymaking assessment for each stage and how the technology enabler can help the 4Ps of health-care gain insight into the benefits and potential value of CCDSS by demonstrating the breastfeeding supporting policy.
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Saul J. Berman, Steven Davidson, Kazuaki Ikeda, Peter J. Korsten and Anthony Marshall
This report by the IBM Institute for Business Value, part of long-term study of C-suite executives, probes the perspectives of the 818 CEOs to find out what they think the future…
Abstract
Purpose
This report by the IBM Institute for Business Value, part of long-term study of C-suite executives, probes the perspectives of the 818 CEOs to find out what they think the future will bring and how they’re positioning their organizations to prosper in the “age of disruption.”
Design/methodology/approach
The report also focuses specifically on what a subset of the 818 – CEOs of the most successful enterprises surveyed – do differently.
Findings
IBM analysis identified a small group of organizations that have both a strong reputation as leading innovators and an outstanding financial track record. Lead by so-called Torchbearer CEOs the firms are exploring opportunities to leverage emerging technologies and ecosystems to pursue entirely new revenue streams and models.
Practical implications
CEOs recognize the confluence of technologies magnifies their impact across markets and economies. Emerging technologies intersect and combine in new and different ways, enabling enterprises to redefine how they engage with their customers and partners.
Originality/value
Two-thirds of executives surveyed plan to reassess their strategic direction and explore the potential for novel, non-traditional forms of growth. They’re actively pursuing opportunities to play a new or different role in the ecosystems they inhabit.
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This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.
Abstract
Purpose
This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.
Design/methodology/approach
This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.
Findings
The fast pace at which technologies are advancing mean that CEOs are in a position that requires bold strategies to flourish in the modern marketplace. How to embrace technology and best utilize what opportunities it presents is a key concern. There are many different approaches, but those CEOs seen as market followers need to carefully watch how the torchbearer CEOs choose to adapt. More importantly, those wishing to become torchbearers rather than followers need to reconsider what strategies they implement for what gains.
Practical implications
The paper provides strategic insights and practical thinking that have influenced some of the world’s leading organizations.
Originality/value
The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.
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Marcello Mariani and Jochen Wirtz
This work consists of a critical reflection on the extent to which hospitality and tourism management scholars have accurately used the term “analytics” and its five types (i.e…
Abstract
Purpose
This work consists of a critical reflection on the extent to which hospitality and tourism management scholars have accurately used the term “analytics” and its five types (i.e. descriptive, exploratory, predictive, prescriptive and cognitive analytics) in their research. Only cognitive analytics, the latest and most advanced type, is based on artificial intelligence (AI) and requires machine learning (ML). As cognitive analytics constitutes the cutting edge in industry application, this study aims to examine in depth the extent cognitive analytics has been covered in the literature.
Design/methodology/approach
This study is based on a systematic literature review (SLR) of the hospitality and tourism literature on the topic of “analytics”. The SLR findings were complemented by the results of an additional search query based on “machine learning” and “deep learning” that was used as a robustness check. Moreover, the SLR findings were triangulated with recent literature reviews on related topics (e.g. big data and AI) to generate additional insights.
Findings
The findings of this study show that: there is a growing and accelerating body of research on analytics; the literature lacks a consistent use of terminology and definitions related to analytics. Specifically, publications rarely use scientific definitions of analytics and their different types; although AI and ML are key enabling technologies for cognitive analytics, hospitality and tourism management research did not explicitly link these terms to analytics and did not distinguish cognitive analytics from other forms of analytics that do not rely on ML. In fact, the term “cognitive analytics” is apparently missing in the hospitality and tourism management literature.
Research limitations/implications
This study generates a set of eight theoretical and three practical implications and advance theoretical and methodological recommendations for further research.
Originality/value
To the best of the authors’ knowledge, this is the first study that explicitly and critically examines the use of analytics in general, and cognitive analytics in particular, in the hospitality and tourism management literature.
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Abstract
Purpose
Many higher education institutions are investigating the possibility of developing predictive student success models that use different sources of data available to identify students that might be at risk of failing a course or program. The purpose of this paper is to review the methodological components related to the predictive models that have been developed or currently implemented in learning analytics applications in higher education.
Design/methodology/approach
Literature review was completed in three stages. First, the authors conducted searches and collected related full-text documents using various search terms and keywords. Second, they developed inclusion and exclusion criteria to identify the most relevant citations for the purpose of the current review. Third, they reviewed each document from the final compiled bibliography and focused on identifying information that was needed to answer the research questions
Findings
In this review, the authors identify methodological strengths and weaknesses of current predictive learning analytics applications and provide the most up-to-date recommendations on predictive model development, use and evaluation. The review results can inform important future areas of research that could strengthen the development of predictive learning analytics for the purpose of generating valuable feedback to students to help them succeed in higher education.
Originality/value
This review provides an overview of the methodological considerations for researchers and practitioners who are planning to develop or currently in the process of developing predictive student success models in the context of higher education.
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Robert Handfield, Seongkyoon Jeong and Thomas Choi
The purpose of this paper is to elucidate the emerging landscape of procurement analytics. This paper focuses on the following questions: what are the current and future state of…
Abstract
Purpose
The purpose of this paper is to elucidate the emerging landscape of procurement analytics. This paper focuses on the following questions: what are the current and future state of procurement analytics?; what changes in the procurement process will be required to enable integration of analytical solutions?; and what future areas of research arise when considering the future state of procurement analytics?
Design/methodology/approach
This paper employs a qualitative approach that relies on three sources of information: executive interviews, a review of current and emerging technology platforms and a small survey of subject matter experts in the field.
