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Entrepreneurs interact with others and, through this, benefit from access to knowledge, resources and skills that enhance their own entrepreneurial and organizational capabilities. This paper aims to contribute to the literature interested in identifying and analyzing important antecedents of entrepreneurs’ choices regarding social relations. The study shows how the venture stage, innovativeness and internationalization of the firm potentially influence entrepreneurial choices regarding their social sources of advice.

The analysis is based on cross-sectional survey data for the years 2009 and 2010, involving 13 Middle East and North African (MENA) countries. Respondents include future prospective entrepreneurs, start-ups and owner-managers of operating businesses, a total of 13,251 respondents across all countries for the entire period.

Entrepreneurs with innovative ventures draw more on advice sources that are able to give information useful for the commercialization of innovative products, and entrepreneurs of internationally exposed ventures rely on a broad base of advice sources that can connect them with a foreign market. However, the outcomes regarding the impact of “different venture stages” point to social interaction patterns that are strongly influenced by local culture and that do not support the assumption of universal entrepreneurship behavior. This study shows that social interactions decline in quantity the more as the venture progresses in age. However, the type of social interaction (e.g. private or professional sources) that entrepreneurs engage throughout the different venture stages remains essentially the same and does not change across different entrepreneurial phases. In the MENA sample, private relations remain the most important source of advice throughout all phases, and they are not replaced by other contacts.

Limitations of this paper refer to the use of a large-scale database that cannot address certain issues without more direct observation, such as the quality of different social relations. Future research could address this issue by offering more fine-grained items for the different advice sources.

The paper contributes to the debate on whether entrepreneurship is universal in nature. It focuses on data from emerging and developing countries in the Arab world, which is has not been studied very much in the entrepreneurship literature.

The purpose of this paper is to investigate the combined roles via trust relationships of the two technology cores of the firm: information technology (IT) and R&D and their impact on new product success.

A model was tested whereby trust and the integrated IT strategy account for a significant amount of the variance in a broad range of new product development (NPD) outcomes for a survey sample of 223 manufacturing firms. Respondents said design practices and quality methods like Six Sigma accounted for a total of over 25 percent of the reports of the most helpful approaches in promoting effective NPD. At the same time their biggest challenges were having a clear strategic direction within which to operate and resolving cost and resource issues which accounted for over a third (34 percent) of barriers to success.

Respondents reported that a total of over 25 percent of the reports of the most helpful approaches in promoting effective included these quality methods. At the same time their biggest challenges were having a clear strategic direction within which to operate and resolving cost and resource issues which accounted for over a third (34 percent) of barriers to success. High-tech firms were less likely to report integrated IT strategies, but this tended to be counterbalanced by high levels of trust in the IT function and adoption of organizational innovations for execution of strategic intent. Implications for future research and practice are discussed.

Survey methods produce broad results with low response rates in most studies involving R&D and NPD, and this study is no exception.

With the challenge of strategy alignment reported by many of these firms, it seems clear that the top management team cannot afford to leave NPD challenges to engineering teams and NPD programs without guidance and general vision.

NPD has become the staple of most manufacturing firms as a way of meeting and beating the competition worldwide. However, trust between functional areas often starts before people are even employed and should begin in training and educational programs.

Designing NPD programs is at the heart of many firms’ competitive strategies and the fast learning companies are the winners. Very little is known about the trust relationship between IT and R&D and their combined effects on new product success which we have found to be significant and unexpected in their impacts.

As a response to global population growth and increasing demand for food, farmers have been complementing traditional agriculture practices with vertical farming (VF) and indoor hydroponic systems. To facilitate the growth of the VF industry, this paper aims to identify business model elements and their configurations that lead to high firm performance.

The research goals were met by conducting literature reviews coupled with a fuzzy-set qualitative comparative analysis (fsQCA) on five business model elements, “superior” OR “strong” performance as two possible outcomes, and the top-ranked global VF growers listed in the Crunchbase Database.

From the fsQCA results, it was observed that several business model configurations lead to strong firm performance. Vertical farms growing in urban settings and having strong customer engagement platforms, coupled with a presence of business-to-business (B2B) sales channels, are more consistently associated with superior performance. These results imply that the decision configuration of location, along with customer engagement activity and sales activity are differentiating factors between good firm performance and superior firm performance in the case of vertical farms.

