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Maintenance actions are initiated by the annual maintenance budget (AMB) allocated for a plant system. It ensures that the assets are kept in a healthy and reliable state. The purpose of this paper is to scientifically evaluate the AMB for an air conditioning (AC) plant system, which forms an integral part of facility management.

This paper estimates the AMB of an AC system for specific contextual conditions using incidence matrix of the budget influencing variables. The diagonal elements of the matrix represent the values of the budget variables, whereas the off-diagonal elements represent the degree of influence among these. The maintenance budget function is derived from this matrix and is used to evaluate the AMB for the AC system as a percentage of its asset replacement value (ARV).

The AMB for the AC plant system considered in the work is evaluated as 11.1 per cent of ARV for the conditions unique to its context. The actual maintenance expenditure is lower by 7.6 per cent than the estimated value of the budget.

The results of the methodology are a good guide for practicing facility managers. The shortcoming of the methodology is that it relies on fixed weights of the inter-relations among the budget variables, which may not be necessarily true.

This approach is expected to aid the maintenance managers in envisaging the AMB for their plant systems which operate under contextual conditions specific to their plant.

There is no evidence to indicate existence of maintenance budget evaluation methodology for an AC plant system based on its contextual conditions. This paper attempts to fill this gap.

Material handling (MH) is an important facility in any manufacturing system. It facilitates the transport of in-process material from one workstation (WS) to another. MH devices do imply incurring capital costs and, therefore, minimizing their deployment without compromising on smooth material flow will ensure savings in addition to the optimal use of productive shop floor space and, avoid space cluttering. The purpose of this paper is to evaluate the minimal network that connects all the WSs, therefore ensuring economic and safe manufacturing operations.

Graph theoretical approach and Prim’s algorithm for minimal spanning tree is used to evaluate the minimal span of the MH devices. The algorithm is initialized by translating the graph of WSs into a distance matrix to evaluate the minimal MH network.

The minimal length of the MH devices is evaluated for a typical case study.

The step-by step methodology explained in the manuscript acts as a good guide for practicing operational managers. The shortcoming of the methodology is that, it presumes the use of modular MH devices that will need to be reconfigured based on dynamic changes to the manufacturing system.

The methodology is explained in detail to enable the practicing managers to use it for designing their MH networks in any manufacturing system.

There is no evidence to indicate the use of minimal spanning tree algorithm for design of MH networks in a manufacturing system. This paper attempts to fill this gap.

Grounded in the stakeholder theory and signaling theory, this study aims to broaden the research agenda on environmental, social and governance (ESG) investing by uncovering public sentiments and key themes using Twitter data spanning from 2009 to 2022.

Using various machine learning models for text tonality analysis and topic modeling, this research scrutinizes 1,842,985 Twitter texts to extract prevalent ESG investing trends and gauge their sentiment.

Gibbs Sampling Dirichlet Multinomial Mixture emerges as the optimal topic modeling method, unveiling significant topics such as “Physical risk of climate change,” “Employee Health, Safety and well-being” and “Water management and Scarcity.” RoBERTa, an attention-based model, outperforms other machine learning models in sentiment analysis, revealing a predominantly positive shift in public sentiment toward ESG investing over the past five years.

This study establishes a framework for sentiment analysis and topic modeling on alternative data, offering a foundation for future research. Prospective studies can enhance insights by incorporating data from additional social media platforms like LinkedIn and Facebook.

Leveraging unstructured data on ESG from platforms like Twitter provides a novel avenue to capture company-related information, supplementing traditional self-reported sustainability disclosures. This approach opens new possibilities for understanding a company’s ESG standing.

By shedding light on public perceptions of ESG investing, this research uncovers influential factors that often elude traditional corporate reporting. The findings empower both investors and the general public, aiding managers in refining ESG and management strategies.

This study marks a groundbreaking contribution to scholarly exploration, to the best of the authors’ knowledge, by being the first to analyze unstructured Twitter data in the context of ESG investing, offering unique insights and advancing the understanding of this emerging field.

Amidst the turbulent tides of geopolitical uncertainty and pandemic-induced economic disruptions, the information technology industry grapples with alarming attrition and aggravating talent gaps, spurring a surge in demand for specialized digital proficiencies. Leveraging this imperative, firms seek to attract and retain top-tier talent through generous compensation packages. This study introduces a holistic, integrated theoretical framework integrating machine learning models to develop a compensation model, interrogating the multifaceted factors that shape pay determination.

