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This study aims to examine the relationships between organizational learning capabilities, open innovation and firm performance (FP) in the context of small and medium enterprises (SMEs) in the emerging economies.

Data collected from 384 manufacturing SMEs operating across the seven emirates of the UAE were statistically analyzed using SmartPLS 3 to examine the hypotheses of this study.

The results show that organizational learning capabilities positively influences both inbound and outbound dimensions of open innovation (OI). Inbound open innovation (IP) practice positively impacted both market effectiveness and profitability, while outbound open innovation (OP) practice only affected profitability. Findings further confirmed the mediating role of IP practice on the relationships of organizational learning capabilities with market effectiveness and profitability. In contrast, OP practice did not mediate the relationships of organizational learning capabilities with market effectiveness and profitability.

To the best of the authors knowledge, this is among the first study contributing to the extant innovation literature in terms of investigations into the significant and complex interrelations of organizational learning capabilities, OI and FP in a single study, demonstrating various theoretical implications in the context of manufacturing SMEs in emerging countries. Overall, the findings of this study confirmed that the owners/managers of the UAE’s manufacturing SMEs need to be acquainted with the need of creating a working environment fostering organizational learning processes and capabilities to enhance IP and OP activities, thereby improving their market effectiveness and profitability.

The United Nations' sustainable development goals (SDGs) put together a global framework in an attempt to address environmental, social and governance (ESG) concerns. Measuring a company’s contribution to the SDGs relies heavily on ESG reporting. This paper aims to examine the impact of ESG reporting on the corporate profitability of listed companies in Thailand over the period of 2019–2021.

Using 147 listed firms in the ESG group, content analysis was used to quantify the ESG reporting (within 11 themes), while corporate profitability was measured by return on asset and return on equity. Descriptive analysis, correlation matrix and panel regression are used to analyze the data of this study.

Consistent with the legitimacy, stakeholder and signaling theories, the authors found a statistically significant and positive impact of ESG reporting on corporate profitability in Thailand.

The findings highlight the importance of incorporating ESG considerations into companies’ reporting and decision-making processes, as these can enhance firm profitability and performance, attract stakeholders, improve their competitive advantage and step toward sustainability.

This study aims to examine the influence of ownership structure and board characteristics on the performance of Indonesian Islamic rural banks (IRB) using the system generalized method of moment model.

This research uses Indonesian IRB unbalanced annual panel data from 2016 to 2022. IRB performance is measured by return on assets (ROA), return on equity (ROE) and nonperforming financing (NPF). The ownership structure is represented by controlling shareholders, ownership of the board of directors (BD) and ownership of the board of commissioners (BC). Meanwhile, board characteristics are represented by the size of the BC, the proportion of female board directors and female president directors.

The results show that the ownership structure and board characteristics play an important role in improving the IRB’s performance. Technically, the results show that the size of the BC and the ownership of the BD increase all IRB performance measures. Female president directors and controlling shareholders improve IRB’s performance as measured by ROA and ROE. Women’s boards of directors improve IRB performance as measured by NPF. Meanwhile, the ownership of the BC does not show its effect on all IRB performance measures.

This study fills a literature gap on the influence of ownership structure and board characteristics on IRB Indonesia’s performance. In addition, it adds understanding and insight for Islamic bank regulators, management and IRB depositors in Indonesia.

To the best of the authors’ knowledge, this study is one of the first to provide an empirical survey on the influence of controlling shareholders and board characteristics on IRB performance, particularly in Indonesia.

The purpose of the current exploratory study is to provide a more comprehensive understanding of the essence of impulse buying and postulate the drivers behind it for Azerbaijani consumers who practice Islam. Though there are numerous factors that impact impulsive buyer tendencies, current empirical research focuses on demographic indicators and persona characteristics that influence impulsive buying, which are considered as internal factors.

The current research is exploratory and uses the quantitative research method. A total of 442 responses were collected from the surveys between April 6th and May 6th, 2022, with random sampling. The collected data was analyzed with the SPSS Statistics software and the SPSS AMOS 26 version. Cronbach’s alpha was calculated to measure the reliability of the scales. The construct validity of the questionnaire was measured with confirmatory factor analysis (CFA). The correlation among variables was measured by calculating the Pearson correlation coefficient. The structural equation model was run to determine the structural relationship between latent variables and test the proposed hypotheses.

According to the regression weights, all the latent variables, excluding materialism, have a statistically significant impact on the impulsive buying tendency. Hedonic motivation and self-esteem have a positive relationship with hedonic buying tendency, while self-control is inversely related. In terms of the impact of demographic indicators on impulsive buying tendency, for Azerbaijani consumers, impulsive buying behavior, differs based on age, education and marital status. However, contrary to previous research, impulsive buying behavior does not change based on income level. However, based on the squared multiple correlations, the predictive power of the model is 24%.

