We introduce and study an extension of the Roberts orthogonality, in the setting of C∗-algebras. More precisely, in a C∗-algebra A, for a, b∈ A and a nonempty subset of A, say B, a is called B-Roberts orthogonal to b, denoted by a⊥RBb, if ‖ a+ bc‖ = ‖ a- bc‖ for all c∈ B. For certain special C∗-algebras, including the C∗-algebra of all 2 × 2 complex matrices, we obtain a nontrivial subset B such that the B-Roberts orthogonality coincides with the usual orthogonality. We also introduce a new concept of smoothness in normed linear spaces in terms of the additivity property of the usual Roberts orthogonality. A complete characterization of the same is obtained in the case of the classical ℓ∞n spaces. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature.

Purpose: This study aims to investigate the insights on how international customer behavior is impacted toward online customer review (OCR) by the mediating effects of social and personal norms in the varied cultural context. The study also investigates how support of peer influence could moderate the effect of OCR. Design/methodology/approach: This study has used socialization theory, congruity theory and theory of planned behavior along with studies of different literature to develop a conceptual model. The conceptual model has been validated using PLS-SEM analysis with survey considering 306 usable respondents. The mediating effects and moderating impacts have been analyzed by mediating analysis process (process tool) and multi group analysis, respectively. Findings: The results of the model, with 62% explanative power, highlight that social norm acts as a strong mediating variable to impact OCR intention while peer influence acts as a vital moderator to impact OCR intention. Research limitations/implications: The theoretical model provides a solid foundation to future researchers for further study in this field. This study also provides the practitioners a unique opportunity towards understanding customer motivation for OCR intention. Accordingly, practitioners could bring some transformational changes in their organizations for getting better reviews from the customers. Originality/value: This study develops a unique theoretical model with high explanative power. Very few studies have ventured in this field. This study has added value to the body of literature on consumer behavior as well as individualism. Furthermore, this study has developed some of the novel relationships between different factors such as individualism, peer influence, international consumers, social norm and so on in the context of OCR which is one of the unique contributions of this study. © 2022, Emerald Publishing Limited.

The primary objective of this study was to develop a novel data-driven machine learning-based multiobjective robust optimisation strategy to improve the overall quality of multivariate manufacturing processes. The new strategy was conceptualised considering a manufacturing environment with unreplicated non-normal data observations and limited opportunity for off-line sequential design of experiments. At a macro level, the new strategy adopts suitable artificial intelligence-based process models and a fine-tuned non-dominated sorting genetic algorithm-II (NSGA-II) to derive robust efficient process setting conditions. These robust solutions are iteratively derived considering process model predictive uncertainties, process setting sensitivities, and variance-covariance structure of uncontrollable multivariate non-normal inputs (or covariates). These solutions are also ranked based on multicriteria decision-making (MCDM) techniques to facilitate implementation. In this study, the quality of the best-ranked solutions was compared (w.r.t. closeness to specified multiple targets and predicted multivariate output variabilities) with those of the solutions obtained from parametric and commercial software-based approaches using three different real-life manufacturing cases. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

We present a complete characterization of the right-symmetric points in the one sum of two Banach spaces. We also obtain some basic properties of the left-symmetric (right-symmetric) points in the p sum, (Formula presented.) ((Formula presented.)), of two Banach spaces. Using these properties we (a) give examples of Banach spaces which do not have any non-zero left-symmetric points and (b) prove a complete characterization of those left-symmetric and right-symmetric points in the p sum, (Formula presented.), of two Banach spaces, whose components satisfy an additional norm assumption. We give examples of Banach spaces where all non-zero left-symmetric or right-symmetric points satisfy this additional norm assumption. We also present an alternative proof of the recently obtained characterization of the left-symmetric and the right-symmetric points in (Formula presented.), (Formula presented.), and (Formula presented.), (Formula presented.). © 2023 Informa UK Limited, trading as Taylor & Francis Group.

