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This study examines how intellectual capital (IC) drives firm performance via the lens of dynamic capabilities (DCs). Drawing on resource-based view (RBV) and dynamic capability view (DCV), the authors elaborate the mediating role of learning, integration and reconfiguration DC in the Indian banking context.

A sample of 358 top- and middle-level managers from the Indian banking sector was administered with structured questionnaires for data collection. Structural equation modeling (SEM) and Sobel test were used to analyze the data and test the hypothesized mediating effect.

The findings reveal that learning and integration DCs are key mediators in IC and banks' performance relationships in an emerging economy context. In contrast, the analysis revealed partial mediating role of reconfiguration DC. Furthermore, the learning DC has been identified as the primary mediating mechanism for transforming bank's IC into performance benefits.

This study provides an important implication for the IC and DC link by empirically developing and validating a model in the Indian banking sector and making a several contributions to the related literature. This sector needs to incorporate and strengthen their IC and DCs to attain enhanced performance in today's dynamic environment. Bank managers can use these findings to bring their knowledge-related activities to channelize specific DCs to transform banks' IC when seeking to improve overall performance. Theoretically, this study extends previous research by outlining a set of organizational elements that tend to influence firm performances with the help of IC, learning, integration and reconfigurations DCs.

Although several studies have investigated the links between IC, DC and firm performance, studies on emerging economies are scarce. This study is one of the most in-depth investigations of the relationship between IC, learning, integration and reconfiguration DCs and firm performance in an integrated framework, with a particular focus on the banking sector of an emerging economy.

In this chapter, we reflect on cognitive aids and their role in strategy work. Strategy research and practice abound with frameworks, models, tools, and processes meant to describe and guide the strategy work of managers. These are all examples of cognitive aids. These aids guide and support managerial cognition, the way managers make sense of the world. What we collectively call the cognitive aids of strategy have a profound impact on the way managers learn about, conceptualize, share, and enact strategy work and strategies in their organizations. Despite the importance of their cognitive role, many cognitive aids in strategy are presented without reference to the underlying cognitive theory that explains why and how the aid might be useful. Tools are presented as useful for management thinking, but without any substantive reflection or exploration of the cognitive reasons. In this chapter, we provide a definition of cognitive aids in strategy and begin exploring the landscape of cognitive theories that can explain why something might be a cognitive aid. We then briefly outline the contributions to the edited volume “Cognitive Aids in Strategy,” and end with an invitation to expand your exploration beyond.

This paper aims to describe the development, use and evaluation of a web-based mental capacity assessment tool (MCA WAT) within the Learning Disabilities division of a Mental Health and Learning Disabilities Trust in England.

The MCA WAT was developed to ensure there was contemporaneous recording of each capacity assessment; help staff improve their knowledge, confidence and satisfaction in completing assessments of capacity; and analyse the number and type of capacity assessments completed. Three questionnaires were developed: a 12-item multiple-choice knowledge questionnaire, a nine-item confidence questionnaire and a 10-item satisfaction questionnaire, which reflect the five core principles of the MCA (2005). Eight-five health and social care staff who were trained to use the MCA WAT and had used it at least once had their scores from pre- and post-use of the questionnaires analysed using Wilcoxon signed-ranks tests. Data on the use of the MCA WAT was analysed.

Results showed a significant increase in knowledge, confidence and satisfaction from pre- to post-questionnaires. Concerns remain regarding staff understanding of who is the decision maker and the use of communication aids to enhance capacity. These issues are being addressed through training and updating the information boxes in the MCA WAT. The MCA WAT has been used to complete 3,645 capacity assessments over a five-year time scale, covering a wide range of decisions.

This project demonstrates the effectiveness of a contemporaneous method of conducting and recording MCA assessments.

This research navigates the confluence of data analytics, machine learning, and artificial intelligence to revolutionize the management of urban services in smart cities. The study thoroughly investigated with advanced tools to scrutinize key performance indicators integral to the functioning of smart cities, thereby enhancing leadership and decision-making strategies. Our work involves the implementation of various machine learning models such as Logistic Regression, Support Vector Machine, Decision Tree, Naive Bayes, and Artificial Neural Networks (ANN), to the data. Notably, the Support Vector Machine and Bernoulli Naive Bayes models exhibit robust performance with an accuracy rate of 70% precision score. In particular, the study underscores the employment of an ANN model on our existing dataset, optimized using the Adam optimizer. Although the model yields an overall accuracy of 61% and a precision score of 58%, implying correct predictions for the positive class 58% of the time, a comprehensive performance assessment using the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) metrics was necessary. This evaluation results in a score of 0.475 at a threshold of 0.5, indicating that there's room for model enhancement. These models and their performance metrics serve as a key cog in our data analytics pipeline, providing decision-makers and city leaders with actionable insights that can steer urban service management decisions. Through real-time data availability and intuitive visualization dashboards, these leaders can promptly comprehend the current state of their services, pinpoint areas requiring improvement, and make informed decisions to bolster these services. This research illuminates the potential for data analytics, machine learning, and AI to significantly upgrade urban service management in smart cities, fostering sustainable and livable communities. Moreover, our findings contribute valuable knowledge to other cities aiming to adopt similar strategies, thus aiding the continued development of smart cities globally.

