Innovative technology enterprises are recognized internationally as an important pillar in modern economic activity. This paper presents the findings from a research combining qualitative and quantitative methods, with the specific goal of identifying and verifying the characteristics that affect their survival and growth. Results from an in-depth longitudinal qualitative case study, that examines a mature and constantly growing (in its 10-year operation) technologically innovative enterprise, reveal that a number of characteristics pertaining to both the profile of the entrepreneurial team, as well as of the employees, significantly affect company survival and growth in this context. Moreover, we recognize and analyze three stages in its evolution: an initial “evolutionary” growth (infancy and youth), followed by a “revolutionary” (crisis), and a second “evolutionary” (maturity) stage. Our findings are further corroborated and enriched through a survey with N = 27 entrepreneurs in innovative technology startups. We contribute to existing literature on innovative technology entrepreneurship, by identifying characteristics that entrepreneurs and employees should bear, towards its survival and growth. Moreover, a practical application of the life cycle approach is described for technologically innovative companies. Finally, a specific prescription that can help guide future theoretical and practical endeavors in innovative technology entrepreneurship is also provided accordingly.

This paper has developed and deployed a Bluetooth Low Energy (BLE) beacon-based indoor positioning system in a two-floor retail store. The ultimate purpose of this study was to compare the different indoor positioning techniques towards achieving efficient position determination of moving customers in the retail store. The innovation of this research lies in its context (the retail store) and the fact that this is not a laboratory, controlled experiment. Retail stores are challenging environments with multiple sources of noise (e.g., shoppers’ moving) that impede indoor localization. To the best of the authors’ knowledge, this is the first work concerning indoor localization of consumers in a real retail store. This study proposes an ensemble filter with lower absolute mean and root mean squared errors than the random forest. Moreover, the localization error is approximately 2 m, while for the random forest, it is 2.5 m. In retail environments, even a 0.5 m deviation is significant because consumers may be positioned in front of different store shelves and, thus, different product categories. The more accurate the consumer localization, the more accurate and rich insights on the customers’ shopping behavior. Consequently, retailers can offer more effective customer location-based services (e.g., personalized offers) and, overall, better consumer localization can improve decision making in retailing.

We discuss the challenges of implementing an Internet-based platform for creating collaborative supply chains using a case study in the retail sector. The case presents how an Internet-based collaboration platform was implemented to address the strategic issue of increasing shelf availability and customer service in grocery retailing, an issue that has emerged as one of the major confrontations for the whole sector over the past years. The case presented shows the challenges of executing such strategic collaborative supply-chain initiatives which, although arguably beneficial, can be hindered by IT adoption failures. A longitudinal view of the case is presented, from an initial pilot back in 2001 to the final success in 2005. We discuss the particular challenges of the execution of Internet-enabled collaborative supply-chain initiatives as well as possible managerial actions through a simple framework we develop based on the lessons from the case.