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This paper investigates the critical role of entrepreneurial computer engineers in driving innovation and accelerating economic growth through the creation of technological businesses. By analyzing their contributions to innovation ecosystems, this paper aims to provide insights into the unique impact these engineers have within the entrepreneurial landscape. The study underscores the need for tailored policies that harness the potential of entrepreneurial computer engineers to achieve macroeconomic objectives amidst ever-changing economic complexities.

To examine the significance of entrepreneurial computer engineers within the innovation ecosystem, this study uses bibliometric analysis with VOS viewer to explore existing literature on engineering and entrepreneurship.

Using bibliometric analysis, the authors reveal a network of interconnected skills such as risk-taking, self-determination and project-based learning, evident in the co-occurrence of keywords.

The entrepreneurial computer engineer plays a central role in steering technical innovation and fostering sustainable economic growth within the field of computer engineering. The authors pinpoint specific skills and strategic pathways crucial for the growth and development of entrepreneurial computer engineers, emphasizing the unique contributions and challenges within this domain.

This paper addresses the under-researched topic of engineering entrepreneurship in relation to innovation, aiming to provide knowledge and insights into the intersection of engineering and entrepreneurship. By examining this nexus, the paper contributes to filling the gap in existing literature and offers valuable perspectives for both academia and industry.

New product development (NPD) is necessary for business sustenance and customer satisfaction. Six Sigma and Design for Lean Six Sigma (DLSS) efficiently employ the repetitive stages for NPD, leading to quality performance and profitability. This study aims to map the quality performance through NPD attributes through the Lean methodology.

The data on NPD were collected from 267 respondents from manufacturing companies to map the relationship between Six Sigma and DLSS for NPD. Confirmatory factor analysis was employed to confirm model fit, while structural equation modeling was employed to analyze the empirical data for framework testing. The study included nine variables and fourteen hypotheses identified from the literature.

The statistical results of this study show that NPD attributes such as innovation, marketing, organization, customer, product and technology positively influence the Lean Six Sigma structured improvement process (LSSSIP) and DLSS. Moreover, integrating these attributes in Lean planning enhance quality performance. This empirical investigation's findings indicate that ten of the 14 hypotheses were supported, giving the study a strong foundation.

The data collection was limited to northern India; therefore, the results may not be generalizable to other areas of the world.

NPD involves handling technical issues and factors such as cost, operational bottlenecks, economic changes, competitors' strategy and company policy. This study helps understand the various NPD parameters and their relationship to Lean, which enables an effective NPD implementation strategy.

The current philosophy of NPD calls for a concurrent engineering approach; therefore, the entire organization must be part of this process. This study uses the holistic framework by optimizing NPD with Lean Six Sigma (LSS) principles. The study is unique in that, to date, research does not integrate NPD attributes with the objectives of LSS to develop an efficient NPD implementation strategy.

The aim of this paper is to investigate the development of the quality movement in Sweden since the mid-20th century. The authors are convinced that a summary of the Swedish quality journey so far will offer important lessons for further quality improvements in Sweden and elsewhere.

The authors study how the quality movement has been included in the industrial agenda and how it has been adopted in student curricula and in research. The authors have a focus on how business leaders have learnt, adopted, adapted and innovated with respect to quality development. often in collaboration with academia.

Although the quality movement has fit well with the Swedish culture and that successful corporate leaders have successfully used the specific cultural characteristics there is still a lot to be learnt with respect to the public sector, where the ideas from the quality movement have problem to overcome institutional barriers.

The study is limited to the Swedish context.

There is a serious need to revitalize the public sector by getting leaders and politicians to understand the need for systematic quality improvement.

If future Swedish achievements with respect to healthcare and other social welfare elements are to once again become world-class, the public sector needs to be open-minded and collaborate with the industrial sector and academia to find cost-effective strategies for making quality improvements. However, the private sector must also be alert not to be overtaken by some highly active Asian countries.

Swedish large companies have been very successful in applying quality leadership – however, in the public sector, this has not been the case. Suggestions for improvement are made.

Smart technologies in today's Internet of Things (IoT) era cover a wide spectrum, including smart identification technologies, robotics, virtual reality (VR), augmented reality (AR), wearable technologies, cloud, artificial intelligence (AI), machine learning (ML), and blockchain. This study aims at revealing the role and importance of smart technologies in service systems which encompass a wide spectrum of sectors. The scope of this study covers the main applications in transportation and logistics, retail, health care, hospitality, and financial services sectors. A broad overview is provided for the potentials and benefits of smart technology utilization in these service areas, and future research agenda is suggested. Findings reveal that simultaneous use of smart technologies bring tremendous opportunities in terms of efficiencies and automation, reshaping and transforming operations and business processes of the service enterprises. The study highlights that although core proven applications of smart technologies are in manufacturing, generally lagging service sector applications promise radical productivity improvements, performance enhancements, and increased service quality. By radically upgrading the customer satisfaction to entirely new levels and providing unique customer experiences, smart technologies lead to bottom line profitability improvements as well competitiveness.

This study examines the relationship between servant leadership and its dimensions on firm performance, with big data playing the role of a mediator.

