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This chapter introduces Learning Health Systems (LHS) and the impact of data science on such systems. It also examines the necessary properties of data used in LHS and identifies patients who may benefit from a transition to palliative care. Finally, it examines the way LHS can be used to identify racial and social disparities in access to palliative care.

This research proposes a framework to conceptualise the potential realm of data regarding shipping connectivity for application of data analytics which can be used to generate deeper insights with respect to the state of such linkages and potential areas for practical application.

The study method involved comprehensive presentation of different perspectives of assessing shipping connectivity and levels of data contained within container shipping services and proposed potential application to analyse profitability, performance, competitiveness, risk and environmental impact.

Advances in capabilities to handle large volumes of data offer scope for an integrated approach which utilises all available data from various stakeholders in analyses of liner shipping connectivity. Research shows how different types of data contained in container shipping services are related and can be organised for application of data analytics.

Research implications are offered to shipping lines, port managers and operators and policymakers.

This research presented a conceptual framework that captures the range of data involved in container shipping services and how data analytics can be practically applied in an integrated manner.

This paper is the first in literature to discuss in detail the different levels of data that reside within shipping services that constitute liner shipping connectivity for application of data analytics.

The learning objectives have been prepared following Bloom’s taxonomy (Bloom et al., 1956). After completing the case study, the students will be able to identify and recall the prerequisites necessary for establishing a pathology laboratory. (knowledge); analyze the micro- and macroenvironmental factors considered by Mr Sabihul Haque in the development of the strategic plan for Healthcare Laboratories (HCL) (knowledge and application); explain the key components of the Porter’s value chain and their significance in the operation of HCL (comprehension and evaluation); use the TOWS analysis to map the internal strengths, weaknesses, opportunities and threats of HCL (application and synthesis); and analyze the challenges faced by protagonist in managing HCL and generate suggestions for addressing the challenges (analysis and synthesis).

HCL, an enterprise established in 2018 in Sahdeo Khap, Gaya, Bihar, India, aims to provide high-quality pathological diagnostic services in semi-urban and rural areas. This health-care initiative is pioneering, offering pathology services to make high-quality, low-cost diagnostic services accessible in rural India. In rural settings, numerous health-care hurdles make it challenging for individuals to access the care they need. Since its inception, HCL has expanded its reach to connect more areas, facilitating diagnostic services for people in remote regions. The establishment of laboratories in semi-urban areas aims to reduce patient travel time, costs and health risks by bringing services directly to their doorstep. Haque, the chief executive officer of the lab, grappled with multiple challenges, including selecting an appropriate location for the lab, recruiting and retaining skilled workforce, managing logistics supply, collaborating with local health-care providers, dispelling the stigma among the population that superior services are only available in cities and enhancing health literacy in rural communities. Following numerous meetings with Ms Ummati Naiyyer, head of operations, they worked collaboratively to address these challenges, developing a blueprint and future plan to operate services in rural areas. This case study provides insights into the obstacles faced by HCL striving for success in rural areas. It elucidates the beneficial application of the Porter’s value chain, along with an analysis of macro- and microenvironmental factors. Unique challenges such as societal stigma and mistrust are specifically emphasized. Students engaging with this case study will enhance their problem-solving skills through brainstorming and providing recommendations, contributing to potential solutions for HCL’s difficulties.

The teaching notes for the HCL case is designed to enhance the learning experience of undergraduate and graduate students within the context of the course. This case study serves as a valuable teaching tool, allowing students to apply theoretical knowledge to real-world scenarios in the health-care industry. The notes provide a framework for instructors to facilitate discussions, encourage critical thinking and promote a deeper understanding of key concepts related to establishing diagnostic laboratories in rural areas.

Teaching notes are available for educators only.

CSS3: Entrepreneurship.

The purpose of this study is to investigate the relationship between the level of sustainability reporting [environmental, social and governance (ESG)] and sectorial energy performance across both developed and emerging economies.

