The Maturing of Entrepreneurial Firms: Entrepreneurial Orientation, Firm Performance, and Administrative Heritage

A large body of research has exhibited the positive effect of entrepreneurial orientation (EO) on firm performance. However, research that attempts to explore what happens to high EO firms when they mature is sorely needed. Every firm establishes a heritage over time that impacts future capabilities. In the current research, we build on the international business literature to examine how a firm’s administrative heritage moderates the long-term effects of the EO-performance relationship, examined through the firm’s asset specificity, founder tenure, and home culture embeddedness. From this, implications are derived for EO retention and the firm’s awareness of administrative heritage and how to shape it to their advantage

The Impact of Organizational Goal Setting on the Industrial Munificence-goal Attainment Relationship

In seeking to exploit environmental resources and opportunities, CEOs can either set multiple goals or narrow their focus on a few targets for the organizations. What approach will help organizations to benefit more from industrial munificence? In this paper, we investigate the moderating effects of CEOs’ goal setting (including the number of goals and the prioritization of these goals) on the relationship between industrial munificence and the satisfaction of goal attainment. By examining 277 small and medium-size firms in four countries, we find that CEOs need to stretch their goal list while keeping a clear priority order among these goals in order to capitalize on industrial munificence. Implications of our study are discussed

The Law and Policy of People Analytics

(Excerpt) Recently, leading technology companies such as Google and IBM have started experimenting with people analytics, a new data-driven approach to human resources management. People analytics is just one example of the phenomenon of big data, in which analyses of huge sets of quantitative information are used to guide a variety of decisions. Applying big data to workplace situations could lead to more effective work outcomes, as in Moneyball, where the Oakland A\u27s baseball franchise used statistics to assemble a winning team on a shoestring budget. People analytics is the name given to this new approach to personnel management on a wider scale. Although people analytics is a nascent field, its implementation could transform the ways that employers approach HR decisions. Data may help firms determine which candidates to hire, how to help workers improve job performance, and how to predict when an employee might quit or should be fired. In addition, people analytics could provide insights on more quotidian issues like location of the employee offices and use of break times. The data that drives these decisions may be collected in new ways: through the use of innovative computer games, software that monitors employee electronic communications and activities, and devices such as ID badges that record worker locations and the tone of conversations. Data may also be collected from sources outside the employer which have been gathered for different purposes, like real estate records, or for undefined purposes, like Google searches. While people analytics has great potential, no one has yet comprehensively analyzed the employment law or business ethics implications of these new technologies or practices. To date, most of the discussion centers on the uses for the data, not on its effects or its interactions with the law of the workplace. This Article seeks to survey these effects and interactions. Part I provides an overview, reviewing the history of employment testing, defining data mining, and describing the most current trends in people analytics. Part II describes the use of computer games and other technology to gather information. Part III examines the implications of people analytics on workplace privacy norms and laws. Part IV discusses the impact on equal-opportunity norms; while more and better information should lead to more merit-based decisions, disparate impact or unconscious bias could still operate to harm already-marginalized workers. Part V concludes with normative observations and preliminary policy notes. As the field of people analytics continues to develop, we must keep the values of employee voice, transparency, and autonomy as guiding principles

A framework for mining lifestyle profiles through multi-dimensional and high-order mobility feature clustering

Human mobility demonstrates a high degree of regularity, which facilitates the discovery of lifestyle profiles. Existing research has yet to fully utilize the regularities embedded in high-order features extracted from human mobility records in such profiling. This study proposes a progressive feature extraction strategy that mines high-order mobility features from users' moving trajectory records from the spatial, temporal, and semantic dimensions. Specific features are extracted such as travel motifs, rhythms decomposed by discrete Fourier transform (DFT) of mobility time series, and vectorized place semantics by word2vec, respectively to the three dimensions, and they are further clustered to reveal the users' lifestyle characteristics. An experiment using a trajectory dataset of over 500k users in Shenzhen, China yields seven user clusters with different lifestyle profiles that can be well interpreted by common sense. The results suggest the possibility of fine-grained user profiling through cross-order trajectory feature engineering and clustering

