Entrepreneurship, Innovation, and Technology

This study delves into the role of women’s entrepreneurship in Ireland, employing a demand-constraint-choice framework to analyze motivations and factors influencing entrepreneurial ventures. Qualitative research using semi-structured interviews provides a comparative analysis of motivations, perceptions, and experiences across Ireland’s cultural and economic contexts. Findings highlight the influence of cultural factors on entrepreneurial choices, the importance of government support, and the need for proactive intervention for gender-inclusive economic development. The study’s insights extend beyond Ireland, contributing to global strategies for empowering women in entrepreneurship, making it valuable for policymakers, researchers, and entrepreneurs seeking to understand women’s entrepreneurial leadership.

This study delves into Lebanon’s women entrepreneurs and their pivotal role in economic advancement and innovation amidst gender inequality and social discrepancies. It examines these women’s multifaceted challenges and opportunities, employing the Demand-Constraint-Choice (DCC) framework to unravel their motivations, leadership roles, and decision-making processes. Additionally, the research explores the factors that drive Lebanese women to embrace entrepreneurship, utilizing a qualitative approach through 12 in-depth interviews. The findings reveal the complex interplay of societal, economic, and personal factors influencing these entrepreneurs. Despite significant hurdles, Lebanese women entrepreneurs demonstrate remarkable adaptability and resilience, balancing familial responsibilities with business pursuits. Ultimately, this study sheds light on the intricate dynamics within Lebanon’s entrepreneurial landscape, offering valuable insights into the experiences and contributions of women entrepreneurs.

Internationalization theories posit that emerging market enterprises utilize different entry modes than those in advanced economies. While research has explored various internationalization approaches, scant attention has been paid to how Turkish small and medium enterprises (TSMEs) internationalize and the factors influencing their entry mode. This study aims to fill this gap by investigating the internationalization strategies of Turkish SMEs, focusing on their entry mode. The study uses qualitative methods such as semi-structured interviews to identify correlations and applications of theoretical frameworks in the context of small firms. Additionally, it aims to uncover the challenges faced by these SMEs during internationalization. Interviews with business owners reveal that uncertainties, network conflicts, lack of cultural intelligence, and limited experienced management impede international expansion. The study also notes that recent changes in global trade policies and market dynamics may further impact TSMEs entering international markets.

Entrepreneurship today has grown beyond classical theories and conventional wisdom. It provides contemporary and hybrid insights to manage competitive entrepreneurship by acquiring technology-led entrepreneurial education and global best practices. Entrepreneurship has both vertical and horizontal paths of evolution thorough synchronization of meta-learning with contemporary business practices. It has a reciprocal relationship with radical shifts in markets and entrepreneurial knowledge and competence. These factors strengthen the coexistence of social and economic goals. This chapter discusses vertical and horizontal integration in entrepreneurship by converging the triadic edges of entrepreneurial education, innovation, and technology to support growth and continuity of entrepreneurship. Discussion based on the factors influencing entrepreneurial education, innovation, technology, coevolution of entrepreneurship argues that though integrating innovation and technology faces several economic challenges, the evolution and management of small enterprises stand as the pillars of social and economic growth at the bottom of the pyramid.

This research delves into international entrepreneurship development, explicitly focusing on elucidating the internationalization entry modes embraced by small and medium-sized enterprises (SMEs) hailing from Lusophone African nations. It provides a critical analysis of established internationalization frameworks, emphasizing their fundamental characteristics, encountered challenges, and limitations when attempting to portray the nuances of the internationalization process, particularly in terms of entry-mode selection. Drawing from the author’s experiences while investigating 29 Lusophone African SMEs in Mozambique and Angola, this study puts forth an innovative, integrated, and holistic framework designed to shed new light on the internationalization endeavors of these entrepreneurs. This framework considers the contemporary landscape of information and communication technology and the pervasive influence of digitalization in the business arena. It recognizes the pivotal role played by the Internet, web-enabled tools, and platforms, including the dynamic realms of social media and online professional communities of practice. Through synthesizing empirical findings and theoretical insights, this research contributes to the ongoing discourse on international entrepreneurship by proposing a framework that bridges existing gaps and offers a more comprehensive perspective. By considering the transformative power of digital technologies and their impact on the strategies of Lusophone African entrepreneurs, this study provides a valuable resource for scholars, practitioners, and policymakers seeking to understand better and support the internationalization journeys of entrepreneurs in emerging economies.