Survey responses used for analysis in this study have been taken from business managers associated reputed private sector organizations in India. A conceptual model is proposed grounded to the Conservation of Resource Theory (COR). Structural equation modeling has been used to test the proposed model.

Servant leadership significantly relates to firm performance, whereby Big Data is seen to play the role of a mediator. The results also indicate that none of the dimensions of servant leadership independently affect firm performance.

The study adds to extant research by examining the mediating mechanism of Big Data in servant leadership and firm performance. It also suggests that each dimension of servant leadership gets reflected in overall servant leadership. Here it is important to note that Big Data analytics partially mediate the effectiveness of servant leadership.

The phenomenon of delayed payment to contractors, particularly in the construction industry, is a vital one and has implications for the health of economies of both developing and developed countries. However, the knowledge of this phenomenon seems patchy and scattered. This paper aims to provide a comprehensive overview of the knowledge on the subject matter with directions for future research.

A systematic literature review coupled with a scientometric analysis was used to identify the main strands of delayed payment to contractor research as a basis for qualitative analysis and directions for future investigations.

Current trends of delayed payment to contractor research are categorised into five broad themes, namely: causes, effects, mitigation measures, ethical and law and regulatory issues. On the basis of these themes, directions for future research are proffered.

To the best of the authors knowledge, this is the first attempt at providing a comprehensive and an integrated knowledge on delayed payment to contractor research with pointers for further investigation and policy directions.

This paper aims to examine the relationship between board structure and risk-taking, exploring how this association is influenced by advanced technologies in the banking sector.

This study uses a panel sample of 22 Pakistani banks from 2011 to 2018. To test the authors’ hypothesis, the authors use regression analysis with two-way cluster robust standard errors. Further, the authors also check the robustness of the authors’ findings using alternate proxies of board structure and bank risk-taking behavior. To address endogeneity concerns, the authors use the two-stage least square technique.

In the era of the Fourth Industrial Revolution, Pakistani banks’ digitalization is modeled by the presence of Temenos-T24/Oracle as their core banking system (software providing end-to-end operational integration). Its interactional effect with corporate governance is evaluated to implicate informed risk-taking by the board as a result of improved information access and analysis. The authors find that board size has a positive association with risk-taking, and the use of modern technology reshapes this association in the banking sector.

The contribution of this paper is twofold. First, the impact of board structure on bank risk-taking has not been extensively researched in Pakistan – a highly volatile and unpredictable economy. Second, the evaluation of the role of technology on bank risk is being researched for the very first time – a uniqueness of this paper.

Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose of this study is to concentrate on the energy sector and explore the trading volume prediction issue for the thermal coal futures traded in Zhengzhou Commodity Exchange in China with daily data spanning January 2016–December 2020.

The nonlinear autoregressive neural network is adopted for this purpose and prediction performance is examined based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays and ratios for splitting the trading volume series into training, validation and testing phases.

A relatively simple model setting is arrived at that leads to predictions of good accuracy and stabilities and maintains small prediction errors up to the 99.273th quantile of the observed trading volume.

The results could, on one hand, serve as standalone technical trading volume predictions. They could, on the other hand, be combined with different (fundamental) prediction results for forming perspectives of trading trends and carrying out policy analysis.

The purpose of this paper is to find out which research topics and methods in information science (IS) articles are used in other disciplines as indicated by citations.

The study analyzes citations to articles in IS published in 31 scholarly IS journals in 2015. The study employs content analysis of articles published in 2015 receiving citations from publication venues representing IS and other disciplines in the citation window 2015–2021. The unit of analysis is the article-citing discipline pair. The data set consists of 1178 IS articles cited altogether 25 K times through 5 K publication venues. Each citation is seen as a contribution to the citing document’s discipline by the cited article, which represents some IS subareas and methodologies, and the author team's disciplinary composition, which is inferred from the authors’ affiliations.

The results show that the citation profiles of disciplines vary depending on research topics, methods and author disciplines. Disciplines external to IS are typically cited in IS articles authored by scholars with the same background. Thus, the export of ideas from IS to other disciplines is evidently smaller than the earlier findings claim. IS should not be credited for contributions by other disciplines published in IS literature.

This study is the first to analyze which research topics and methods in the articles of IS are of use in other disciplines as indicated by citations.

Even though the end-user satisfaction construct has gained prominence as a surrogate measure of information systems performance assessment, it has received scant formal treatment and empirical examination in the data analytics systems field. In this respect, this study aims to examine the vital role of user satisfaction as a proxy measure of data analytics system performance in the financial engineering context.

This study empirically validated the proposed model using primary quantitative data obtained from financial managers, engineers and analysts who are working at Jordanian financial institutions. The quantitative data were tested using partial least squares-based structural equation modeling.

The quantitative data analysis results identified that technology quality, information quality, knowledge quality and decision quality are key factors that enhance user satisfaction in a data analytics environment with an explained variance of around 69%.

This empirical research has contributed to the discourse regarding the pivotal role of user satisfaction in data analytics performance in the financial engineering context of developing countries such as Jordan, which lays a firm foundation for future research.