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By extending the service robot acceptance model (sRAM), this study aims to explore and enhance the acceptance of chatbots. The study considered functional, relational, social, user and gratification elements in determining the acceptance of chatbots.

By using the purposive sampling technique, data of 321 service customers, gathered from millennials through a questionnaire and subsequent PLS-SEM modeling, was applied for hypotheses testing.

Findings revealed that the functional elements, perceived usefulness and perceived ease of use affect acceptance of chatbots. However, in social elements, only perceived social interactivity affects the acceptance of chatbots. Moreover, both user and gratification elements (hedonic motivation and symbolic motivation) significantly influence the acceptance of chatbots. Lastly, trust is the only contributing factor for the acceptance of chatbots in the relational elements.

The study extends the literature related to chatbots and offers several guidelines to the service industry to effectively employ chatbots.

This is one of the first studies that used newly developed sRAM in determining chatbot acceptance. Moreover, the study extended the sRAM by adding user and gratification elements and privacy concerns as originally sRAM model was limited to functional, relational and social elements.

In a recent quasi-experimental study, the effects of a large German public startup support measure entitled “EXIST – Business Startup Grant” (EGS) on a variety of outcomes were determined, but without examining which factors are responsible for these program effects. The present study investigates the contribution of several factors to the success of the program in promoting product development and business planning.

By means of a two-wave panel design and fixed-effects panel regressions, evidence is generated that provides unique insights into the effect mechanisms of a publicly funded startup grant. The data for the study come from the program monitoring of the startup support measure.

Several factors were identified that significantly drive the effects of the program on the product development and business planning stages, namely the program-induced improvement of the skills of the startup team, intensification of cooperation with pilot customers/users, increase in the degree of networking and advice/support from third parties and the effort put into business plan preparation.

Startup support programs are a crucial aspect of technology and innovation policies, which are often evaluated in order to find out whether they generate effects. Assessing whether a program is effective or not, however, does not usually allow specific recommendations on how to improve the measure to be developed. Further information on the mechanisms of intervention is needed for this purpose. The present study takes up on this idea and provides this information for a specific type of public startup support measure.