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The purpose of the present study is to investigate the community’s view of power outages and their effect on sustainable community development. This research has three aims. This project will first investigate how a blackout affects a community’s ability to live sustainably.

A survey questionnaire was developed that included 22 factors from the literature. The questionnaire was distributed to diversified segments of society from different regions of Pakistan and examined critical factors affecting sustainable community development. A total of 349 (77%) responses were received.

The study results confirm that power failure negatively affects the sustainable life of a community. This study found that the reduced production of large manufacturing, decrease leisure and comfort, reduced public administrative efficiency, loss of educational opportunities and increased use of traditional energy sources are the most important factors for sustainable community development. Findings also suggest that policymakers and practitioners in public organizations need to ensure that projects are completed in a timely manner to meet growing community needs.

The purpose of this research is to address knowledge gaps related to power outages and sustainable community development. The major impact of power disruptions on communities was previously disregarded in development discourse. Participants in these discussions recognize communities as genuine stakeholders and acknowledge that power outages can have a substantial impact on their economic and social development. However, previous studies have neglected to address this phenomenon.

The increasing awareness and adoption of technology, particularly artificial intelligence (AI), reshapes industries and daily life, fostering a proactive approach to risk management and leveraging advanced analytics, which may affect the stock price crash risk (SPCR). The main objective of the current study is to explore how AI adoption influences SPCR.

This study employs an Ordinary Least Squares (OLS) fixed-effect regression model to explore the impact of AI on SPCR in Chinese A-share listed companies from 2010 to 2020. Further, number of robustness analysis (2SLS, PSM and Sys-GMM) and channel analysis are used to validate the findings.

The primary findings emphasize that AI adoption significantly reduces SPCR likelihood. Further, channel analysis indicates that AI adoption enhances internal control quality, contributing to a reduction in firm SPCR. Additionally, the observed relationship is notably more pronounced in non-state-owned enterprises (non-SOEs) compared to state-owned enterprises (SOEs). Similarly, this distinction is heightened in nonforeign enterprises (non-FEs) as opposed to foreign enterprises (FEs). The study finding also supports the notion that financial analysts enhance transparency, reducing the SPCR. Moreover, the study results consistently align across different statistical methodologies, including 2SLS, PSM and Sys-GMM, employed to effectively address endogeneity concerns.

Our study stands out for its distinctive focus on the financial implications of AI adoption, particularly how it influences firm-level SPCR, an area that has been overlooked in previous research. Through the lens of information asymmetry theory, agency theory, and the economic implications of integrating AI into financial markets, our study makes a substantial contribution in mitigating SPCR.

This study underscores the pivotal role of AI adoption in influencing stock markets for enterprises in China. Embracing digital strategies, fostering transparency and prioritizing talent development are key for reaping substantial benefits. The study recommends regulatory bodies and service providers to promote AI adoption in strengthening financial supervision and ensure market stability, emphasizing the importance of investing in technologies and advancing talent development.

In the rapidly evolving digital economy, businesses face formidable pressures to maintain their competitive standing, prompting a surge of interest in the intersection of artificial intelligence (AI) and digital transformation (DT). This study aims to assess the impact of AI technologies on corporate DT by scrutinizing 3,602 firm-year observations listed on the Shanghai and Shenzhen stock exchanges. The research delves into the extent to which investments in AI drive DT, while also investigating how this relationship varies based on firms' ownership structure.

To explore the influence of AI technologies on corporate DT, the research employs robust quantitative methodologies. Notably, the study employs multiple validation techniques, including two-stage least squares (2SLS), propensity score matching and an instrumental variable approach, to ensure the credibility of its primary findings.

The investigation provides clear evidence that AI technologies can accelerate the pace of corporate DT. Firms strategically investing in AI technologies experience faster DT enabled by the automation of operational processes and enhanced data-driven decision-making abilities conferred by AI. Our findings confirm that AI integration has a significant positive impact in propelling DT across the firms studied. Interestingly, the study uncovers a significant divergence in the impact of AI on DT, contingent upon firms' ownership structure. State-owned enterprises (SOEs) exhibit a lesser degree of DT following AI integration compared to privately owned non-SOEs.

This study contributes to the burgeoning literature at the nexus of AI and DT by offering empirical evidence of the nexus between AI technologies and corporate DT. The investigation’s examination of the nuanced relationship between AI implementation, ownership structure and DT outcomes provides novel insights into the implications of AI in the diverse business contexts. Moreover, the research underscores the policy significance of supporting SOEs in their DT endeavors to prevent their potential lag in the digital economy. Overall, this study accentuates the imperative for businesses to strategically embrace AI technologies as a means to bolster their competitive edge in the contemporary digital landscape.