For most of the Slacks-Based Measure (SBM)-type models in Data Envelopment Analysis, the variable returns-to-scale (VRS) condition is required to become feasible and translation invariant while applied on negative data. Unlike them, the “Base Point SBM (BP-SBM)” and “Super BP-SBM” models accept negative values under four returns-to-scale (RTS) conditions; however, (i) they are not translation invariant, (ii) they are sensitive to some perturbation terms which help to translate negative data to positive data by avoiding division-by-zero irrationality, and (iii) the Super BP-SBM model suffers from infeasibility issue. To address the above limitations, this study improves the existing BP-SBM and Super BP-SBM models. The Improved BP-SBM (IBP-SBM) and Improved Super BP-SBM (ISBP-SBM) models are translation invariant under four different RTS conditions, and do not involve any perturbation terms. Although the proposed ISBP-SBM model is feasible under the four RTS conditions, it fails to provide Pareto efficient projections. Therefore, we propose a two-stage super-efficiency approach, where the ISBP-SBM model is applied on the first stage to obtain the input savings and output surpluses, which are incorporated in the IBP-SBM model to be applied on the second stage. To validate the proposed models, their results are compared with some state-of-the-art models using a benchmark dataset. Experimental findings indicate that the proposed models have overcome the limitations of the existing models while applied on negative data. Further, three real-life case studies of supplier selection, financial performance evaluation, and airline performance evaluation are used to discuss the applicability of the proposed approach. © 2022 Elsevier Ltd

Purpose: Employee turnover, a reality that Indian retail organizations cannot ignore, is the central theme of this paper. The authors have aimed to empirically establish corporate social responsibility initiatives (CSRI) and transformational leadership (TL) as rather unconventional predictors that can potentially influence retail employees’ intention to stay (ITS) through sequential mediation by employer branding (EB) and organizational identification (OI). Design/methodology/approach: Data collected using a structured questionnaire from three hundred and five frontline employees working with twenty-nine Indian retail outlets in the Delhi-National Capital Region (NCR) region was tested using structural equation modelling. Findings: Findings confirmed the impact of both CSRI and TL on ITS, with sequential mediation by EB and OI. While OI partially mediated the effect of EB on ITS, TL exerted more influence than CSRI in enhancing EB. Originality/value: This study enhances retail literature by empirically testing a unique fusion of organization and individual-level predictors that influence ITS as an individual-level outcome. Having TL and a firm corporate philosophy of CSR spending can enhance a retailer’s image as a preferred employer brand and generate OI to successfully address employee turnover © 2023, Emerald Publishing Limited.

Partner relationship management (PRM) is a set of methods, tools, strategies, and web-based capabilities that a business-to-business (B2B) firm uses to manage its relationships with partners, resellers, and other third parties. Integrating artificial intelligence (AI) into PRM helps automate processes and procedures by eliminating human error and processing data faster and more accurately. Following growing attention from scholars and practitioners to AI-PRM, this study builds on the dynamic capability view (DCV) and absorptive capacity theory to develop a conceptual model to understand the requirements for a B2B firm's adoption of AI-PRM and its impact on business value. Since AI-PRM is still relatively new in scholarly research, there are no specific scales in the existing literature that could be used to capture specific factors and preconditions for its adoption, thus we explore a set of new metrics. We test the conceptual model using structural equation modeling with data from 427 B2B firms. Our results show that firms improve operational performance when an AI-PRM system is reflected in customized partner services and partner engagement, which in turn yields business value. © 2023 The Authors