Using data culled from 3,311 observations from 50 different countries over a ten-year period (2008–2017), an ESG-score-derived independent variable is regressed against dependent performance indicator variables (operation ratio, return on equity and Tobin’s Q). Two types of control variables complete the regression analysis in this study: firm-specific and macroeconomic.

The findings of this study elicited from the empirical results demonstrate that there is a significant relationship between ESG and operational performance (operation ratio). However, there is no significant relationship between ESG and financial performance (return on equity) and market performance (Tobin’s Q). However, the relationship between ESG and operation ratio is stronger in emerging than in developed economies.

The model in this study presents a valuable analytical framework for exploring sustainability reporting as a driver of performance across energy sectors in both developed and emerging economies. In addition, this study highlights energy-sectorial managerial implications contrasting developed, as juxtaposed with, emerging economies.

The use of over-the-top (OTT) platforms grew substantially after the declaration of the COVID-19 pandemic in 2020. With the pandemic receding, there is a concern that users may not continue with their subscriptions. To counter this, OTT service providers must strategize proactively to retain and acquire new users once the pandemic abates. Positing that understanding the consumption values that users ascribe to OTT platform usage can provide useful customer retention insights, the purpose of this paper is to use the theory of consumption value (TCV) to study the values that users derived from their use of OTT following the onset of the pandemic.

The mixed-method approach is used to collect qualitative and quantitative data. Analysis of qualitative responses collected through interviews of 12 current OTT platform users helped identify two categories of OTT platform-specific values: attribute-level and benefit-based. Next, the study examined the association of values thus identified with one another, as well as with continued intentions to use OTT platforms, by analyzing data collected from 371 existing users.

The findings indicated that functional value quality and social value, representing the attribute-level values, were positively associated with two benefit-based values – functional value price and emotional value (EMV). Next, EMV was not only associated with intentions but also partially mediated the association of attribute-level values with intentions. Premium subscription purchased and increased viewing time were confirmed to have moderating effects on the association between attribute-level and benefit-based values.

The study is amongst the foremost research initiatives to examine consumption values derived from OTT platform usage after the onset of the pandemic. Its novelty also comes from its identifying OTT platform-specific consumption values for the first time and adding a new dimension to the TCV by examining the interplay of these values in the OTT platform context.

This paper proposes a progressive, multi-level framework for diagnosing maintenance performance: rapid performance health checks of key performance for different equipment groups and end-to-end process diagnostics to further locate potential performance issues. A question-based performance evaluation approach is introduced to support the selection and derivation of case-specific indicators based on diagnostic aspects.

The case research method is used to develop the proposed framework. The generic parts of the framework are built on existing maintenance performance measurement theories through a literature review. In the case study, empirical maintenance data of 196 emergency shutdown valves (ESDVs) are collected over a two-year period to support the development and validation of the proposed approach.

To improve processes, companies need a separate performance measurement structure. This paper suggests a hierarchical model in four layers (objective, domain, aspect and performance measurement) to facilitate the selection and derivation of indicators, which could potentially reduce management complexity and help prioritize continuous performance improvement. Examples of new indicators are derived from a case study that includes 196 ESDVs at an offshore oil and gas production plant.

Methodological approaches to deriving various performance indicators have rarely been addressed in the maintenance field. The proposed diagnostic framework provides a structured way to identify and locate process performance issues by creating indicators that can bridge generic evaluation aspects and maintenance data. The framework is highly adaptive as data availability functions are used as inputs to generate indicators instead of passively filtering out non-applicable existing indicators.

In today's business environment, big data is viewed as the “new oil,” which is rapidly changing the traditional business models and mode of operations. According to commentaries and scholarly work, big data and its applications have penetrated deeply into the very core of the products, services, and functional areas of many firms. What remains unclear is how using this “new oil” (big data) and “new technique” (data diagnosticity) can result in new “products and processes.” The purpose of this paper is to examine the effects of big data usage on product and process innovation, and the moderating role of data diagnosticity on said associations.