The diffusion of information and communication technologies has enabled many payment methods at the point of sale. Emerging countries are usually laggards of technology, and hence, there is a need to fill the gap when new methods of payment tend to diffuse at a slower pace than in other countries. This study aims to fill this gap by extending and testing the UTAUT2 technology acceptance model to analyze the variables that influence consumers’ intentions to use methods of payment at different stages of the product life cycle: mobile payments in the mature stage, digital wallets in the growing track, and radio frequency identification payments for a novel Internet of Things payment method at the introduction stage. Drawing from the information systems theory, this study extends UTAUT2 to include trust, risk, and network externalities to explain intentions to use such payment methods. Using a sample of 264 users and a survey design, the results show that complexity negatively influences consumers’ intention to use mobile money, whereas effort expectancy, facilitating conditions, habit, and price value positively influence consumers’ intention to use mobile money.

While the fashion sector is booming, increasing attention has been brought to the range of negative environmental and social impacts that the industry is responsible for. In emerging markets, post-pandemic, large fashion firms are facing a new set of increased sustainability challenges where scholarly attention is required. Using multiple case studies approach and content analysis, we conceptualize a framework that offers insights into the prevailing sustainability challenges of fashion firms in India. This empirical article attempts to identify social, economic and environmental challenges being faced by large fashion houses in India. The article also attempts to identify environmentally sustainable innovation best practices toward social, economic, and environmental challenges being faced by large fashion houses in India. The framework offers ten best practices, including sourcing and developing indigenous technology to process alternative raw materials and collaborations with artisans and NGOs. In addition, the article also offers innovative practices in supply chains of fashion firms to enhance sustainability.

This paper examines an intersection between innovation and environmental, social, and governance (ESG) initiatives in enterprise management just before and after a time of market meltdown and resurgence from 2019 to 2022. Specifically, we focus on how ESG activities reveal patterns of innovation that can assist companies in excelling. ESG scores reflect how a company operates—in other words, principles and practices that a company has in place—while various impact measures reflect the outcomes of these operational initiatives or practices. A Refinitiv ESG dataset for global firms in 2019 and 2022 was obtained through the Wharton Research Data Services (WRDS) database. The data were classified at the industry level to take into account variations in industrial growth, leadership and innovation trends and because companies typically compete within their industry group. The ESG indexes from the Refinitiv WRDS database enabled the comparison of multiple industries on various ESG-related aspects before and after the COVID-19 pandemic. Additionally, we use machine learning models and predictive analytics to identify patterns and relationships within the ESG data that can provide companies within industries with a competitive edge. We find that a large majority of industries substantially improved the governance pillar of their ESGs, while at the same time most industries fell in the environmental and social scores. Machine learning models identify groupings of industries expressing varying patterns of rises and falls across the three pillars of ESGs, with the dominant pattern being the corporate governance score increase accompanied by the environmental and social scores decline. We explore the implications of these data analytics groups for a better understanding of industry-level reactions to the COVID-19 global pandemic alongside the emergence of future trends of differential importance assigned to the different pillars of ESG in different industries.

Digital entrepreneurship (DE) is experiencing significant growth, with the long-term sustainability of these businesses closely tied to the entrepreneurial skills of their founders. Various factors fuel the entrepreneurial drive, ensuring satisfaction and engagement in their endeavors. It is widely argued that a country’s capacity for fostering DE relies on its cultural values, educational system, supportive ecosystem, and backing from the government and business entities, collectively contributing to the overall sustainability of the economy. The acceptance of DE from an economic and technological standpoint plays a crucial role in creating an environment conducive to institutional change, thereby preventing regulatory capture. Digital entrepreneurs are pivotal in disseminating knowledge and introducing innovative business practices. Many nations, particularly in the West, recognize DE as a critical driver of economic growth, job creation, and modernization.

This study operates through three distinct phases: identifying factors, conducting expert interviews, and crafting a Decision-Making Threat and Opportunity (DMTOP) framework. By amalgamating 23 chosen factors derived from prior literature and expert suggestions, the study categorizes these factors into six dimensions. The framework is applied to ascertain the impact of these factors on both urban and rural settings. The intention is to evaluate the influence of these diverse factors on DE. The resulting insights are anticipated to significantly aid policymakers in achieving their DE objectives. The framework delineates seven dimensions, each with differing impacts on urban and rural infrastructures. These factors collectively contribute to a 47.82 percent overall impact on existing urban facilities while exhibiting a −13.04 percent on the rural side.