Purpose: The purpose of this study is to determine the factors that enable citizens to adopt e-government services at different maturity levels: information, two-way communication, transaction and political participation. Design/methodology/approach: The study uses a grounded approach by conducting semi-structured interviews. Findings: The study reveals that the factors influencing the adoption of e-government services vary at different levels. It identifies 27 influencing factors. In total, 13 of these factors influence adoption at the information level; 13 at the two-way communication level; 25 at the transactional level; and 16 at the political participation level. Auxiliary facilities, connectedness, corruption avoidance, transparency and fairness, customer support and forced adoption, not commonly discussed as influencing factors for e-government adoption in the extant literature, have been revealed in this study. Research limitations/implications: This study uses qualitative research and rather than generalization, the focus is explicitly on obtaining an in-depth understanding. Although the sampling used is sufficient for the purpose of this study and allows reasonable conclusions to be drawn; however, it cannot be considered representative of a vast country like India. Academicians and information systems researchers can use these findings for further research. Practical implications: This study advances the understanding of e-government adoption. The findings have potential implications for public administrators and policymakers in successfully designing, developing and implementing e-government services at different maturity levels. Originality/value: Existing e-government adoption theories are of limited scope and do not capture and specify the complete essence of citizens’ adoption characteristics at different levels of e-government services. Hence, a theoretical gap exists, which this study aims to fill. © 2022, Emerald Publishing Limited.

In recent years, machine learning (ML)-based approaches have gained increasing attention in occupational accident research. However, the challenges of data uncertainty, unstructured information handling, lower prediction power of algorithms, and crisp rule generation and its linguistic description remain predominant in ML research, particularly in occupational risk prediction. The present study aims to develop a new methodology to effectively address the aforementioned challenges. The methodology is developed for the tasks of prediction and rule generation. The predictive model, namely rough set (RS) sample-based PSO-ANFIS is developed for prediction, and then, decision rules are generated using the lower approximation of RS and Z-number. The developed methodology contributes by: (i) handling data uncertainty using lower approximation of RS, (ii) unstructured information handling using Latent Dirichlet Allocation (LDA)-based topic modeling, and (iii) prediction using an optimized ML model, (iv) extraction of crisp decision rules using the lower approximation of RS, and (v) determining reliability of the crisp decision rules using Z-number. The efficacy of the proposed method over some state-of-the-art (i.e., ANN, KNN, SVM, C4.5, C5.0, CART, RF, and RS-PSO-ANFIS) is demonstrated using some benchmark datasets acquired from the UCI ML repository, and one real-life occupational safety data acquired from an integrated steel plant in India. A total of 22 implementable crisp safety decision rules have been extracted from the predictive results based on the lower approximation of RS. Experimental results also reveal that the RS sample-based PSO-ANFIS produces minimum mean absolute error (MAE) in risk prediction and is found to be the most robust algorithm. © 2022 Elsevier Ltd

