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This study aims to investigate the combined use of System Dynamics (SD) applications in Enterprise Engineering (EE) research and practice. SD application in EE is becoming widely accepted as a tool to support decision-making processes and for capturing relationships within enterprises.

A systematic literature review (SLR) is conducted using a standard SLR method to provide a comprehensive review of existing literature. The search was conducted on ten platforms identifying 30 publications which were analysed through the use and development of a codebook.

The SLR showed that 90% of the result set consisted of peer-reviewed academic conferences and journal papers. The SLR identified a highly dispersed author set of 83 authors. Amongst these authors, Vinay Kulkarni was an active author who has co-authored up to four publications in this research area. The analysis further revealed that the combined use of SD applications and EE is an emerging research area that still needs to develop in maturity. While all phases of EE have received attention, the current research work is more focused on the design phase. The important gap between model development and implementation is identified.

The study elucidates the existing status of interdisciplinary research combining techniques from the SD and EE disciplines, suggesting future research topics that combine the strengths of these existing disciplines.

In recent times, the field of corporate intelligence has gained substantial prominence, employing advanced data analysis techniques to yield pivotal insights for instantaneous strategic and tactical decision-making. Expanding beyond rudimentary post observation and analysis, social media analytics unfolds a comprehensive exploration of diverse data streams encompassing social media platforms and blogs, thereby facilitating an all-encompassing understanding of the dynamic social customer landscape. During an extensive evaluation of social media presence, various indicators such as popularity, impressions, user engagement, content flow, and brand references undergo meticulous scrutiny. Invaluable intelligence lies within user-generated data stemming from social media platforms, encompassing valuable customer perspectives, feedback, and recommendations that have the potential to revolutionize numerous operational facets, including supply chain management. Despite its intrinsic worth, the actual business value of social media data is frequently overshadowed due to the pervasive abundance of content saturating the digital realm. In response to this concern, the present study introduces a cutting-edge system known as the Enterprise Just-in-time Decision Support System (EJDSS).

Leveraging deep learning techniques and advanced analytics of social media data, the EJDSS aims to propel business operations forward. Specifically tailored to the domain of marketing, the framework delineates a practical methodology for extracting invaluable insights from the vast expanse of social data. This scholarly work offers a comprehensive overview of fundamental principles, pertinent challenges, functional aspects, and significant advancements in the realm of extensive social data analysis. Moreover, it presents compelling real-world scenarios that vividly illustrate the tangible advantages companies stand to gain by incorporating social data analytics into their decision-making processes and capitalizing on emerging investment prospects.

To substantiate the efficacy of the EJDSS, a detailed case study centered around reverse logistics resource recycling is presented, accompanied by experimental findings that underscore the system’s exceptional performance. The study showcases remarkable precision, robustness, F1 score, and variance statistics, attaining impressive figures of 83.62%, 78.44%, 83.67%, and 3.79%, respectively.

This scholarly work offers a comprehensive overview of fundamental principles, pertinent challenges, functional aspects, and significant advancements in the realm of extensive social data analysis. Moreover, it presents compelling real-world scenarios that vividly illustrate the tangible advantages companies stand to gain by incorporating social data analytics into their decision-making processes and capitalizing on emerging investment prospects.

This paper applies an original construct of “subjective risk intelligence (SRI)” to the small business context. By leveraging on its multidimensionality, it aims to shed light on the existing ambiguities in the analysis of the relationship between the entrepreneurial attitude towards risk evaluation and firms’ financial stability.

The empirical investigation refers to the Italian context, where an ad hoc survey has been administered to a sample of small businesses. Based on both a linear and a semiparametric regression, results show a significant relationship between SRI and firm’s financial structure, and that such relationship is basically nonlinear.

Evidence shows that entrepreneurs with a high level of risk intelligence run highly leveraged firms. Moreover, in the light of the non-linearity of such relationship, higher levels of risk intelligence are associated with a greater capacity of the entrepreneur to govern the financial balance of the enterprise only up to a certain threshold. Over this threshold, risk intelligence generates overconfidence leading the entrepreneur to a reckless behaviour in taking financial risks.

From a theoretical point of view, the paper contributes to the literature by shedding lights on the complexity of the relationship between risk intelligence and small businesses. From a policy point of view, findings suggest that, to train new entrepreneurs, the educational system aims should focus on the development of two specific “soft skills”: the ability to manage emotions and the ability to glimpse opportunities even in uncertain situations.

Supply chains need to be made viable in this volatile and competitive market, which could be possible through digitalization. This study is an attempt to explore the role of Industry 4.0, smart supply chain, supply chain agility and supply chain resilience on sustainable business performance from the lens of natural resource-based view.