In the context of an evolving digital-based global economy, this study aims to investige the impact of digital transformation (DT) on a firm’s internal control (IC) quality. It also explores how the personal traits of (CEOs) – such as age, gender and educational background – intersect with DT to shape the IC quality in various types of state-owned enterprises (SOEs).

The study uses the data from China A-shares non-financial enterprises, listed on Shanghai and Shenzhen stock exchanges between 2007 and 2020. Using the fixed effect regression method alongside various statistical techniques, such as propensity score matching, alternative analysis and instrumental variables analysis, yields robust findings. These methods effectively address issues related to functional form misspecification and potential biases from omitted explanatory variables.

The findings reveal a positive impact of DT on firm IC quality, and this impact is more pronounced in firms when the CEO is female, young and possesses a higher level of education. Notably, the study also distinguishes between central and local state-owned enterprises (SOEs), highlighting that DT has a greater influence on IC quality in central SOEs, where CEOs often have higher political ranks and closer to government monitoring. Overall, the findings are robust and consist to alternative variable and other statistical methods.

Following are the significant implications for both academia and business. First, firms that effectively adopt DT to enhance IC not only gain a strategic advantage over competitors but also establish efficient risk management practices and a robust IC system. Second, better IC resulting from DT can enhance investor and stakeholder confidence. This is particularly important for publicly traded companies, where investors and analysts closely scrutinize the robustness of IC systems. Third, DT could result in cost savings over time, as automation and streamlined processes may reduce the need for manual efforts and resource-intensive tasks associated with IC.

The findings are contributed to the literature in multiple ways. It enhances our comprehension of the intricate DT-IC quality relationship, and provides valuable insights into the transformative impact of DT on organizational operations and risk management. It also introduces a novel perspective by investigating how CEOs personal traits intersect with DT to shape IC quality, contributing to upper echelons theory. Furthermore, it expands the discussions on firm ownership by considering the types of SOEs (central vs. local), in the DT-IC quality context.

India’s rapid economic growth has triggered a significant transformation in its logistics sector, fueled by comprehensive reforms and digital initiatives outlined in the National Logistics Policy. Smart warehouses, equipped with cutting-edge technologies such as IoT, AI and automation, have taken center stage in this evolution. They play a pivotal role in India’s digital journey, revolutionizing supply chains, reducing costs and boosting productivity. This AI-driven transformation, in alignment with the “Digital India” campaign, positions India as a global logistics leader poised for success in the industry 4.0 era. In this context, this study highlights the significance of smart warehouses and their enablers in the broader context of supply chain and logistics.

This paper utilized the ISM technique to suggest a multi-tiered model for smart warehouse ecosystem enablers in India. Enablers are also graphically categorized by their influence and dependence via MICMAC analysis.

The study not only identifies the 17 key enablers fostering a viable ecosystem for smart warehouses in India but also categorizes them as linkage, autonomous, dependent and independent enablers.

This research provides valuable insights for practitioners aiming to enhance technological infrastructure, reduce costs, minimize wastage and enhance productivity. Moreover, it addresses critical academic and research gaps contributing to the advancement of knowledge in this domain, thus paving the way forward for more research and learning in the field of smart warehouses.

The qualitative modeling is done by collecting experts' opinions using the ISM technique solicits substantial value to this research.

This paper aims to examine the direct impact of entrepreneurship education on university students' entrepreneurial intention and the moderating role of perceived governmental support in terms of financial support and policies and regulations.

The study collected data using questionnaires from students enrolled in compulsory entrepreneurship courses at three public Jordanian universities. Structural equation modeling was used to analyze 1,228 valid questionnaires and test the hypotheses.

The study revealed that entrepreneurship education positively and significantly affects students' entrepreneurial intentions. On the other hand, perceived governmental support in terms of financial support and policies and regulations has a nonsignificant moderating role in the relationship between entrepreneurship education and students' entrepreneurial intention.

This study enriches the literature with new evidence that entrepreneurship education has a positive, direct impact on students' intention to become entrepreneurs. It also contributes to the body of knowledge as the first to examine the role governments’ play besides encouraging entrepreneurship education through their education policies.

This study aims to empirically investigate the impact of oil prices, political instability and changes in stability on the bank diversification of the two types of banking systems in the Middle East and North African (MENA) countries.

The study uses bank diversification, stability measurement of probability of default and Zscore by adopting the generalised method of moment for the data between 2007 and 2021. The authors estimate short- and long-run dynamic panel analysis and a robustness test.