The current study measured the general impulsive behavior of the local population. Analyzing impulsive buyer behavior toward particular products or product categories might lead to different results. Therefore, other researchers can focus on studying impulsive buyer behavior toward different product categories.

Understanding consumer buyer behavior, including impulsive buyer behavior, is among extensively researched subjects. However, studies examining the state of impulsive buyer behavior in Azerbaijan and, most importantly, measuring the impact of the numerous factors behind it are lacking. The majority of the available empirical research in Azerbaijan analyzes consumer behavior from a different perspective. However, none of them approaches it from the perspective of analyzing and measuring impulsive buying. Hence, understanding the motivations behind impulse buying behavior can benefit both businesses and consumers, though for contrasting reasons. The former can use the data to determine the triggers for impulse buying, while the latter can be more conscious of their purchasing behavior to avoid unfavorable financial consequences. The primary implication for businesses is that they will be equipped with empirical evidence on which factors and to what extent they influence consumer buying behavior. Subsequently, they can use it while developing marketing and customer relationship strategies.

There are countless studies focused on defining and measuring impulsive buyer behavior; however, there is a gap in measuring the impulsive buyer behavior of Muslim consumers and, most importantly, understanding the underlying factors. In addition, empiric research examining the state of impulsive buyer behavior of local Muslim consumers in Azerbaijan and, most importantly, measuring the impact of numerous factors behind it is absent. The majority of the available empirical research in Azerbaijan analyzes consumer behavior from a different perspective. However, none of them approaches it from the perspective of analyzing and measuring impulsive buying. The current study analyzes the impact of consumers’ hedonic motivations, materialist values, ability to retain self-control and psychological state on their impulse buying tendency.

This paper reviews recent research on the expected economic effects of developing artificial intelligence (AI) through a survey of the latest publications, in particular papers and reports issued by academics, consulting companies and think tanks.

Our paper represents a point of view on AI and its impact on the global economy. It represents a descriptive analysis of the AI phenomenon.

AI represents a driver of productivity and economic growth. It can increase efficiency and significantly improve the decision-making process by analyzing large amounts of data, yet at the same time it creates equally serious risks of job market polarization, rising inequality, structural unemployment and the emergence of new undesirable industrial structures.

This paper presents itself as a building block for further research by introducing the two main factors in the production function (Cobb-Douglas): labor and capital. Indeed, Zeira (1998) and Aghion, Jones and Jones (2017) suggested that AI can stimulate growth by replacing labor, which is a limited resource, with capital, an unlimited resource, both for the production of goods, services and ideas.

Our study contributes to the previous literature and presents a descriptive analysis of the impact of AI on technological development, economic growth and employment.

Talent management research today is increasing as organizational requirements attempt to meet the challenges of effectively managing talent to achieve organizations’ strategic agendas. However, in learning organizations specifically, investigations of talent management practices are limited, with this study exploring the role of talent management practices in employee satisfaction and organizational performance in Pakistan.

The study was conducted in various universities (public and private) in Pakistan using a quantitative approach. Cross-sectional data are collected through a questionnaire, with analysis and conclusions based on completed questionnaires from 403 respondents.

The study’s findings from the analysis by structural equation modeling (SEM) emphasize the positive and significant effects of most talent management practices (i.e. talent identification, talent development, talent culture and talent retention) on employee satisfaction and organizational performance (talent attraction is the exception). Employee satisfaction positively and significantly affects organizational performance and is found to have a mediating effect, bridging the relationships of most talent management practices (talent identification, talent development, talent culture and talent retention) with organizational performance.

The study’s findings support human resource professionals, academics and policymakers in managing talent practices to enhance organizational performance. The findings assist in developing core skills and talent-related competencies to achieve organizational goals and success.

The study fills the research gaps by developing a framework of talent management practices for employee satisfaction and organizational performance in learning organizations, which warrants further consideration.

The aim of this study is to determine the fracture behavior of wool felt and fabric based epoxy composites and their responses to electromagnetic waves.

Notched and unnotched tensile tests of composites made of wool only and hybridized with a glass fiber layer were carried out, and fracture behavior and toughness at macro scale were determined. They were exposed to electromagnetic waves between 8 and 18 GHz frequencies using two horn antennas.

The keratin and lignin layer on the surface of the wool felt caused lower values to be obtained compared to the mechanical values given by pure epoxy. However, the use of wool felt in the symmetry layer of the laminated composite material provided higher mechanical values than the composite with glass fiber in the symmetry layer due to the mechanical interlocking it created. The use of wool in fabric form resulted in an increase in the modulus of elasticity, but no change in fracture toughness was observed. As a result of the electromagnetic analysis, it was also seen in the electromagnetic analysis that the transmittance of the materials was high, and the reflectance was low throughout the applied frequency range. Hence, it was concluded that all of the manufactured materials could be used as radome material over a wide band.

Sheep wool is an easy-to-supply and low-cost material. In this paper, it is presented that sheep wool can be evaluated as a biocomposite material and used for radome applications.