While the generally available large language models (LLMs), pre-trained on large, general-purpose datasets, are easy to use, they require additional fine-tuning to perform in an academic or business context requiring specific domain knowledge. The current chapter demonstrates putting a transformer-based LLM in context. The example shows a solution for getting insights from a vast internal knowledge base, a unique source of graph-structured domain-specific information. Typically, querying a knowledge graph (a graph database) requires specific skills, limiting access to its data. The use of generative AI enables querying the graph using natural language. An LLM, fine-tuned with the knowledge graph ontology, translates natural language questions to programmatic queries to the graph database and shows the response in human-readable, graphically enriched form.

The authors present an approach they have tested and proved successful in one of the leading media companies in the world. With the help of a concrete showcase from the finance and business news domain, they describe a methodology that is universally applicable to various industries and educational environments. Also, the established solution is vendor-independent and can be built on different underlying technologies, services, and platforms. The achieved outcome unveils the full potential of the data enabling advanced analytics and can be instrumental in higher education for studies in applied business analytics.

Drawing on the intensive literature on Business Resilience and Business model innovation, the purpose of this chapter is to propose a conceptual framework examining the effect of value co-creation and firm resource integration capabilities in transforming business model innovation. Various databases were searched for identifying the literatures on the proposed variable to understand their role in business model innovation. Further, data for pilot testing were collected from goods and service-based micro, small, and medium enterprises (MSME) in India. The literature review and the pilot study findings revealed that value co-creation and resource integration mechanism plays a significant role in business model innovation. Besides, digital transformation intention has a moderating role in the process. In addition, business resilience moderates the relationship between digital transformation intentions and business model innovation. The suggested framework on the basis of literature and the findings of pilot study proposes capabilities and practices that helps in business model innovation. This study is the first of its kind that has provided an in-depth analysis of the impact of business resilience on business model innovation. In addition, intentions for digital transformation have also been studied in the overall process.

Entrepreneurial creativity transcends family businesses by converging the cross-generational experience and efficiency in doing business. The artisanal entrepreneurs derive creativity from the family experience, self-efficacy, consumer desire, and collective intelligence contextually aimed at enhancing business performance. The principal objective of the study is to analyze the artisanal ethnic creativity, which is influenced by technological interventions such as direct-to-garment printing, computer-aided-designing, and adhesive dyes in innovation and marketing of artisanal fashion products in Mexico. This study is based on the qualitative data gathered by conducting 2 workshops during May–July 2023 and a sample size of 36 respondents including consumers and entrepreneurs, purposively selected using semi-structured research instrument. The study indicates that heritage designs on fashion apparels brought to the market by ethnic entrepreneurs could transform consumer preferences and inculcate social values for ethnic products. Artisanal ethnic fashion products have gained the attention of upstream markets due to the underlying patriotic sentiment of ‘Made in Mexico’.

This chapter examines the effect of Intellectual Capital on the survival of startups participating in global accelerator programs in one hundred and fifty-eight countries. Utilizing secondary data from the Entrepreneurship Database Program of Emory University, and following factor analysis on variables, a Logistic Regression Model was constructed to elucidate Intellectual Capital’s positive impact on the survival probabilities of nascent ventures. The model consistently confirms an overall positive influence of intellectual capital, encompassing human (generic and specific), structural, and social capitals on survival. This confirmation holds true even after accounting for contextual variables, gender, and characteristics of the accelerator programs. Findings suggest that as the formal education level of founders increases, the probability of survival decreases, indicating that founders face higher switching costs to sustain the venture. Opting for market orientation over innovation orientation and choosing debt over equity from external shareholders emerge as effective strategies to improve the probability of survival for new ventures. A female majority in the founding team, as well as the focus and curriculum of the accelerator program, positively impact the probability of survival. In contrast, the context of the country where the ventures operate has a negative effect. This chapter addresses a gap in the literature, as most studies validating Intellectual Capital effects on the probability of nascent ventures have been conducted in high-income countries. The present study contributes by including low and middle-income countries in its analysis.

