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A maturity model for Industry 4.0 (I4.0 MM) with influencing factors is designed to address maturity issues in adopting Industry 4.0. Standardisation in I4.0 supports manufacturing industry transformation, forming reference architecture models (RAMs). This paper aligns key factors and maturity levels in I4.0 MMs with reputable I4.0 RAMs to enhance strategy for I4.0 transformation and implementation.

Three steps of alignment consist of the systematic literature review (SLR) method to study the current published high-quality I4.0 MMs, the taxonomy development of I4.0 influencing factors by adapting and implementing the categorisation of system theories and aligning I4.0 MMs with RAMs.

The study discovered that different I4.0 MMs lead to varied organisational interpretations. Challenges and insights arise when aligning I4.0 MMs with RAMs. Aligning MM levels with RAM stages is a crucial milestone in the journey toward I4.0 transformation. Evidence indicates that I4.0 MMs and RAMs often overlook the cultural domain.

Findings contribute to the literature on aligning capabilities with implementation strategies while employing I4.0 MMs and RAMs. We use five RAMs (RAMI4.0, NIST-SME, IMSA, IVRA and IIRA), and as a common limitation in SLR, there could be a subjective bias in reading and selecting literature.

To fully leverage the capabilities of RAMs as part of the I4.0 implementation strategy, companies should initiate the process by undertaking a thorough needs assessment using I4.0 MMs.

The novelty of this paper lies in being the first to examine the alignment of I4.0 MMs with established RAMs. It offers valuable insights for improving I4.0 implementation strategies, especially for companies using both MMs and RAMs in their transformation efforts.

This study aims to enhance understanding of malnutrition's effect on academic achievement of primary school students.

This is a descriptive, cross-sectional design built on Roy's adaptation model (RAM). This study uses a random cluster sample, consisting of 453 primary school students. Contextual stimuli (mother's educational level, income and child’s breakfast eating) and focal stimuli (wasting, thinness, body mass index and stunting) were examined regarding adaptive responses to student’s academic achievement.

The investigation revealed that Model 1, which took into account factors of age, gender, the frequency of breakfast, income, the number of family members and the education of mothers, explained 12% (R² = 0.12) of the variance in academic achievement. Stuntedness (β = −3.2 and p < 0.01), BMI (β = 0.94 and p < 0.001), family income per month (β = 5.60 and p < 0.001) and mother's education (β = 2.79 and p < 0.001) were the significant predictors in Model 2.

This study provides evidence that malnutrition is associated with ineffective academic achievement. Moreover, variables such as the mother's level of education, family income and the child’s breakfast consumption have a significant impact on academic achievements.

RAM is a useful framework for determining factors affecting people's reactions to difficult circumstances.

This study aims to investigate the relationship between corporate sustainability performance and earnings management in emerging East Asian economies.

The authors base on the triple bottom line approach to measure corporate sustainability performance. In terms of earnings management, two models are applied to detect real activities manipulation and discretionary accruals. The authors use panel data analysis of 410 listed non-financial firms in emerging East Asian economies from 2016 to 2020 that are collected from the Thomson Reuters Eikon database.

The authors find a negative influence of corporate sustainability performance on real activities manipulation and discretionary accruals. The findings highlight the long-term perspective of sustainable development strategies in relation to earnings management. The authors conclude that sustainable firms in emerging East Asia are less likely to engage in earnings management.

The study would be of interest to investors who need more detailed assessments of financial reporting quality to facilitate their investment decision-making and to policymakers who need more understanding of business practices and reporting behaviors of East Asian firms.

The study has shed light on the role of corporate sustainability performance in constraining earnings management and the role of corporate ethics in providing transparent and reliable financial reporting in emerging East Asian economies.

This study aims to address the question of why managers make different decisions in employee downsizing when their firms face external threats. Our research intends to shed light on whether and how CEOs' cognition (motivational attributes associated with regulatory focus) influences their decision-making and firms’ strategic actions on downsizing under high resource scarcity in the industry environment.

We used a longitudinal panel of 5,544 firm-year observations of US firms from 2003 to 2015 to test our conceptual model. The data was obtained from various sources, including corporate earnings call transcripts and archival databases. We used panel logistic regressions with both fixed and random effects in our research design.