The combined evaluation of felt and fabric forms of a natural and inexpensive reinforcing element such as sheep wool and the combined evaluation of fracture mechanics and electromagnetic absorption properties will contribute to the evaluation of biocomposites in aviation.

Due to the coronavirus 2019 (COVID-19) pandemic, primary and secondary schools worldwide are deploying online teaching/learning practices, fostering and thus innovation practices. The purpose of this study is to determine the degree to which practices reflecting educational innovation are implemented in the Greek public primary and secondary schools operating under conditions characterized by the COVID-19 pandemic. Determining the relationship among these educational innovation practices is also an aim of the present study.

A research study was conducted in the Greek public primary and secondary schools. 522 teachers fully completed a structured questionnaire. Descriptive statistics, exploratory factor analysis and structural equation modeling were applied to analyze the data.

The findings reveal that administration-related innovation practices, teaching-related innovation practices and online teaching/learning practices are implemented to some extent in primary and secondary schools in Greece, but there is still scope for continued development. The online teaching/learning practices set the foundations for further developing a culture of fully adopting other educational innovation practices in these schools to improve education.

Limited research concerning educational innovation practices has focused on primary and secondary schools. The need for more studies on teaching and learning innovations that have resulted from the COVID-19 crisis is highlighted by the literature. The results of this study support the fact that online teaching/learning implemented in primary and secondary schools is positively associated with administration-related and teaching-related innovation practices, concluding that this forced change in the educational process can act as a catalyst for more changes and innovative actions.

Accounting information systems are mainly rule-based, and data are usually available and well-structured. However, many accounting systems are yet to catch up with current technological developments. Thus, artificial intelligence (AI) in financial accounting is often applied only in pilot projects. Using AI-based forecasts in accounting enables proactive management and detailed analysis. However, thus far, there is little knowledge about which prediction models have already been evaluated for accounting problems. Given this lack of research, our study aims to summarize existing findings on how AI is used for forecasting purposes in financial accounting. Therefore, the authors aim to provide a comprehensive overview and agenda for future researchers to gain more generalizable knowledge.

The authors identify existing research on AI-based forecasting in financial accounting by conducting a systematic literature review. For this purpose, the authors used Scopus and Web of Science as scientific databases. The data collection resulted in a final sample size of 47 studies. These studies were analyzed regarding their forecasting purpose, sample size, period and applied machine learning algorithms.

The authors identified three application areas and presented details regarding the accuracy and AI methods used. Our findings show that sociotechnical and generalizable knowledge is still missing. Therefore, the authors also develop an open research agenda that future researchers can address to enable the more frequent and efficient use of AI-based forecasts in financial accounting.

Owing to the rapid development of AI algorithms, our results can only provide an overview of the current state of research. Therefore, it is likely that new AI algorithms will be applied, which have not yet been covered in existing research. However, interested researchers can use our findings and future research agenda to develop this field further.

Given the high relevance of AI in financial accounting, our results have several implications and potential benefits for practitioners. First, the authors provide an overview of AI algorithms used in different accounting use cases. Based on this overview, companies can evaluate the AI algorithms that are most suitable for their practical needs. Second, practitioners can use our results as a benchmark of what prediction accuracy is achievable and should strive for. Finally, our study identified several blind spots in the research, such as ensuring employee acceptance of machine learning algorithms in companies. However, companies should consider this to implement AI in financial accounting successfully.

To the best of our knowledge, no study has yet been conducted that provided a comprehensive overview of AI-based forecasting in financial accounting. Given the high potential of AI in accounting, the authors aimed to bridge this research gap. Moreover, our cross-application view provides general insights into the superiority of specific algorithms.

It is widely accepted that one criterion for determining if a construction project is successful is whether it is completed within the expected budget. There have been advancements in the management of building projects throughout time but cost overruns remain a key concern in the construction sector internationally, particularly in emerging economies such as Ghana. This study aims to answer the question, “What are the critical success factors (CSFs) that can assist reduce cost overruns in public sector infrastructure projects in the Ghanaian construction industry?”

This study used a quantitative survey method. The questionnaire was pre-tested by interviewing 15 contractors to ascertain the validity of the content. Factor analysis and multiple regression were adopted to analyze the data.

This study discovered that the critical factors that can reduce cost overruns in construction projects in Ghana are directly linked to five themes: early contractor involvement in the project planning stage, adequate funding, good project team relations, competent managers/supervisors and project participant incentives/bonuses. This study identifies indestructible, empirically measurable important success criteria for reducing cost overruns in public building projects in Ghana.

When well thought through from the project initiation stage to completion, these critical successes can also be used to deal with damaging economic effects such as allocative inefficiency of scarce resources, further delays, contractual disputes, claims and litigation, project failure and total abandonment.

The uniqueness of this research resides in the fact that it is, to the best of the authors’ knowledge, a first-of-its-kind investigation of the CSFs for reducing cost overruns in public building projects in developing countries.