The findings reveal that Islamic banks are slightly lower in diversification and stability than conventional peers in the region. Diversification increases with a positive increase in GDP growth, law and order, political stability, bank size, asset quality, oil price, return on equity, profitability and change in banking asset-based stability. The authors found consistency in the two stability measurements in both short- and long-run situations.

Despite the change in banking stability and economic growth and oil prices improved diversification, banks in the region are not diversifying during the crisis period and political instability. Therefore, policymakers should improve mechanisms to monitor the crisis and political unrest to avoid the systemic risk that adversely affects the system through macro-financial linkages in the region.

This study uses change dual stability measurements and oil prices to predict MENA region bank diversification. The authors extended the banking literature by estimating the relationship between crisis periods, political and banking stability, oil prices and other institutional indicators of banking diversification. This study uncovers the effect of the global crisis period on banking diversification and the impact of banking stability changes and validates the models through robustness tests.

The aim of this study is to develop a new multidimensional index to measure export potential of textile firms by using firm-level data.

After a conceptual model, a structural equation model is developed with five dimensions and 27 observed variables based on resource-based view theory. The measurement model is solved by Linear Structural Relations (LISREL) with maximum likelihood algorithm by using data collected from 454 textile firms in Türkiye.

In this study, a new multidimensional index that measures export potential of textile firms is developed. With the proposed model, the export potential of textile firms can be calculated numerically with the five dimensions: Resources, Dynamism, Knowledge, Innovation and Sustainability. The comparison of the output of the proposed model with the control variable, firm’s actual export values, shows a significantly high success ratio of 90.76%.

The model is applicable for textile firms at different export levels, regions and sub-sectors. The Export Potential Index for Textile Industry model is verified by using Turkish textile industry data. The robustness of the model may be increased by verifying the model by using some other countries data. This model can be implemented to other industrial sectors with some modification of the dimensions and variables.

The proposed model will contribute to the firms by calculating their export potential in five dimensions with their own variables numerically. The model will help firms to develop strategies to increase their export potential and to the governmental and industrial organizations to develop incentives policies.

This paper fills the gap in the literature by proposing a multidimensional index that determines a firm’s export potential numerically by using firm-level data.

This study aims to evaluate the impact of sustainable strategic planning and management (SSPM) on sustainable performance (ecological, social and economic) among micro, small and medium enterprises (MSME) in the culinary sector in Southeast Asia (SEA).

This study used a quantitative approach surveying 300 halal-certified MSMS culinary businesses in the SEA region. Data were gathered using an online survey platform and analyzed using the partial least squares structural equation modeling technique.

Green management (GRM), digital transformation management (DTM) and Halal Business Management (HBM) exert a significant influence on SSPM. Furthermore, SSPM positively influences the sampled halal culinary MSMEs’ ecological, social and economic performances in SEA. The study also reveals that GRM, DTM and HBM positively influence Islamic ethics compliance and good governance.

This study sheds light on the often-ignored MSME business in the halal culinary sector in SEA. Examining sustainable and strategic planning synergy strengthens the development of a comprehensive framework for SSPM and could encourage more MSMEs to practice sustainable business operations. Moreover, the research also underlines the urgent need to harmonize halal standards in the SEA region. The approach would intensify multilateral support for economic growth and halal trade between SEA countries and beyond.

This study addresses the research gap in the strategic and sustainable management of halal culinary MSMEs within the developing and vast SEA economic region. This research is among the few, if not the first, that combines the GRM, DTM and HBM influence on SSPM and examines the impact on sustainable, ethical and good governance performances. The insights derived from this study contribute to formulating robust, sustainable policies, fostering avenues for sustainable initiatives and enhancing regional and global cooperation for a sustainable future.

Step length is a key factor for pedestrian dead reckoning (PDR), which affects positioning accuracy and reliability. Traditional methods are difficult to handle step length estimation of dynamic gait, which have larger error and are not adapted to real walking. This paper aims to propose a step length estimation method based on frequency domain feature analysis and gait recognition for PDR, which considers the effects of real-time gait.

The new step length estimation method transformed the acceleration of pedestrians from time domain to frequency domain, and gait characteristics of pedestrians were obtained and matched with different walking speeds.

Many experiments are conducted and compared with Weinberg and Kim models, and the results show that the average errors of the new method were improved by about 2 meters to 5 meters. It also shows that the proposed method has strong stability and device robustness and meets the accuracy requirements of positioning.

A sliding window strategy used in fast Fourier transform is proposed to implement frequency domain analysis of the acceleration, and a fast adaptive gait recognition mechanism is proposed to identify gait of pedestrians.