Our results suggest that CEOs' motivational attributes could influence their employee downsizing decisions in response to external threats. We find that CEOs who are more promotion-focused (a stronger drive towards achieving ideals) are less likely to lay off employees during high resource scarcity. Conversely, CEOs with a higher prevention focus (a greater concern for security) do not have a meaningful impact on employee downsizing during periods of external resource scarcity.

Previous research has argued that a significant external threat would diminish individuals' impact on firm strategies and outcomes. Our findings challenge this idea, indicating that CEOs with a stronger drive towards achieving ideals are less inclined to lay off employees when resources are scarce in the environment. This study contributes to behavioral strategy research by providing new insights into how upper echelons’ cognition can influence their decision-making and firms’ employee downsizing.

Cloud computing services gained huge attention in recent years and many organizations started moving their business data traditional server to the cloud storage providers. However, increased data storage introduces challenges like inefficient usage of resources in the cloud storage, in order to meet the demands of users and maintain the service level agreement with the clients, the cloud server has to allocate the physical machine to the virtual machines as requested, but the random resource allocations procedures lead to inefficient utilization of resources.

This thesis focuses on resource allocation for reasonable utilization of resources. The overall framework comprises of cloudlets, broker, cloud information system, virtual machines, virtual machine manager, and data center. Existing first fit and best fit algorithms consider the minimization of the number of bins but do not consider leftover bins.

The proposed algorithm effectively utilizes the resources compared to first, best and worst fit algorithms. The effect of this utilization efficiency can be seen in metrics where central processing unit (CPU), bandwidth (BW), random access memory (RAM) and power consumption outperformed very well than other algorithms by saving 15 kHz of CPU, 92.6kbps of BW, 6GB of RAM and saved 3kW of power compared to first and best fit algorithms.

The proposed multi-objective bin packing algorithm is better for packing VMs on physical servers in order to better utilize different parameters such as memory availability, CPU speed, power and bandwidth availability in the physical machine.

There are two main objectives in this study. First, we aim to develop a set of constructs for health task management support (HTMS) features to evaluate which health-related tasks are supported by mobile health application (mHealth app) functions. Second, drawing on innovation resistance theory (IRT), we examine the impacts of the newly developed HTMS dimensions on perceived usefulness, alongside other barrier factors contributing to technology anxiety.

Using a mixed-method research design, this research seeks to develop new measurement scales that reflect how mHealth apps support older adults’ health-related needs based on interviews. Subsequently, data were collected from older adults and exploratory factor analysis was used to confirm the validity of the new scales. Partial least squares structural equation modeling (PLS-SEM) was used to analyze survey data from 602 older adults.

The PLS-SEM results indicated that medical management task support, dietary task support, and exercise task support were positively associated with perceived usefulness, while perceived complexity and dispositional resistance to change were identified as antecedents of technology anxiety. Perceived usefulness and technology anxiety were found to positively and negatively influence adoption intention, respectively.

This study enriches the information systems literature by developing a multidimensional construct that delineates how older adults’ health-related needs can be supported by features of mHealth apps. Drawing on IRT, we complement the existing literature on resistance to innovation by systematically examining the impact of five types of barriers on technology anxiety.

This study aims to examine the mediating role of green finance in the relationship between green banking practices and the sustainability performance of banking institutions in developing economies.

The authors performed an empirical investigation by applying the “partial least squares structural equation modeling (PLS-SEM)” based on a representative sample of 414 bank employees working in the National Capital Region, India.

The study’s outcome confirms that employee, top-management, operation and policy related practices substantially influence green finance and banks’ sustainability performance. On the contrary, customer related practices insignificantly influence banks’ sustainability performance. Further, green finance substantially influences the sustainability performance of banking institutions.

This study shed light on green banking practices that can assist in achieving the vision of the “Clean India Mission” of the Indian government. In addition, it encourages policymakers and bank managers to fulfill their social responsibility by engaging employees and customers in cleaner operations to promote banks’ sustainability performance.

This is ground-breaking research that enriches the understanding of green banking practices and green finance by providing a novel theoretical framework concerning the sustainability performance of banking institutions. Theoretically, this paper also broadens the scope of corporate social responsibility literature by applying the resource-based view theory in finance and banking.

ChatGPT is an advanced artificial intelligence (AI) form that can generate human-like text based on large amounts of data. This paper aims to empirically examine the ChatGPT adoption level among Indian individuals by considering the key factors in determining individuals’ attitudes and intentions toward newly emerged AI tools.