Drawing upon a stratified sample of 2488 observations, this study determines whether compensation can be accurately predicted via constructs derived from the integrated theoretical framework, employing various cutting-edge machine learning models. This study culminates in discovering a random forest model, exhibiting 99.6% accuracy and 0.08° mean absolute error, following a series of comprehensive robustness checks.

The empirical findings of this study have revealed critical determinants of compensation, including but not limited to experience level, educational background, and specialized skill-set. The research also elucidates that gender does not play a role in pay disparity, while company size and type hold no consequential sway over individual compensation determination.

The research underscores the importance of equitable compensation to foster technological innovation and encourage the retention of top talent, emphasizing the significance of human capital. Furthermore, the model presented in this study empowers individuals to negotiate their compensation more effectively and supports enterprises in crafting targeted compensation strategies, thereby facilitating sustainable economic growth and helping to attain various Sustainable Development Goals.

The cardinal contribution of this research lies in the inception of an inclusive theoretical framework that persuasively explicates the intricacies of a machine learning-driven remuneration model, ennobled by the synthesis of diverse management theories to capture the complexity of compensation determination. However, the generalizability of the findings to other sectors is constrained as this study is exclusively limited to the IT sector.

The purpose of this paper is to propose a methodology to evaluate the annual maintenance budget (AMB) for a plant system as a percentage of its asset replacement value (ARV).

Variables influencing the budget for the plant system are identified and modeled in terms of a plant maintenance Budget digraph. The nodes in the digraph represent the function of budget influencing variables and edges represent the degree of influence among these. The plant maintenance budget function is derived from the equivalent matrix of the plant maintenance budget digraph and is used to evaluate the AMB for the plant system as a percentage of its ARV. The presented model is illustrated with an example.

The proposed methodology enables the maintenance managers and practicing engineers to evaluate the AMB for maintenance tasks based on plant specific variables that can vary from one plant to another, without resorting to general rules-of-thumb method of budgeting or to expert judgment.

The methodology suggested does not claim to cover all aspects of budgeting and is confined to direct maintenance costs.

A fair estimation of AMB for a plant system gives an appropriate direction to maintenance actions and ensures that the assets will be kept in healthy and reliable state. This is desirable because it will ensure that maintenance resources are appropriately benchmarked and the resources are utilized judiciously. This ensures maintenance effectiveness.

The paper presents a structural approach to budgeting for maintenance tasks. This approach is valuable for plant managers, with ease in implementation.

The purpose of this paper is to analyse the shifting nature of governance reforms, both at global and national levels, in the increasingly commercialised game of cricket. The authors explore the inter-relationship and linkages between governance and commercialism, and in the process, question the contemporary reliance placed on governance as a generic counter-commercialist force and accountability aid.

The analysis is based on a comprehensive analysis of cricketing archives, newspapers and online media. The authors specifically utilise a range of review reports, governance and accounting information from annual reports and websites of the International Cricket Council (ICC) as well as different national cricket governing bodies (NCBs).

The paper vividly demonstrates the importance of recognising the specific significance of different cultural traditions and modes of organising – and not presuming a particular form of impact. The findings highlight that the adoption of a dominant market logic by cricket administrators has resulted in a shift in the balance of power in favour of non-western nations. India has emerged as the clear leader and driving force shaping the way cricket is globally governed. The consequences have been profound but not in terms of delivering, enhanced standards of transparency and accountability. Drawing on institutional theory, the paper argues that the scale of the Board of Cricket Control of India’s financial and operational control over the ICC has not only led to an increasingly commercialised game but engendered divergent and highly questionable standards of governance at the level of NCBs.

Unlike other global games, cricket has an imperialistic root, and has gone through the process of globalisation in relatively recent times. Also, the commercialisation of cricket has resulted in the global economic and power base shifting from the West to the East, giving us the opportunity to study the dynamics between commercialisation and governance in a quite different globalisation context that allows an assessment to be made of the culturally contingent nature of governance as a substantive organising force.

The Indian manufacturing sector currently contributes 16–17% to gross domestic product (GDP) and gives employment to around 12% (2014) of the country's workforce. Among the various initiatives planned under Atma Nirbhar Bharat and “Make in India”, the Indian government aims to increase the share of the manufacturing sector to country's GDP to 25% by 2025. To ensure sustainable growth of the Indian manufacturing industries in global market, successful and implementation of strategies such as lean manufacturing, green manufacturing along with six sigma are crucial. This research aims at identification and analysis of barriers to successful implementation of integrated Lean Green Six Sigma (LG&SS) strategies.