Introduction: Numerous decision-making situations are faced in education where Artificial Intelligence may be prevalent as a decision-making support tool to capture streams of learners’ behaviours.

Purpose: The purpose of the present study is to understand the role of AI in student performance assessment and explore the future role of AI in educational performance assessment.

Scope: The study tries to understand the adaptability of AI in the education sector for supporting the educator in automating assessment. It supports the educator to concentrate on core teaching-learning activities.

Objectives: To understand the AI adaption for educational assessment, the positives and negatives of confidential data collections, and challenges for implementation from the view of various stakeholders.

Methodology: The study is conceptual, and information has been collected from sources comprised of expert interactions, research publications, survey and Industry reports.

Findings: The use of AI in student performance assessment has helped in early predictions for the activities to be adopted by educators. Results of AI evaluations give the data that may be combined and understood to create visuals.

Research Implications: AI-based analytics helps in fast decision-making and adapting the teaching curriculum’s fast-changing industry needs. Students’ abilities, such as participation and resilience, and qualities, such as confidence and drive, may be appraised using AI assessment systems.

Theoretical Implication: Artificial intelligence-based evaluation gives instructors, students, and parents a continuous opinion on how students learn, the help they require, and their progress towards their learning objectives.

We argue that a fundamental issue regarding how to search and how to switch between different cognitive modes lies in the decision rules that influence the dynamics of learning and exploration. We examine the search logics underlying these decision rules and propose conceptual prompts that can be applied mentally or computationally to aid managers’ decision-making.

By applying Multi-Armed Bandit (MAB) modeling to simulate agents’ interaction with dynamic environments, we compared the patterns and performance of selected MAB algorithms under different configurations of environmental conditions.

We develop three conceptual prompts. First, the simple heuristic-based exploration strategy works well in conditions of low environmental variability and few alternatives. Second, an exploration strategy that combines simple and de-biasing heuristics is suitable for most dynamic and complex decision environments. Third, the uncertainty-based exploration strategy is more applicable in the condition of high environmental unpredictability as it can more effectively recognize deviated patterns.

This study contributes to emerging research on using algorithms to develop novel concepts and combining heuristics and algorithmic intelligence in strategic decision-making.

This study offers insights that there are different possibilities for exploration strategies for managers to apply conceptually and that the adaptability of cognitive-distant search may be underestimated in turbulent environments.

Drawing on insights from machine learning and cognitive psychology research, we demonstrate the fitness of different exploration strategies in different dynamic environmental configurations by comparing the different search logics that underlie the three MAB algorithms.

This systematic literature review aims to explore the adoption, global acceptance and implementation of human resources (HR) analytics (HRA) by reviewing literature on the subject. HRA adoption can assist HR professionals in managing complex procedures and making strategic human resource management (SHRM) decisions more effectively. The study also aims to identify the applications of analytics in various disciplines of management.

The review is conducted using a domain-based structured literature review (SLR), emphasizing the diffusion of innovative thinking and the adoption process of HRA among early adopters. The philosophical stances are analyzed with the combination of research onion model and PRISMA protocol. Secondary data are gathered from published journals, books, case studies, conference proceedings, web pages and media stories as the primary source of information.

The study finds that skilled professionals and management assistance can significantly impact adoption intentions, enabling professionals to deal with analytics. The examples and analytical models provided by early adopters allow managers to manage complex processes and make SHRM decisions.

The study suggests that the lack of use of quantitative techniques is a key limitation and should be considered in future studies. Despite the rise in the number of research papers on HRA, its application in the workplace remains limited.

This research can assist managers in implementing HRA and help resolve complex and inefficient processes, making SHRM decisions.

This study adds to the existing body of knowledge on how HRA can aid a company's efficacy and performance and can be considered one of the first to link adoption and HRA.