The importance of cutting-edge technologies in ensuring the success of today's supply chains is crucial, as they not only boost corporate performance but also increase competitive advantages in the pursuit of sustainable development goals (SDGs). The adoption of digitization, however, is still facing a number of barriers and challenges, as has been highlighted in trade journals, scientific literature, and by discussions with domain experts. Using a systematic literature review as well as experts’ opinions, we have identified supply chain digitization barriers in light of SDGs. These barriers have been ranked using the Fuzzy Analytic Hierarchy (FAHP) process and broken down into four main criteria and twelve sub-criteria. According to the findings of this study, the most important factor in the adoption of SCD is ‘administrative barriers.’ Note that administrative capability contributes to the achievement of SDG 12 (responsible consumption and production) and SDG 17 (partnership for goals). This research would assist supply chain managers in the decision-making process as it provides structural thinking and framework by establishing the relationship between the barriers of SCD and their effect on the SDGs. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Purpose: The purpose of this study is to determine the antecedents of obesity among the younger generation of Indians (Generation Y) from a psychological and lifestyle consumer perspective. The study also investigates the moderating role of demography on the body mass index (BMI) of Indian youths. Design/methodology/approach: The study initially develops a conceptual model, stemming from an extensive theoretical research, and subsequently validates this using structural equation modeling (SEM) technique with a sample size of 1,242 Indian youths. Findings: The study concludes that consumers' food habits (FH) and physical activity (PA) positively impact consumers' physical health (PH), which influences their BMI levels (BLs). Anxiety (AX), depression (DE), stress (ST), peer pressure (PP) and work pressure (WP) impact individuals' mental health (MH), which also influences their BLs. Finally, there is a significant moderating impact of demographic factors, such as age (AG), gender (GE) and income levels (ILs) on the relationship between individuals' physical and MH and individuals' BLs. Research limitations/implications: This study proposes a new model which highlights the issue of youth consumer obesity from the psychological and lifestyle perspectives. The model is effective as it has a high explanative power of 73%. The study investigates consumer obesity from emerging market like India perspective, but the study does not examine consumer food consumption behavior and obesity from developed market perspective. Practical implications: Youth obesity could be considered a global pandemic, and obesity rates among the Indian youth are also increasing. This study provides valuable inputs and understanding of consumer markets to policy makers, consumer protection institutions, organizations related to the food and beverage industry, healthcare workers and consumers themselves regarding the antecedents of youth obesity (BL) in developing and emerging markets. Originality/value: The study adds value to the body of literature related to consumer obesity, FH, consumer psychology and lifestyle through findings that are new in terms of findings' specificity, contextual focus and explication. Moreover, the study extends the cognitive theory of DE and the theory of planned behavior (TPB). The research effectively offers significant theoretical and practicable market knowledge to both scholars and marketing practitioners, as well as policy makers and institutions dealing with youth obesity, particularly in emerging markets. © 2022, Emerald Publishing Limited.

The fourth industrial revolution, also known as Industry 4.0, covers a broad range of technologies. The applications of Industry 4.0 help in interconnectivity as well as automation process and support seamless operations. Not many studies have taken place to examine how different applications of Industry 4.0 could help in turbulent situations or in crises like COVID-19 pandemic. Thus, there is a research gap. In such background, the aim of this study is to investigate how different applications of Industry 4.0 could help to respond to such turbulent situation and help in business continuity and sustainability of operations. The present study develops a theoretical model combining technology–organization–environment framework and dynamic capability view to examine the role of Industry 4.0 applications for business continuity and sustainability of business operations especially for managing any turbulent situation like COVID-19 pandemic. The theoretical model was validated using partial least square structural equation modeling technique considering 338 respondents from different industries. The study found that there is a greater need to adopt Industry 4.0 applications for managing business operations during any turbulent situation. The study also found that there is a significant moderating impact of corporate governance in adopting Industry 4.0 applications to manage business operations especially during crisis. IEEE

Background: India, the seventh-largest country in the world and the second-most populated faces enormous challenges when it comes to healthcare. The country’s healthcare system was close to collapse due to the detrimental effects of the COVID-19 pandemic. Telehealth, which enables treating patients remotely, played a critical role during these challenging times. This systematic review investigates in detail the role of telehealth during COVID-19 and its application beyond the pandemic. Methods: Database searches on PubMed, Scopus, Science Direct and Web of Science were carried out for studies published on telehealth, and articles were included if they focused on any audio or video telehealth consultation during the pandemic in India. Findings were synthesised into three main themes: applications, benefits and challenges of telehealth services. Methodological quality was assessed using JBI critical appraisal tools. Results: The initial search on databases yielded 1143 articles. Of those, 19 met the eligibility criteria. Findings highlight the effective utilisation of telehealth across multiple medical specialities. Although insufficient technological infrastructure and other barriers due to the virtual consultation challenge the successful implementation of telehealth in India, it has the potential to bridge the rural-urban healthcare divide with cost-effective and easily accessible services. Conclusion: High patient/provider satisfaction underscores the need to integrate telehealth into routine healthcare practices in the country. However, the review urges the government and healthcare practitioners to address the telehealth challenges with prime importance to ensure quality healthcare throughout the nation even after the pandemic. © 2023, The Author(s).