Based on an evaluative literature review and expert's opinion, this research identifies 18 barriers to lean, green and six sigma implementation in the manufacturing industry in India. The inter-relationships among the barriers is identified using an integrated approach of ISM (interpretive structural modelling) and Fuzzy Matrice d’Impacts Croisés Multiplication Appliquée á un Classement (MICMAC).

ISM helps to derive key managerial insights and implementation plan based on the identified inter-relationships among the barriers. Fuzzy MICMAC analysis classifies the barriers into four categories, namely, autonomous, driver, dependent and linkage to understand their relative impact on the implementation of LG&SS practices in the Indian manufacturing industry. “Lack of cooperation and mutual trust between management and employees”, “The scarcity of time and work pressure”, “lack of continuous improvement work culture” and “lack of cooperation from suppliers” that forms the top most level of the model. “Weak legislation” is a highly significant barrier to LG&SS implementation in the Indian manufacturing industries.

It is expected that the findings of this research will help the Indian manufacturing industry to derive a sustainable competitive advantage through an effective implementation of LG&SS practices.

This study can be seen as the first attempt in investigating barriers to successful implementation of lean, green and six sigma strategies in the Indian manufacturing industries using ISM and fuzzy MICMAC analysis.

Lack of appropriate student support and drawbacks in academic progression signify the importance of enhancing assessment and feedback in higher education (HE). Although assessment and feedback are significant in HE, minimal empirical research holistically explores the best practices. This study aims to address the niche and develop a decisive guideline for enhancing assessment setting and feedback provision within HE curricula.

A systematic approach was taken to obtain data for the study: a literature review underpinning the thematic content analysis of study documents, followed by semi-structured interviews. Document analysis contained mid-module reviews/student feedback; rubrics used in assessment; and formative/summative feedback provided for the graded work. Documental analysis informed the key attributes of the semi-structured interview. Interpretive structural modelling (ISM) analysis identified the influence and reliance of each driver.

This study revealed 15 drivers – 4 fundamental, 6 significant and 5 important – for enhancing assessment and feedback. The level partitioning from the ISM analysis established that all assessment and feedback needs to be underpinned by the university policy and fed into the assessment regime and marking scheme. This study identified that National Student Survey results were significantly improved due to implementing said drivers compared with the national and sector benchmarks.

The developed drivers enable the best practices in assessment setting and feedback provision. The level partition diagram can be used as a decisive guideline or a provisional framework in assessment and feedback provision for quality assurance in HE.

This study is one of, if not the only, to develop a guideline for signposting drivers and their influence and reliance to enhance assessment and feedback in a holistic HE setting. The developed drivers and the level partition diagram bring novelty and add to the current body of knowledge.

Purpose: This chapter discusses the role and use of chatbots adopted by the different categories of banks (private and public sector banks) in India. The chapter presents brief essential services offered by Indian chatbots regarding accuracy, technology providers and virtual assistance, ways to connect, etc. This chapter concluded that most of the questions answered by the Indian chatbots are already available on the banks’ websites, and there is a need for enhancement in the capabilities of Indian chatbots.

Need for the Study: The need for the study is based on the working of banking chatbots, customer query handling, and the efficiency of the chatbots in India. The chapter helps to analyze the services offered by various banks.

Methodology: This chapter is based on secondary data collected from banks’ websites and articles from various journals. The study is based on nine banks (both private and public sectors) those are having working chatbots (SBI, HDFC Bank, ICICI Bank, Yes Bank, IndusInd Bank, Kotak Mahindra Bank, Axis Bank, Andhra Bank, Bank of Baroda). The present study is focused on chatbots, their services, and software applications for various customer-handling capacities.

Findings: The research concluded that Indian banks are investing a small amount in using chatbots, yet Indian chatbots are deficient regarding far too provincial administrations as they are adequate just for standard and basic inquiries. Also, Indian customers are not properly aware of chatbots and virtual assistance.

Practical Implications: This study provides an overview of the working chatbots in India (for both public and private sector banks) and their functions, as well as the capacities of these chatbots. The previous conducted studies are based on the uses, importance, and working of chatbots/artificial intelligence (AI) in banking. In this study, after discussing the different services, it is found that Indian banks need to update their AI/Virtual assistance with more features.