This paper contributes to expanding the knowledge of business model theory, business model configurations and VF management, providing specific guidelines for vertical farm owners and investors related to decision-making for higher firm performance, as well as positive environmental, social and economic impact.

Prior research underscores the critical role of prestigious underwriters in shaping the success of the initial public offering (IPO) process, particularly for young firms that do not have much of a track record. Recent scholarly work has shown that the likelihood of a start-up securing a lead prestigious underwriter is influenced by its ability to provide important signals of organizational legitimacy, as conveyed in the employment experiences of the firm's top management team. Building further on theories of organizational attention and decision making, this chapter seeks to examine whether lead prestigious underwriters also consider different types of signals of organizational legitimacy that might be suggested by the existence of ties between young firms and corporate venture capital (CVC) investors.Analysis of 1830 IPOs during 1990–1999 indicates that having a tie to CVC investor provides added legitimacy value over that provided by independent venture capital investors alone. Further analysis of 315 IPOs affiliated with CVC investors suggests that prestigious underwriters pay attention primarily to endorsement-rather than resource-related signals of legitimacy when it comes to CVC ties, and that they pay more attention to investment screening prominence than to business management prominence when it comes to endorsement legitimacy. We also found that prestigious underwriters pay more attention to signals of IPO legitimacy provided by CVC investment in IPO markets that are hot than those that are cold. Our findings provide important theoretical extensions to the study of the certification value of interorganizational affiliations and its impact on IPO success.

Book review by William H. A. Johnson. Casson, Mark et al., eds. The Oxford Handbook of Entrepreneurship, New York, NY: Oxford University Press, 2006. ISBN 9780199288984

This study aims to predict popular contributors through text representations of user-generated content in open crowds.

Three text representation approaches – count vector, Tf-Idf vector, word embedding and supervised machine learning techniques – are used to generate popular contributor predictions.

The results of the experiments demonstrate that popular contributor predictions are considered successful. The F1 scores are all higher than the baseline model. Popular contributors in open crowds can be predicted through user-generated content.

This research presents brand new empirical evidence drawn from text representations of user-generated content that reveals why some contributors' ideas are more viral than others in open crowds.

This research suggests that companies can learn from popular contributors in ways that help them improve customer agility and better satisfy customers' needs. In addition to boosting customer engagement and triggering discussion, popular contributors' ideas provide insights into the latest trends and customer preferences. The results of this study will benefit marketing strategy, new product development, customer agility and management of information systems.

The paper provides new empirical evidence for popular contributor prediction in an innovation crowd through text representation approaches.

Outlines the technological problems which make it hard to deliver high quality video over the internet, e.g. insufficient bandwidth, clients’ machines etc.; and considers how they might be solved. Describes how digital video, audio presentations and animations can be streamed to a computer and the ways in which various US sectors are actually using streaming media at the moment. Discusses the future for multimedia applications in corporate training, e‐business and higher education; and predicts they will be very widely used as the technology improves.

The lack of clarity in defining data science is problematic in both academia and industry because the former has a need for clarity to establish curriculum guidelines in their work to prepare future professionals, and the latter has a need for information to establish clear job description guidelines to recruit professionals. This lack of clarity has resulted in job descriptions with significant overlap among different related professional groups. This study examines the industry view of five professions: statistical analysts (SAs), big data analytics professionals (BDAs), data scientists (DSs), data analysts (DAs) and business analytics professionals (BAs). The study compares the five fields with the unified backdrop of their common semantic dimensions and examines their recent dynamics.

1,200 job descriptions for the five Big Data professions (SA, DS, BDA, DA and BA) were pulled from the Monster website at four points in time, and a document library was created. The collected job qualification records were analyzed using the text analytic method of Latent Semantic Analysis (LSAs), which extract topics based on observed text usage patterns.

The findings indicated a good alignment between the industry view and the academic view of data science as a blend of statistical and programming skills. This industry view remained relatively stable during the 4 years of our study period.

This research paper builds upon a long tradition of related studies and commentaries. Rather than relying on subjective expertise, this study examined the job market and used text analytics to discern a space of skill and qualification dimensions from job announcements related to five big data professions.