The study tests the proposed model using a covariance-based structural equation modelling and further investigates the ranking of each construct using the artificial neural networks approach in AMOS and SPSS respectively. A total of 234 respondents selected using purposive sampling aided in capturing the industry practices across supply chains in the UK. The full collinearity test was carried out to study the common method bias and the content validity was carried out using the item content validity index and scale content validity index. The convergent and discriminant validity of the constructs and mediation study was carried out in SPSS and AMOS V.23.

The results are overtly inferring the significant impact of Industry 4.0 practices on creating smart and ultimately sustainable supply chains. A partial relationship is established between Industry 4.0 and supply chain agility through a smart supply chain. This work empirically reinstates the combined significance of green practices, Industry 4.0, smart supply chain, supply chain agility and supply chain resilience on sustainable business value. The study also uses the ANN approach to determine the relative importance of each significant variable found in SEM analysis. ANN determines the ranking among the significant variables, i.e. supply chain resilience > green practices > Industry 4.0> smart supply chain > supply chain agility presented in descending order.

This study is a novel attempt to establish the role of digitalization in SCs for attaining sustainable business value, providing empirical support to the mediating role of supply chain agility, supply chain resilience and smart supply chain and manifests a significant integrated framework. This work reinforces the integrated model that combines all the constructs dealt with in silos so far in prior literature.

The current research study aims to examine the application feasibility and impact of artificial intelligence (AI) among higher educational institutions (HEIs) in talent acquisitions (TA).

A systematic sampling method was adopted to collect the responses from the 385 staff working across the various levels of management in HEIs in metropolitan cities in India. JAMOVI & SmartPLS 4 were applied to validate the hypothesis by performing the simple percentage analysis and structural equation modelling. The demographic and construct variables considered were adoption, actual usage, perceived usefulness, perceived ease of use and talent management.

The key indicators of perceived usefulness are productivity, perceived ease of use, adaptability, candidate experience with the adoption of AI, frequency in decision-making in its actual usage and career path of development in the HEIs. These are the most influential items impacting the application of AI in TA.

AI has the potential to revolutionize TA in HEIs in the form of enhanced efficiency, improved candidate experience, more objective hiring decisions, talent analytics and risk automation. However, they facilitate resume screening, candidate sourcing, applicant tracking, interviewing and predictive analytics for attrition.

The external business environment of the organization is always changing at a rapid pace. For a firm to adapt to changing client requirements, it must implement the right business procedures and strategies. To improve competitive advantage, this study investigates the roles that supply chain partnerships, cross-functional integration, responsiveness and resilience play in achieving competitive advantages in Palestine.

Industrial institutions in Palestine constitute the study population. Data are collected by distributing surveys via Google Forms linked to manufacturers in industries such as the Leather and shoe Industry, metal industries, chemical industries, construction industries, textile industries, stone and marble industries, pharmaceutical industry, veterinary industry, food industry, plastic industry, paper industry, major advantages and disadvantages. The SEM-PLS approach is used to analyze the data.

The findings demonstrate that supply chain responsiveness, resilience and cooperation are all improved by cross-functional integration in inventory data integration and immediate operation. Supply chain partnerships improve the supply chain’s responsiveness, resilience and competitive advantage by involving partners in work teams and exchanging best practices. The enhancement of supply chain resilience and competitive advantage is influenced by the company’s capacity to act promptly in response to variations in demands.

This paper faces some limitations and it can be drawn as follows: To enhance supply chain risk management, the study continues to concentrate on manufacturing organizations that have internal integration. It also emphasizes the necessity of supply chain integration, which establishes direct connections with outside partners.

The findings of this study suggest some policy implications, as follows: To provide the manufacturing sector with a competitive edge, operations supervisors must be able to track and assess processes to ensure they are meeting demand. Firms that possess the ability to adjust to novel procedures or advancements in technology gain a competitive edge by guaranteeing consistent and high-quality delivery of products.

By implementing IT integration, this study theoretically and practically advances the understanding of the resource-based view of competitive advantages. This study focuses on providing insights into the nature of the relationship between supply chain partnership, cross-functional integration, responsiveness and flexibility and competitive advantages in the manufacturing sector in the Palestinian market.

We examined skill building techniques and changes over the course of a semester with pre and post-test data collection after implementing experiential learning assignments.

The Schutte Self-Report Emotional Intelligence Test (SEEIT) was used to measure emotional intelligence among students who interned for a 16-week period.

We found a significant difference using a paired samples t-test in SSEIT scores between the pre (M = 126.6, SD = 4.3) and the post-internship (M = 133.8, SD = 5.7) scores. t(5) = -5.61, p = 0.002. Students had an overall increase in mean scores over the course of one semester.

This was a pilot study that we completed to determine applicability of internship and increasing emotional intelligence. Overall, we saw an increase in EI in pre and post-test comparisons. This was a pilot study, so more research is needed on this topic.

