PurposeThis research is an empirical study that addresses whether knowledge resources impact on, or do not impact on, innovation development and if this impact is mediated by dynamic capabilities in the medical tourism sector in Mashhad city, Iran.Design/methodology/approachA quantitative research methodology was applied and questionnaires were used for data collection in this study. A total of 108 questionnaires were collected of which 102 questionnaires were valid. Data were analyzed using structural equation modelling technique.FindingsEmpirical evidence obtained from the study reveals that the dynamic capability of learning plays a significant role in transforming knowledge resources into innovation in the medical tourism sector. The mediating role of coordinating capability in the relationship between explicit and tacit knowledge and innovation is considerable and it influences human capital, as well. Sensing capability also exhibits some degree of a mediating role; however, integrating capability is not influential and its role in transforming explicit knowledge to innovation is rejected.Originality/valueMost studies on innovation in medical tourism focused on market and its typology, and neglected the role of knowledge resources and dynamic capabilities. The current study bridges this gap and thus contributes to the scientific literature.

The aim of this study is to build a dynamic systems model (DSM) in order to construct scenarios for the future of university–industry collaboration (UIC) in Iran. A mixed research methodology was used and six stages of scenario development were followed. To identify the key variables (forces that contribute to the promotion of UIC), the relevant innovation systems theories and literature related to UIC considering macro and micro environment was explored. A questionnaire was designed to identify the scenario driving forces of UIC in Iran and 90 respondents participated from both university and industry side. In order to construct a DSM based on Iranian context, generating and interpreting scenarios, 25 interviews were conducted with the major players of UIC including university professors, industrial experts and also experts from government institution associated with two Iranian cluster industries (Automotive and Biotechnology). These experts were selected based on a snowball sampling technique. Theme analysis was used to analyse the qualitative data and generate scenarios. A set of evolved states (exploratory and future-backwards scenarios) served to illustrate the plausible futures of UIC in Iran, and therefore to inform an improvement agenda for UIC activities.

The research objective is to develop system models of the university-industry collaboration (UIC) element of a national innovation system and to evaluate its potential as a framework to inform economic policies. A UIC system model for Iran was developed and applied to explore key policy concerns, current performance, and barriers to development. A mixed-method approach was used. Firstly a survey was used to scope system elements from the input of 94 expert stakeholders and to establish related issues. An insight of the difference between university and industry actors' views regarding problems of UIC effectiveness was also formed. Secondly, 25 semi-structured interviews with stakeholders were used for building the conceptual model of the UIC system. Quantitative data were analyzed with Mann-Whitney U test, and for analyzing interviews, theme analysis was applied. Analysis of the causal relationships in the UIC model indicated there are a range of barriers that maintain a predominant negative behaviour pattern that limits UIC performance. This negative equilibrium is manifest as significant lack of trust at all interfaces in the system. The systems model is a complex interaction of reinforcing loops that emphasizes the scale of challenge policy-makers face in creating effective UIC outcomes. A set of policy choices informs an improvement agenda for UIC activities in Iran. This method is a framework to address the current lack of effective approaches to aid understanding of the complexity of behavioural forces that can help politicians to form coherent policies that address often hidden systematic biases.

This research focuses on the consequence of poor understanding of the social phenomenon of innovation and the effect immature social infrastructure can have in limiting the benefits of proximity and prevent the entrepreneurial process of knowledge spill-over opportunities.Interviews of system actors in technology firms in the new cluster city of Cyberjaya (Malaysia) revealed they had low levels of interaction amongst the system communities and weaker relationship with local universities than local government agencies. The research contributes to the theoretical concept of proximity, where a lack of richness of a social infrastructure and low density of informal (unplanned) social networks influence the proximity benefits and limits the opportunity density of entrepreneurs knowledge spill-over.For policy implications, this research highlights developing deeper collaborative relationships with universities, reducing the dependency on local public authorities and investing in a richer social infrastructure; or utilizes existing mature towns/cities in preference to greenfield developments.