Predicting stock prices is difficult because of their multiple input variables, volatility, and unpredictable nature. To provide a suitable model for forecasting the global stock market, this study conducts an exploratory analysis comparing two models based on Artificial Intelligence: Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) Neural Networks. The work considers a publicly accessible dataset and uses feature engineering to extract time-series features. Stock price predictions are made using the SVM and LSTM algorithms. For this purpose, Accuracy (ACC) and Root Mean Squared Error (RMSE) are considered accuracy and performance measures. According to the results, LSTM with mean accuracy (ACC) = 0.9061 achieved better accuracy than SVM with mean accuracy (ACC) = 0.881. SVM with mean RMSE = 0.729 achieved better performance and the degree of fit to the data than LSTM with mean RMSE = 427.1. According to the results, the study demonstrates the effectiveness and applicability of machine learning methods for estimating the values of the global stock market and providing valuable models for researchers, analysts, and investors.

In the dynamic landscape of entrepreneurship, understanding the intricate factors that influence startup success is vital for investors, policymakers, and entrepreneurs alike. This chapter presents a meticulous analysis of a curated dataset, delving into the prediction of startup success based on various key features. Employing advanced data analysis techniques such as machine learning algorithms and statistical modeling, we explore the relationships among variables, including funding amounts, geographic location, milestones achieved, number of employees, business sector, and types of investors. From a dataset of 923 US startups founded between 1984 and 2013, we determined that 80 percent of the firms can be considered successful in terms of having belonged to the 500 Global group of high-potential and fast-growing firms receiving specific, competitive venture capital funding. Additionally, from the dataset, 65 percent of firms were eventually acquired, which can also be viewed as a marker of success. The startup and acquisition rates fell, and the startup closure rates increased in the immediate aftermath of both the 2001 and 2008 market crises. The gradient boosting and adaptive boosting ensemble learning algorithms disambiguated startup acquisitions and closures. Angel-funded and venture capital (VC)-funded startups had approximately equal failure rates. While the startup success and failure rates did not greatly differ according to the type of investment funding received, three key findings emerged: (1) startup and acquisition rates declined and start-up closures increased after each financial crisis; (2) startups funded by VC received higher amounts of funding than those funded by angel investors; and (3) successful startups had received approximately twice as much funding, 30 million USD compared to 15 million USD, as those that failed. Moreover, the ensemble machine learning algorithms of gradient boosting and adaptive boosting proved particularly powerful at levels surpassing 80 percent for predicting which specific startups would survive or fail. Survival in the focal dataset ultimately meant acquisition by a larger firm, enhancing access to markets and resources, and cashing out the VC position.

This chapter delves into the multifaceted landscape of entrepreneurial readiness among bachelor-level students, examining its connections with emotional support, perceived attractiveness, learning orientation, perceived ability, and passion for work. Conducted through a cross-sectional survey, the research gathered data from 278 randomly selected students at a private higher education institution. Utilizing a structured questionnaire customized by the authors, the study seeks to offer practical insights and deepen our comprehension of entrepreneurial readiness. Quantitative techniques, including confirmatory factorial analysis, regression analysis, and correlation analysis within SPSS-Amos, were employed for data analysis. The results refine educational strategies and support systems, aiming to foster the development of future entrepreneurs. This research significantly enhances our understanding of the nuanced aspects of entrepreneurial readiness among business students. By investigating traditional determinants of entrepreneurial intention and acknowledging the pivotal role of emotional support from professors, the study addresses a critical gap in literature. The unique contribution lies in unraveling how emotional support from academic community shapes students’ entrepreneurial readiness, providing a holistic perspective on the intricate decision-making processes involved in entrepreneurship. The findings offer practical implications for nurturing and empowering the entrepreneurial potential of students.