Students were placed in situations during the internship process to help facilitate real world problems and were required to apply applicable textbook knowledge, develop theory-based activities and report their findings. Students worked with various age groups and learned how to work with a variety of populations including faculty, teachers, children and parents on a regular basis and this process contributed to their experience and potentially increased emotional intelligence over a 16-week period.

This research addresses the importance of emotional intelligence (EI) in career readiness and its role in potentially mitigating burnout in psychological professions.

This is important to those in the field of psychology and child development and family studies because it addresses concerns with the shortage of skilled and prepared workers.

This study aims to identify necessary conditions for innovative entrepreneurship in cities and determine similarities in entrepreneurial configurations among them.

The authors assessed the necessary conditions for various levels of entrepreneurial output and categorized cities based on similar patterns by applying necessary condition analysis (NCA) and cluster analysis in a sample comprised of 101 cities from the entrepreneurial cities index, representing a diverse range of urban environments in Brazil. A comprehensive data set, including both traditional indicators from official Bureau of statistics and nontraditional indicators from new platforms of science, technology and innovation intelligence, was compiled for analysis.

Bureaucratic complexity, urban conditions, transport infrastructure, economic development, access to financial capital, secondary education, entrepreneurial intention, support organizations and innovation inputs were identified as necessary for innovative entrepreneurship. Varying levels of these conditions were found to be required for different entrepreneurial outputs.

The static nature of the data limits understanding of dynamic interactions among dimensions and their impact on entrepreneurial city performance.

Policymakers can use the findings to craft tailored support policies, leveraging the relationship between city-level taxonomy and direct outputs of innovative entrepreneurial ecosystems (EEs).

The taxonomy and nontraditional indicators sheds light on the broader societal benefits of vibrant EEs, emphasizing their role in driving socioeconomic development.

The cluster analysis combined with NCA’s bottleneck analysis is an original endeavor which made it possible to identify performance benchmarks for Brazilian cities, according to common characteristics, as well as the required levels of each condition by each city group to achieve innovative entrepreneurial outputs.

Our understanding of the strategies that lead to the success of start-ups when they scale-up is limited when it occurs at the regional periphery. The main purpose of this study is to explore the specific strategies that start-ups employ to scale-up, specifically in contexts with high resource constraints at the regional periphery.

Analyzing the data from personal in-depth interviews with engineering and science start-up founders in peripheral regions of upstate New York USA bordering the Canadian Ontario, we explored a combination of internal and external strategies that start-ups employed to scale-up.

The study found that start-ups prioritize building internal scaling capacity in their human capital, organizational structure, scalable business model, finance and business ownership. To foster the scaling process further, start-ups develop new effective external strategies that target the business environment.

Policymakers and regional governments can use our research to develop more effective industrial policies for supporting start-ups’ growth and subsiding strategic industry clusters for rebooting new competition policy, which is a current debate in many industrialized economies including the US. This targeted regional industrial policy is specially needed when scaling-up at the regional periphery.

Our study is specially need it when scaling-up at the regional periphery and with limited resources.

This study enriches our understanding of the growth of start-ups and small ventures by providing context-based insights into how firms build the capacity to scale-up in highly challenging and uncertain business environments in a peripheral bordering region between the USA and Canada. It also offers useful managerial and policy implications.

The purpose of this research is to determine how supply chain management (SCM) might be less affected by COVID-19 by using innovative technologies such as the Internet of Things (IoT), eco-friendly corporate practices and other digital advancements. It strongly emphasizes the use of technology to improve supply networks’ and Omani firms’ performance.

Using a mixed-methods research strategy, this study integrates both qualitative and quantitative approaches. It involves a survey and interviews with supply chain and IT managers from various industries in Oman to gather data and evaluate the impact of technology on SCM.

This study finds that IoT capabilities, smart technologies (STs) and green practices significantly mitigate COVID-19 impacts on SCM. The performance of the supply chain and the business are both improved by these technologies’ positive effects on integrating various supply chain elements, such as suppliers, internal processes and customer relations.

The main constraint of this study is its concentration on businesses in Oman, potentially restricting the applicability of its findings to broader contexts. Future studies could investigate similar frameworks across various geographic and industry settings.

The findings suggest that incorporating STs into SCM is crucial for enhancing operational efficiency and resilience against disruptions such as COVID-19. This offers valuable insights for managers and policymakers in adopting technology-driven strategies for SCM.

This study highlights the significant role of technology in sustaining supply chains during pandemics, thereby supporting economic stability and societal well-being. It underscores the importance of technological advancements in maintaining supply chain continuity in challenging times.

By empirically examining the effect of emerging technologies on enhancing SCM in the context of the COVID-19 pandemic, specifically in the Oman market, this research makes a unique contribution to the body of knowledge.