Findings
The procurement analytics landscape developed in this research suggests that the authors will continue to see major shifts in the sourcing and supply chain technology environment in the next five years. However, there currently exists a low usage of advanced procurement analytics, and data integrity and quality issues are preventing significant advances in analytics. This study identifies the need for organizations to establish a coherent approach to collection and storage of trusted organizational data that build on internal sources of spend analysis and contract databases. In addition, current ad hoc approaches to capturing unstructured data must be replaced by a systematic data governance strategy. An important element for organizations in this evolution is managing change and the need to nourish an analytic culture.
Originality/value
While the majority of forward-looking research and reports merely project broad technological impact of cognitive analytics and big data, much of it does not provide specific insights into functional impacts such as the impact on procurement. The analysis of this study provides us with a clear view of the potential for business analytics and cognitive analytics to be employed in procurement processes, and contributes to development of related research topics for future study. In addition, this study suggests detailed implementation strategies of emerging procurement technologies, contributing to the existing body of the literature and industry reports.
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Valentina Iscaro, Laura Castaldi, Paolo Maresca and Clelia Mazzoni
This paper aims to investigate the role of predictive models in the learning and decision-making processes of strategic management. The rapid advancement of digitalisation has…
Abstract
Purpose
This paper aims to investigate the role of predictive models in the learning and decision-making processes of strategic management. The rapid advancement of digitalisation has contributed to increasing the complexity of the worldwide economy and led to various new competitive dynamics.
Design/methodology/approach
To achieve this purpose, a literature review has been carried out and a predictive model based on Watson, an IBM supercomputer, is presented as a qualitative process model.
Findings
Specific insights derived from a review of the literature highlight organisations' need to modify their decision- and strategy-making processes, which are increasing in speed and frequency, thus also leading to the formulation of emergent and trigger event strategies based on the identification of conditions that require the revision of all or part of the firm's strategy. Predictive models, acting as filters, transform data into informative knowledge that decision-makers can interpret based on individual domain knowledge.
Originality/value
From a theoretical point of view, this paper contributes to the field of digital transformation by proposing the economics of complexity as a paradigm through which to observe and study the issue of predictive models in strategic management. Additionally, the authors analyse the phenomenon from a cognitive perspective, defining the new learning dynamics of digital transformation and the social learning cycle triggered by big data and predictive models. From a managerial and policy-making point of view, this suggests the need to re-shape traditional education contents and dynamics and foster skills that are multi-disciplinary, multi-domain, multi-empathic, multi-interaction and multi-communication between people and things.
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Siddharth Gaurav Majhi, Arindam Mukherjee and Ambuj Anand
Novel and emerging technologies such as cognitive analytics attract a lot of hype among academic researchers and practitioners. However, returns on investments in these…
Abstract
Purpose
Novel and emerging technologies such as cognitive analytics attract a lot of hype among academic researchers and practitioners. However, returns on investments in these technologies are often poor. So, identifying mechanisms through which cognitive analytics can add value to firms is a critical research gap. The purpose of this paper is to theorize how cognitive analytics technologies can enable the dynamic capabilities of sensing, seizing and reconfiguring for an organization.
Design/methodology/approach
This conceptual paper draws on the extant academic literature on cognitive analytics and related technologies, the business value of analytics and artificial intelligence and the dynamic capabilities perspective, to establish the role of cognitive analytics technologies in enabling the sensing, seizing and reconfiguring capabilities of an organization.
Findings
Through arguments grounded in existing conceptual and empirical academic literature, this paper develops propositions and a theoretical framework linking cognitive analytics technologies with organizations’ dynamic capabilities (sensing, seizing and reconfiguring).
Research limitations/implications
This paper has critical implications for both academic research and managerial practice. First, the authors develop a framework using the dynamic capabilities theoretical perspective to establish a novel pathway for the business value of cognitive analytics technology. Second, cognitive analytics is proposed as a novel antecedent of the dynamic organizational capabilities of sensing, seizing and reconfiguring.
Originality/value
To the best of the authors’ knowledge, this is the first paper to theorize how cognitive analytics technologies can enable dynamic organizational capabilities, and thus add business value to an organization.
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Despite the escalating significance and intricate nature of supply chains, there has been limited scholarly attention devoted to exploring the cognitive processes that underlie…
Abstract
Purpose
Despite the escalating significance and intricate nature of supply chains, there has been limited scholarly attention devoted to exploring the cognitive processes that underlie supply chain management. Drawing on cognitive-behavioral theory, the authors propose a moderated-mediation model to investigate how paradoxical leadership impacts manufacturing supply chain resilience.
Design/methodology/approach
By conducting a two-wave study encompassing 164 supply chain managers from Chinese manufacturing firms, the authors employ partial least squares structural equation modeling (PLS-SEM) to empirically examine and validate the proposed hypotheses.
Findings
The findings indicate that managers' paradoxical cognition significantly affects supply chain resilience, with supply chain ambidexterity acting as a mediating mechanism. Surprisingly, the study findings suggest that big data analytics negatively moderate the effect of paradoxical cognition on supply chain ambidexterity and supply chain resilience, while positively moderating the effect of supply chain ambidexterity on supply chain resilience.
Research limitations/implications
These findings shed light on the importance of considering cognitive factors and the potential role of big data analytics in enhancing manufacturing supply chain resilience, which enriches the study of behavioral operations.
Practical implications
The results offer managerial guidance for leaders to use paradoxical cognition frames and big data analytics properly, offering theoretical insight for future research in manufacturing supply chain resilience.
Originality/value
This is the first empirical research examining the impact of paradoxical leadership on supply chain resilience by considering the role of big data analytics and supply chain ambidexterity.
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