This paper used “partial least square structural equation modeling” (PLS-SEM) to investigate the relation among several latent factors by applying a representative sample of 351 individuals.

This study found that trialability, performance expectancy and personal innovativeness significantly influence individuals' attitudes, while compatibility and effort expectancy do not significantly impact attitudes. Additionally, trialability, performance expectancy, effort expectancy, personal innovativeness and attitude significantly influence behavioral intentions. However, compatibility has an insignificant impact on behavioral intention. Moreover, the research highlights that attitude and behavioral intention directly correlate with actual use. Specifically, the absence of compatibility makes people hesitate to use technology that does not meet their specific needs.

These unique findings provide valuable insights for technology service providers and government entities. They can use this information to shape their policies, deliver timely and relevant updates and enhance their strategies to boost the adoption of ChatGPT.

This paper is one of the pioneering attempts to exhibit the research stream to understand the individual acceptance of ChatGPT in an emerging country. Moreover, it gained significant attention from individuals for delivering a unique experience and promising solutions.

This paper aims to investigate the impact of the revised Code of Corporate Governance 2017 (CCG-2017) clauses pertaining to board independence, mandatory inclusion of female directors, audit committee (AC) chair independence and directors’ expertise on earnings manipulation.

Using an unbalanced panel of 323 listed companies from 2015 to 2019, this study uses panel data regression models with a robust methodology called difference-in-differences to tackle the potential endogeneity.

This study’s findings show that, as compared to the pre-CCG-2017 period, board- and AC-related variables increased significantly in the post-CCG-2017 period. Furthermore, financial experts on the board and board independence have a negative effect on discretionary accruals (DAs), whereas female directors and DAs are positively related, as is real activity manipulation. The AC-related variables, such as AC independence, expertise in AC, and AC chair independence, are significantly different from the preperiod to the postperiod, whereas their relationship is not according to the hypotheses of the study. Moreover, these results are robust to additional analysis of the alternative proxies for female directorship and the endogeneity problem.

The findings of this study have implications for regulators and practitioners who are concerned with the functions of the board of directors (BOD). The findings of this research study show that earnings management (EM) may be reduced by independent and expert directors. However, board gender diversity is not reducing the EM. Therefore, the decision to appoint female directors to the board should be based on their business and professional attributes rather than simply filling quotas or blindly adhering to regulations. Moreover, the findings of this research may assist the regulator in encouraging listed firms to enhance board governance via independence, diversity and competency, which are useful for effective monitoring.

This study fills a gap in the literature by providing the first evidence of country-specific regulation (CCG-2017), concerning the BOD and AC-related clauses on EM in Pakistan, which is missing in the relevant literature general and in Pakistan in particular.

In this research work, the general form of reliability measures, which include availability, mean time to failure (MTTF), and sensitivity analysis, are investigated with their graphical representation, which would help designers and engineers improve the reliability of the system. Along with reliability assessment, a mathematical model is developed and solved to achieve the minimum cost of the system as well as maximum reliability.

In the proposed work, a general model of a solar seed sowing machine is considered for reliability evaluation using the Markov model process. The proposed system is a series-parallel arrangement of components where three components, namely the solar panel, batteries and direct current (DC) motor, are connected in series while all the operators are connected in parallel. The implemented Markov model approach assesses several parameters of reliability, which opens the scope for improvement in reliability and other measures like mean time to failure (MTTF) and sensitivity of the proposed system. So that the machine can deliver the desired output on the field. Also, the particle swarm optimization (PSO) algorithm is applied to optimize the cost of the system with the desired level of reliability.

Implementation of PSO provides the optimal cost for the proposed system with a predetermined level of reliability, which shows the relationship between reliability and cost of the system. Also, the Markov process approach provides the availability function, reliability function, reliability at different time values, MTTF and sensitivity of the proposed system.

This work evaluates the crucial characteristics of reliability for the proposed solar seed sowing machine using the Markov model which is a stochastic model. The assessment of reliability measures such as MTTF, availability and sensitivity plays a vital role in measuring and improving the performance of the machine in the actual environment. Examining the data obtained in this research is of great importance for the manufactures and system designers. Also, the powerful optimization technique PSO is implemented to solve a non-linear mixed-integer programming problem that provides the optimal cost for the system with desired reliability.