Societies that are more oriented on being traditional, as in Latin America, emphasize the institution of family; hence, family businesses may be supported by laws, regulations, services, and education and training. Therefore, in these kinds of developing countries, family is the first school, where values and norms shape the practices that are shaped (Verbeke et al., A values-based analysis of bifurcation bias and its impact on family firm internationalization. Asia Pacific Journal of Management, 1–29, 2019). Evidence shows that parents can become role models for the youngest (Lee et al., Entrepreneurship education and founding passion: The moderating role of entrepreneurial family background. Frontiers in Psychology,12(743672), 2021). Undergraduates with family business traditions are more inclined to choose the entrepreneurial route (Cieślik & van Stel, Explaining university students’ career path intentions from their current entrepreneurial exposure. Journal of Small Business and Enterprise Development, 24(2), 313–332, 2017). Notwithstanding controversial results concerning parents as role models’ influence when dealing with the entrepreneurial career option (Abd El Basset et al., Reducing barriers to female entrepreneurship in Oman: Does family matter? Journal of Enterprising Communities: People and Places in the Global Economy, 2022). Few studies have been done in Latin America, especially in countries such as Mexico and Peru. These countries have higher entrepreneurial capacity (Bosma & Kelley, Global Entrepreneurship Monitor [GEM] 2018/2019 Global Report. GEM Global Entrepreneurship Monitor. https://​www.​gemconsortium.​org/​file/​open, 2019; GEM, Global Entrepreneurship Monitor 2022/2023 Global Report: Adapting to a “New Normal”. GEM, 2023). In Mexico, higher education institutions are working to foster entrepreneurship in the community, and other higher education institutions are trying to build more robust entrepreneurial ecosystems in their institutions. Peru is among the first four countries with the most significant entrepreneurial activity within the group of efficiency-based economies (Bosma & Kelley, Global Entrepreneurship Monitor [GEM] 2018/2019 Global Report. GEM Global Entrepreneurship Monitor. https://​www.​gemconsortium.​org/​file/​open, 2019); the country has been advised about the relevance of entrepreneurial education necessary in the educational system to help reduce the level of the business survival indicator (GEM, Global Entrepreneurship Monitor 2017/18. GEM, 2018). Therefore, understanding the perspective of students from different entrepreneurial university ecosystems becomes important.

This chapter offers an in-depth analysis of analytics-related occupations within the US labor market, with a primary focus on providing valuable insights to students seeking the best job application strategies. Employing various data mining techniques, including Exploratory Data Analysis, Time Series Analysis, Clustering Analysis, and Association Rules Analysis, we examine the trends and statistics surrounding these occupations over the past ten years, drawing from historical job posting data aggregated from Lightcast. The research investigation encompasses critical aspects such as essential skill requirements, the most prevalent job titles, geographical job distribution, participating industries and companies, as well as compensation and benefits associated with each occupation. Furthermore, we explore the grouping of occupations that share similar characteristics and identify the skills frequently sought after by employers in analytics-related job postings. Additionally, the research also provide job posting forecasts and other pertinent information for each occupation, equipping students to make well-informed career decisions.

Descriptive analytics on the most popular analytics-related occupations and job titles, salary and wage ranges for each occupation, the most sought-after skills, industries and companies with the highest job postings, cities with the most job postings, time series decomposition revealing trends and seasonality in job postings, occupational groupings, and frequently co-occurring skills in job postings were generated as the results of the research. These research results are conveniently hosted on GitHub in the form of interactive and user-friendly Jupyter Notebooks static website. This platform allows students to engage with the analysis results, presented as Power BI Dashboards and Plotly Visualizations, facilitating informed decision-making. By leveraging data mining techniques to dissect the analytics-related job landscape, our research serves to inform analytics students and graduates about the diverse career opportunities available. It provides guidance on crafting effective job application strategies and ultimately supports their post-education professional journey.

This chapter examines the disparities between industry requirements and the qualifications offered by the BU Metropolitan College Applied Business Analytics (BU MET ABA) program, with the aim of enhancing program and student competitiveness in the evolving Analytics job market in the United States. The study identified specific skills and qualifications lacking in the program but deemed essential by the industry, providing insights for program refinement. Additionally, it established a standardized self-service employability framework to guide students in the U.S. job search. The research yielded two key outcomes: First, it discovered gaps in the BU MET ABA program, guiding improvements to align with current industry demands. Second, it provided a four-phases standardized employability framework for students navigating the job market, empowering them with a deeper understanding of industry dynamics for increased preparedness. Recommendations from the gap analysis focus on strengthening the BU MET ABA program’s curriculum, ensuring alignment with dynamic industry needs. First, ongoing industry engagement, facilitated by an advisory board, will integrate emerging technologies and real-world applications. Initiatives such as skill assessment platforms, expanded internship programs, soft skills development, alumni feedback mechanisms, and mentorship programs contribute to a holistic approach of closing the gaps.

The second outcome of the research resulted in the development of a four-phase self-service standardized framework. This framework incorporates four distinct Power BI dashboards and two Python-based web applications designed to assist students in discovering their preferred occupations, assessing skills requirements, addressing skill gaps, and applying for jobs in the appropriate locations. These tools enhance the student experience, providing practical guidance throughout the job search and application process. This research and benchmarking efforts aim to keep the program at the forefront of analytics education, while lifelong learning opportunities for alumni underscore a commitment to continuous improvement. These measures collectively position the ABA program as a leading force in preparing students for successful careers in analytics and business intelligence.