We introduce the notion of approximate smoothness in a normed linear space. We characterize this property and show the connections between smoothness and approximate smoothness for some spaces. As an application, we consider in particular the Birkhoff–James orthogonality and its right-additivity under the assumption of approximate smoothness. © 2023, The Author(s).

Despite the exponential growth of artificial intelligence (AI) research in operations, supply chain, and productions management literature, empirical insights on how organisational behavioural mechanisms at the human–technology interface will facilitate AI adoption in small- and medium-sized enterprises (SMEs), and subsequent impact of the adoption on sustainable practices and supply chain resilience (SCR) is under-researched. To bridge these gaps, we integrate resource orchestration and knowledge-based view theoretical perspectives to develop a novel structural model examining antecedents to SCR and AI adoption, considering AI adoption as a pivot for facilitating SCR. The structural equation modelling technique was employed on the data collected from 280 Vietnamese manufacturing SMEs’ operations managers. Our results demonstrate that leadership will drive AI adoption by creating a data-driven, digital and conducive culture, and strengthening employee skills and competencies. Furthermore, AI adoption positively influences CE practices, SC agility and risk management, which will help to achieve SCR. For managers, the importance of internal organisational employee-centric mechanisms to create value from AI adoption without impeding business value is highlighted. We reveal the enablers that will help in transforming SMEs to become resilient by deriving appropriate responses to unprecedented disruptions through data-driven decision-making leveraging AI adoption. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Purpose: This study assesses the capability of artificial intelligence integrated customer relationship management (AI-CRM) technology for sustaining family businesses in times of crisis, such as the COVID-19 pandemic. The study also investigates the moderating role of strategic intent in sustaining family businesses in times of crisis. Design/methodology/approach: The authors used dynamic capability view theory and related literature on family business and technology adoption to develop a conceptual model. This model has been validated using the structural equation modeling technique considering 332 usable responses from people of India involved in family businesses and technology adoption. The study also uses multigroup analysis to examine the moderating role of strategic intent. Findings: The study finds that adoption of AI-CRM technology significantly and positively impacts dynamic capabilities of the family businesses, such as sensing, seizing and transforming capabilities, which in turn positively and significantly influences their sustainability during crises. The study also highlights the significant moderating impact of strategic intent for sustaining family business firms in uncertain times. Practical implications: This study has highlighted the importance for family businesses to adopt AI-CRM technology and its influence on their dynamic capabilities. The study also provides important inputs to the management of family businesses regarding adoption of new technologies and their significance during crises. The study also documents that strategic intent could help family businesses to survive during such times. The study is conducted in India and thus cannot be generalized. Originality/value: This study table is unique in that it investigates the influence of AI-CRM technology and the moderating role of strategic intent on family business sustainability in times of crisis. Moreover, the proposed theoretical model is a unique model with explanative power of 71%. © 2022, Ranjan Chaudhuri, Sheshadri Chatterjee, Sascha Kraus and Demetris Vrontis.

Purpose: The study explores extant knowledge on the nature of the relationship between internal and external corporate governance mechanisms, particularly board characteristics and audit quality, respectively, while also investigating how the relationship varies across geographies. Design/methodology/approach: The extant knowledge is synthesized using a meta-analysis, which is conducted using a sample of 56 empirical studies from publications of varying grades. The studies span over 25 years (1996–2021) and cover 147 empirical samples (343,787 firm-year observations) across more than 20 countries. The dependent variable is audit fees, and the independent variable captures 12 different measures of board characteristics. Findings: Overall, the results reveal a positive association between board characteristics and audit fees, indicating complementarity between governance mechanisms. Effect size analysis shows board characteristics, like size and independence, are positively associated with audit fees. However, heterogeneity is noted for some characteristics, and further analysis by geography (developed vs emerging countries) explains the heterogeneity. Practical implications: This study helps multiple stakeholders like firms, shareholders, boards, regulators and policymakers in designing and strengthening governance frameworks. Social implications: Both governance and auditing literature benefit from identifying specific board characteristics that drive audit quality consistently across different institutional settings and samples. Heterogeneity analysis helps improve the understanding of contradictions documented in prior literature. Originality/value: This meta-analysis is the first to explore the interplay between internal and external corporate governance mechanisms, with a focus on board characteristics and audit quality. The study provides valuable insights on how different governance mechanisms influence each other while highlighting, for the first time, how the interaction between governance mechanisms varies by a country's level of development. © 2022, Emerald Publishing Limited.

