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The purpose of this paper is to develop a new software reliability growth model considering different fault distribution function before and after the change point.

In this paper, the authors have developed a framework to incorporate change-point in developing a hybrid software reliability growth model by considering different distribution functions before and after the change point.

Numerical illustration suggests that the proposed model gives better results in comparison to the existing models.

The existing literature on change point-based software reliability growth model assumes that the fault correction trend before and after the change is governed by the same distribution. This seems impractical as after the change in the testing environment, the trend of fault detection or correction may not follow the same trend; hence, the assumption of same distribution function may fail to predict the potential number of faults. The modelling framework assumes different distributions before and after change point in developing a software reliability growth model.

Almost everything around us is the output of software-driven machines or working with software. Software firms are working hard to meet the user’s requirements. But developing a fault-free software is not possible. Also due to market competition, firms do not want to delay their software release. But early release software comes with the problem of user reporting more failures during operations due to more number of faults lying in it. To overcome the above situation, software firms these days are releasing software with an adequate amount of testing instead of delaying the release to develop reliable software and releasing software patches post release to make the software more reliable. The paper aims to discuss these issues.

The authors have developed a generalized framework by assuming that testing continues beyond software release to determine the time to release and stop testing of software. As the testing team is always not skilled, hence, the rate of detection correction of faults during testing may change over time. Also, they may commit an error during software development, hence increasing the number of faults. Therefore, the authors have to consider these two factors as well in our proposed model. Further, the authors have done sensitivity analysis based on the cost-modeling parameters to check and analyze their impact on the software testing and release policy.

From the proposed model, the authors found that it is better to release early and continue testing in the post-release phase. By using this model, firms can get the benefits of early release, and at the same time, users get the benefit of post-release software reliability assurance.

The authors are proposing a generalized model for software scheduling.

This study aims to analyse a proposed model depicting the direct and indirect relationship between consumer perceptions of corporate social responsibility (CSR) and customer citizenship behaviour (CCB) in the banking industry.

In this study, data was collected from banking consumers in India. The final sample included 505 responses. The hypotheses were tested using structural equation modelling.

The findings suggest that consumer perceptions of CSR are positively related to consumer identification with the company and CCB. Also, the relationship between consumer perception of CSR and CCB is mediated through consumer–company identification. This suggests that CSR activities are positively related with the consumer identification with their company, which encourages CCB.

This study contributes empirically and theoretically to expand the limited knowledge about the cognitive link between CSR and consumer behaviour. This study provides new insights about the proposed relationships related to the effects of consumer perception of CSR on CCB.

Effective inventory management is crucial for SMEs due to limited resources and higher risks like cash flow, storage space, and stockouts. Hence, the aim is to explore how technology and know-how can be integrated with inventory practices and impact operational performance.

The basis of the analysis was collecting papers from a wide range of databases, which included Scopus, Web of Science, and Google Scholar. In the first phase of the process, a search string with as many as nine related keywords was used to obtain 175 papers. It further filtered them based on their titles and abstracts to retain 95 papers that were included for thorough analysis.

The study introduced innovative methods of measuring inventory practices by exploring the impact of know-how. It is the first of its kind to identify and demonstrate how technical, technological, and behavioral know-how can influence inventory management practices and ultimately impact the performance of emerging SMEs. This study stands out for its comprehensive approach, which covers traditional and modern inventory management technologies in a single study.

The study provides valuable insights into the interplay between technical, technological, and behavioral know-how in inventory management practices and their effects on the performance of emerging SMEs in Industry 5.0 in the light of RBV theory.

The RBV theory and the Industry 5.0 paradigm are used in this study to explore how developing SMEs' inventory management practices influence their performance. This study investigates the effects of traditional and modern inventory management systems on business performance. Incorporating RBV theory with the Industry 5.0 framework investigates firm-specific resources and technological advances in the current industrial revolution. This unique technique advances the literature on inventory management and has industry implications.

The purpose of this paper is to examine the impact of knowledge of inventory management practices (KIMP) on operational performance (OP) and business performance (BP). This study emphasized understanding the mediational effect of OPs among KIMP and BP in manufacturing firms of Indian small- and medium-sized enterprises (SMEs).

This study analysed the above relationship from the data collected from 351 key officials of 170 Indian SMEs. The detailed analysis, including reliability, validity and testing hypothesis, was done in advanced SmartPLS-SEM 3.3.3 software.

The findings show that KIMP directly influences OP; thus, a company's OP significantly impacts the overall BP. The KIMP has no significant effect on BP. In line with this, the study findings demonstrate a significant, high mediation effect of OPs between KIMP and BP.

The KIMP on OP has been sparsely studied earlier, and neither have they conceptualized in understanding its impact on BP. In such tumultuous times, key manufacturing executives should stress the importance of KIMP as a comprehensive perspective.

Deep learning (DL) is on the rise because it can make predictions and judgments based on data that is unseen. Blockchain technologies are being combined with DL frameworks in various industries to provide a safe and effective infrastructure. The review comprises literature that lists the most recent techniques used in the aforementioned application sectors. We examine the current research trends across several fields and evaluate the literature in terms of its advantages and disadvantages.

The integration of blockchain and DL has been explored in several application domains for the past five years (2018–2023). Our research is guided by five research questions, and based on these questions, we concentrate on key application domains such as the usage of Internet of Things (IoT) in several applications, healthcare and cryptocurrency price prediction. We have analyzed the main challenges and possibilities concerning blockchain technologies. We have discussed the methodologies used in the pertinent publications in these areas and contrasted the research trends during the previous five years. Additionally, we provide a comparison of the widely used blockchain frameworks that are used to create blockchain-based DL frameworks.

