Entrepreneurial Ecosystems as Amplifiers of the Lean Startup Philosophy: Management Control Practices in Earliest-Stage Startups*

Entrepreneurial ecosystems play a key role in the development of startups not only by providing support—such as flexible office space and access to skilled employees, mentors, and investors—but moreover by promoting concrete ideals about “good” entrepreneurship. However, we know less about the role that ecosystems play in managerial practices of startups. In our empirical analysis of management control systems (MCSs) in earliest-stage startups, we witness a strong influence of entrepreneurial ideals—above all, the Lean Startup philosophy—on the MCSs analyzed. Building on cross-sectional field study data resulting from a comprehensive field-immersion strategy and 50 interviews with key actors in an entrepreneurial ecosystem as well as with founder-managers of startups, we consider the entrepreneurial ecosystem as a collective meso-level community that mediates between macro-level institutional pressures and micro-level practices of startups. We show how this community, through a variety of what we term amplifying mechanisms, actively deinstitutionalizes a legacy entrepreneurial philosophy epitomized by the business plan concept. At the same time, the community propagates the Lean Startup philosophy so that this alternative has become the dominant institutional philosophy in the studied ecosystem and its startups. Due to the amplifying mechanisms exerted by the meso level, startups use MCSs that play a crucial role in the rapid experimentation and learning process toward finding a scalable business model that is characteristic of the Lean Startup philosophy. We highlight that this philosophy of scientific experimentation has, to a significant degree, transformed intuitive entrepreneurial processes into a set of transactions that can be steered and accelerated by MCSs.     

Ethics of AI-Enabled Recruiting and Selection: A Review and Research Agenda

Journal of Business Ethics - Companies increasingly deploy artificial intelligence (AI) technologies in their personnel recruiting and selection process to streamline it, making it faster and more...     

Correction to: Improving decisions with market information: an experiment on corporate prediction markets

Experimental Economics - A correction to this paper has been published: https://doi.org/10.1007/s10683-021-09711-0     

Recurrent Neural Networks for Time Series Forecasting: Current status and future directions

Recurrent Neural Networks (RNNs) have become competitive forecasting methods, as most notably shown in the winning method of the recent M4 competition. However, established statistical models such as exponential smoothing (ETS) and the autoregressive integrated moving average (ARIMA) gain their popularity not only from their high accuracy, but also because they are suitable for non-expert users in that they are robust, efficient, and automatic. In these areas, RNNs have still a long way to go. We present an extensive empirical study and an open-source software framework of existing RNN architectures for forecasting, and we develop guidelines and best practices for their use. For example, we conclude that RNNs are capable of modelling seasonality directly if the series in the dataset possess homogeneous seasonal patterns; otherwise, we recommend a deseasonalisation step. Comparisons against ETS and ARIMA demonstrate that (semi-) automatic RNN models are not silver bullets, but they are nevertheless competitive alternatives in many situations.