Purpose: This study aims to investigate the impact of business strategy on the classification shifting practices of Indian firms. Design/methodology/approach: The study considered cost leadership and differentiation strategy. Two forms of classification shifting, namely, expense misclassification and revenue misclassification have been examined in this study. Panel data regression models are used to analyze the data for this study. Findings: The results show that managers of cost leadership strategy firms are more likely to be engaged in expense misclassification, whereas firms following differentiation strategy are likely to be engaged in revenue misclassification. Subsequent tests of this study suggest that firms following a hybrid strategy (mix of cost leadership and differentiation) prefer revenue misclassification over expense misclassification for reporting inflated operating performance. These results imply that firms prefer the shifting tool based on the ease and need of each shifting strategy. These results are consistent with several robustness measures. Practical implications: The results suggest that investors should understand business strategy before developing insights about the accounting quality of firms. Investors should conduct a comprehensive review of income statement items before using items for portfolio evaluation. Originality/value: To the best of the authors’ knowledge, this is the first study to examine the association between business strategy and classification shifting. © 2022, Emerald Publishing Limited.

In 2020, the COVD-19 pandemic emerged as the most severe crisis of the century. Several vaccine manufacturing firms have taken the necessary initiatives to combat this problem. However, profitability issues can bring down these firms’ vaccine manufacturing efforts, thus leading to lower vaccination coverage. Motivated by this issue, we depict a private COVID-19 vaccine supply chain with a supply chain framework comprising of one vaccine manufacturer and multiple private hospitals under demand uncertainty. We incorporate a Stackelberg game-theoretic approach to demonstrate the collaboration between the vaccine manufacturer and the private hospital using wholesale price, two-part tariff and revenue sharing contracts. We determine the optimal number of vaccines and coordination criteria for each contract. Using a real-life approximation of Indian data, we conduct several numerical studies and facilitate the visual depiction of all the theoretical insights obtained from the model. We also discuss the managerial implications of this study. As per our analysis, when private hospitals procure a higher number of vaccines from the vaccine manufacturer, the two-part tariff contract-based collaboration mechanism yields a win-win situation for both the private hospitals and the vaccine manufacturer and is better than the wholesale price contract. © 2023 IMI.

The box girder bridges constructed by prestressing the girders have been widely adopted to avoid the high dead load of the girders and for better performance during the service life. The loss of prestress may affect its performance and service life. Hence proper monitoring of such bridges with minimum efforts are useful for maintenance of the bridge. The correct estimation of the Impact Factor due to vehicle bridge interaction force is essential for designing such bridges. The presence of prestress in bridges influences the responses of bridges and vehicles moving over them. This paper provides the results of the finite element analysis of dynamic interaction between a vehicle and a simply supported pre-stressed box beam bridge. The interaction was conducted using ABAQUS software. Numerical simulations were conducted to show the effects of vehicle speed and road surface roughness profile on the mid-span displacement responses of the internal and external pre-stress box beams of the bridges. Thereafter, the influence of important parameters, namely, pre-stress force, road surface condition, and vehicle speed on the impact factor was investigated. Furthermore, it has been found that the maximum vertical vehicle acceleration response is sensitive to the prestress level and could be used to detect the percentage of prestress. © 2023, The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature.