Managing Technology Push through Marketing Testbeds: The Case of the Hi-Tech Center in Vienna, Austria

The ‘technology push’ approach to technology development rests on the assumption that if you make it, they will come. This assumption carries significant market risk. The technology may miss its intended market window, or the market that was anticipated at the inception of technology development no longer exists at the time of market release. This paper discusses how the Hi Tech Center in Vienna, Austria, a multi national collaborative effort between industry and universities in Central Europe, helps its clients manage technology push by deploying the marketing testbed approach. After identifying lead users for a client’s technology, it characterizes and determines optimal market entry dates and windows of opportunity; readiness for and resistance to adoption; technology acceptance and marketability; and best practices for market entry. The Hi Tech Center learned the following overarching lesson from engaging with six clients in six different industries: marketing testbeds comprise an effective toolkit for managing technology push, primarily because they act as a link between the technology readiness level and the market readiness level. Thus they provide early insight into the customer’s willingness to pay, the degree of fit between key features of the technology and marketability criteria, and, by extension, potential return on investment

Application of the DEA Model in Tourism SMEs: An Empirical Study from Slovakia in the Context of Business Sustainability

Slovak spa services are not given sufficient attention directly following the support and sustainable development. The paper focuses on the evaluation of the overall development and current level of efficiency of the Slovak spas in 2013–2018, through the application of DEA models. Input variables (total number of beds, employees, medical staff) and output variables (use of bed capacity, number of treated clients) within the structure of DEA models analyzed (CCR-I, CCR-O, BCC-I, BCC-O) are determined by results of the correlation analysis. The data were obtained from the annual reports of the spa enterprises. By the results, the average efficiency score for all enterprises reached 0.7527, i.e., the average spa enterprise would need only 75.27% of currently used inputs for a given output production to move to the efficiency frontier. The development of the average efficiency score confirmed a positive growing trend until 2015; however, the efficiency decreased by 1.84% in a year-to-year comparison in 2016–2018. In each year of the analyzed period, the number of inefficient enterprises (66.67%) exceeded that of the efficient ones (33.33%). Through research carried out in spa facilities, the authors contributed to expanding the application of the DEA method in another tourism sector