Data were obtained from Turkish firms that utilize big data in their daily operations and analyzed with the partial least squares' structural equation modeling technique.

The findings revealed that big data usage is a predictor for higher product and process innovation. Diagnostic capabilities of the firms did not amplify the link between big data usage and product innovation, big data usage and process innovation.

This paper is among the first study to examine the association of big data usage, data diagnostic capabilities, product, and process innovations in the Turkish context. Implications for theory and practice are discussed.

Recovery Colleges were developed to support the recovery of people with mental health difficulties through courses co-produced by professionals and people with lived experience. This study aims to examine the use of Recovery Colleges to support people with dementia.

A survey was circulated to UK Recovery College and memory service staff, exploring provision, delivery and attendance of dementia courses. Open responses provided insight into participant views about recovery in post-diagnostic support and the practicalities of running dementia courses.

A total of 51 Recovery College staff and 210 memory service staff completed the survey. Twelve Recovery College dementia courses were identified across the UK. Three categories emerged from the qualitative data: post-diagnostic support, recovery in the context of dementia, challenges and areas of innovation.

This study highlights the benefits and practicalities of running Recovery College courses with people with dementia. Peer-to-peer learning was seen as valuable in post-diagnostic support but opinions were divided about the term recovery in dementia.

This study aims to bring awareness to the developing of fault detection systems using the data collected from sensor devices/physical devices of various systems for predictive maintenance. Opportunities and challenges in developing anomaly detection algorithms for predictive maintenance and unexplored areas in this context are also discussed.

For conducting a systematic review on the state-of-the-art algorithms in fault detection for predictive maintenance, review papers from the years 2017–2021 available in the Scopus database were selected. A total of 93 papers were chosen. They are classified under electrical and electronics, civil and constructions, automobile, production and mechanical. In addition to this, the paper provides a detailed discussion of various fault-detection algorithms that can be categorised under supervised, semi-supervised, unsupervised learning and traditional statistical method along with an analysis of various forms of anomalies prevalent across different sectors of industry.

Based on the literature reviewed, seven propositions with a focus on the following areas are presented: need for a uniform framework while scaling the number of sensors; the need for identification of erroneous parameters; why there is a need for new algorithms based on unsupervised and semi-supervised learning; the importance of ensemble learning and data fusion algorithms; the necessity of automatic fault diagnostic systems; concerns about multiple fault detection; and cost-effective fault detection. These propositions shed light on the unsolved issues of predictive maintenance using fault detection algorithms. A novel architecture based on the methodologies and propositions gives more clarity for the reader to further explore in this area.

Papers for this study were selected from the Scopus database for predictive maintenance in the field of fault detection. Review papers published in this area deal only with methods used to detect anomalies, whereas this paper attempts to establish a link between different industrial domains and the methods used in each industry that uses fault detection for predictive maintenance.

This paper aims to propose an interpretive framework to understand how machine learning (ML) affects the way companies interact with their ecosystem and how the introduction of digital technologies affects the value co-creation (VCC) process.

This study bases on configuration theory, which entails two main methodological phases. In the first phase the authors define the theoretically-derived interpretive framework through a literature review. In the second phase the authors adopt a case study methodology to inductively analyze the theoretically-derived domains and their relationships within a configuration.

ML enables multi-directional knowledge flows among value co-creators and expands the scope of VCC beyond the boundaries of the firm-client relationship. However, it determines a substantive imbalance in knowledge management power among the actors involved in VCC. ML positively impacts value co-creators’ performance but also requires significant organizational changes. To benefit from VCC via ML, value co-creators must be aligned in terms of digital maturity.

The paper answers the call for more theoretical and empirical research on the impact of the introduction of Industry 4.0 technology in companies and their ecosystem. It intends to improve the understanding of how ML technology affects the determinants and the process of VCC by providing both a static and dynamic analysis of the topic.