By responding to five research objectives, the study highlights and assesses the effectiveness of already published works using blockchain and DL. Our findings indicate that IoT applications, such as their use in smart cities and cars, healthcare and cryptocurrency, are the key areas of research. The primary focus of current research is the enhancement of existing systems, with data analysis, storage and sharing via decentralized systems being the main motivation for this integration. Amongst the various frameworks employed, Ethereum and Hyperledger are popular among researchers in the domain of IoT and healthcare, whereas Bitcoin is popular for research on cryptocurrency.

There is a lack of literature that summarizes the state-of-the-art methods incorporating blockchain and DL in popular domains such as healthcare, IoT and cryptocurrency price prediction. We analyze the existing research done in the past five years (2018–2023) to review the issues and emerging trends.

This study aims to examine the effect of supply chain agility (SCA) on operational performance (OP) measurements of steel manufacturing firms. It also investigates the role of cost efficiencies concerning enhance OPs.

The study is based on an experimental research design by collecting data from responses 398 responses of key officials of India’s steel manufacturing firms. Analyses are carried to explore this modern concept with the help of Smart-partial least square (PLS) version 3.3.2 with confirmatory factor analysis and PLS structural equational modelling.

SCA factor (SCAF) directly has influenced the firm’s OP. It also represents cost efficiencies that have partial mediation between the SCAF and OP. The impact of cost efficiencies on OPs is strongly significant as compared to the impact of SCAF on cost efficiencies.

Management teams in the manufacturing industry should stress the role of SCA as a comprehensive concept in responding to market needs in a volatile environment. SCA reflects one of its winning strategies in today’s dynamic and competitive world. Managers must thoroughly know the ramifications of agility to develop a mechanism for determining the procedures and identifying inequality in SC operation.

This study speaks explicitly about the linkage between SCAF, OP, CE. It is an addition to the existing theories of RBV. Enhancements in OP measurements, specifically performance and flexibility, will lead to better firm performance. study conceptualizing the complementing effects of SCA (IS capability) and OPs and second cost efficiencies play positive partial mediating effect in between the link. The achievement of SC agile is especially a critical approach to Boost customer satisfaction and differentiate market position.

The purpose of this study is to conduct a systematic study of important research trends and published electronic word of mouth (eWOM) studies over the past 20 years in the emerging economy. This research is designed to identify the key areas of eWOM based on the Antecedents-Consequence-Intervention (ACI) framework. This paper is also aimed to analyze the current research status of each WOM area and critically review each area for developing future research directions.

616 articles were selected from 112 journals for in-depth review analysis. For the systematic review of articles, an ACI framework was adopted. This paper used systematic review analysis methodology to critically analyze important research studies in each area of the ACI framework with a set of research questions.

Results identify six significant areas of eWOM, i.e. WOM antecedents, outcomes, senders, receivers, eWOM platform and eWOM management representing the whole environment. The result highlights increasing research interest on mixed eWOM and rich eWOM content and market-level source credibility factors. Research also identifies research gaps based on the ACI framework.

Analyzing the recent trends in the eWOM environment with the SLR approach and linking these trends to the ACI framework with a future research agenda in the emerging market indicated a pioneering attempt in eWOM research.

The problem of evaluating potential suppliers has always been based on finding an optimal tradeoff between supplier’s performance consistently meeting firms’ needs and acceptable cost. The purpose of this paper is to propose a hybrid multi-criteria decision framework to quantify this qualitative judgment and reduce ambiguity in selection of suppliers in the era of Industry 4.0.

A hybrid intuitionistic fuzzy entropy weight-based multi-criteria decision model with TOPSIS is proposed. The authors make use of the intuitionistic fuzzy weighted approach operator for aggregating individual decision maker’s opinions regarding each alternative over every criterion. Additionally, the authors employ the concept of Shannon’s entropy to calculate the criteria weights.

Results obtained on the basis of the proposed hybrid methodology are analyzed against two more cases wherein the authors try to showcase the relevance of using IFS and entropy-based decision framework and find out the uniqueness of the proposed framework in supplier selection process.

The proposed model is apposite to solve management problem of supplier selection in two ways: aggregating individual decision maker’s opinion for each of the predefined criteria along with individual decision maker’s importance and ranking the suppliers based on both positive and negative ideal solutions using TOPSIS.

A robust framework incorporates not only suppliers’ performance but also provides weightage to key decision makers. Especially in the context of MCDMs wherein both qualitative and quantitative data is evaluated simultaneously, the proposed framework is unique in its practical implementation of reducing ambiguity in the supplier selection process.

As the number of web applications is increasing day by day web mining acts as an important tool to extract useful information from weblogs and analyse them according to the attributes and predict the usage of a website. The main aim of this paper is to inspect how process mining can be used to predict the web usability of hotel booking sites based on the number of users on each page, and the time of stay of each user. Through this paper, the authors analyse the web usability of a website through process mining by finding the web usability metrics. This work proposes an approach to finding the usage of a website using the attributes available in the weblog which predicts the actual footfall on a website.

PROM (Process Mining tool) is used for the analysis of the event log of a hotel booking site. In this work, authors have used a case study to apply the PROM (process mining tool) to pre-process the event log dataset for analysis to discover better-structured process maps than without pre-processing.

This article first provided an overview of process mining, then focused on web mining and later discussed process mining techniques. It also described different target languages: system nets (i.e. Petri nets with an initial and a final state), inductive miner and heuristic miner, graphs showing the change in behaviour of the dataset and predicting the outcome, that is the webpage having the maximum number of hits.

In this work, a case study has been used to apply the PROM (process mining tool) to pre-process the event log dataset for analysis to discover better-structured process maps than without pre-processing.