The Law and Policy of People Analytics

Leading technology companies such as Google and Facebook have been experimenting with people analytics, a new data-driven approach to human resources management. People analytics is just one example of the new phenomenon of “big data,” in which analyses of huge sets of quantitative information are used to guide decisions. Applying big data to the workplace could lead to more effective outcomes, as in the Moneyball example, where the Oakland Athletics baseball franchise used statistics to assemble a winning team on a shoestring budget. Data may help firms determine which candidates to hire, how to help workers improve job performance, and how to predict when an employee might quit or should be fired. Despite being a nascent field, people analytics is already sweeping corporate America. Although cutting-edge businesses and academics have touted the possibilities of people analytics, the legal and ethical implications of these new technologies and practices have largely gone unexamined. This Article provides a comprehensive overview of people analytics from a law and policy perspective. We begin by exploring the history of prediction and data collection at work, including psychological and skills testing, and then turn to new techniques like data mining. From that background, we examine the new ways that technology is shaping methods of data collection, including innovative computer games as well as ID badges that record worker locations and the duration and intensity of conversations. The Article then discusses the legal implications of people analytics, focusing on workplace privacy and employment discrimination law. Our article ends with a call for additional disclosure and transparency regarding what information is being collected, how it should be handled, and how the information is used. While people analytics holds great promise, that promise can only be fulfilled if employees participate in the process, understand the nature of the metrics, and retain their identity and autonomy in the face of the data’s many narratives

The Law and Policy of People Analytics

Leading technology companies such as Google and Facebook have been experimenting with people analytics, a new data-driven approach to human resources management. People analytics is just one example of the new phenomenon of “big data,” in which analyses of huge sets of quantitative information are used to guide decisions. Applying big data to the workplace could lead to more effective outcomes, as in the Moneyball example, where the Oakland Athletics baseball franchise used statistics to assemble a winning team on a shoestring budget. Data may help firms determine which candidates to hire, how to help workers improve job performance, and how to predict when an employee might quit or should be fired. Despite being a nascent field, people analytics is already sweeping corporate America. Although cutting-edge businesses and academics have touted the possibilities of people analytics, the legal and ethical implications of these new technologies and practices have largely gone unexamined. This Article provides a comprehensive overview of people analytics from a law and policy perspective. We begin by exploring the history of prediction and data collection at work, including psychological and skills testing, and then turn to new techniques like data mining. From that background, we examine the new ways that technology is shaping methods of data collection, including innovative computer games as well as ID badges that record worker locations and the duration and intensity of conversations. The Article then discusses the legal implications of people analytics, focusing on workplace privacy and employment discrimination law. Our article ends with a call for additional disclosure and transparency regarding what information is being collected, how it should be handled, and how the information is used. While people analytics holds great promise, that promise can only be fulfilled if employees participate in the process, understand the nature of the metrics, and retain their identity and autonomy in the face of the data’s many narratives

Institutional entrepreneurial orientation: Beyond setting the rules of the game for blockchain technology

Regulations keep evolving on currency-based blockchain technology, making it difficult for entrepreneurs within this realm to find a home. Going beyond temporal regulations and traditional metrics to help entrepreneurs evaluate formal institutional environments, we apply and extend the theoretical framework of entrepreneurial orientation (EO) to the institutional level (termed institutional EO) to index an institution\u27s innovativeness, proactiveness, and risk-taking towards blockchain technology. Evaluating a country\u27s institutional EO allows entrepreneurs to understand how embedded the institution is within a certain technology (in this case blockchain technology), which in turn, signals to entrepreneurs longer-term havens in which to locate to develop and commercialize the technology

Prior knowledge and new product and service introductions by entrepreneurial firms: the mediating role of technological innovation.

Most research on new product and service development by entrepreneurial firms takes an individual-level, pre-launch perspective or firm-level post-launch perspective. Our study examines two components of the new product and service introduction process: how entrepreneurs’ prior knowledge underpins (1) firm technological innovation prior to the introduction of new products and services (pre-launch) and (2) post-launch viability of those new products and services. Our findings, based on a series of analyses of data from 158 entrepreneurial firms, show that formal technological innovation fully mediates the relation between prior knowledge and the introduction of viable new products and services