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Although there has been extensive research on business incubators (BIs) in the private sector, relatively little is known about university-based business incubators (UBIs) in the context of emerging markets such as the United Arab Emirates (UAE). This study explores the current state of UBIs in the UAE by analysing secondary and primary data obtained through desk-based research and qualitative semi-structured interviews with UBI managers, academics and support staff. Informed by this study comprising nine cases of UBIs in the UAE, the data were collected through Voice over Internet Protocol and telephone during March 2021–February 2022 during the COVID-19 pandemic. Based on the analysis of data using Template Analysis, the findings illustrate that UAE universities’ BIs are relatively young and characterized by eight key themes, which were identified as (a) attracting new students, (b) improving communication skills, (c) curriculum development, (d) extra-curricular development, (e) industry engagement, (f) innovation/spinoffs, (g) research skills and (h) strategic thinking. As per the UAE Vision 2031, UBIs will play a crucial role in innovation and entrepreneurship, and therefore there are implications for academics, entrepreneurs and policymakers.

Broadband connectivity is now essential to ensure a competitive advantage for any business. The analysis of Scotland’s crucial IT infrastructure contribution supported the authors’ thesis that the Government plays a decisive role in Open Innovation ecosystems. Indeed, IT infrastructures are a clear case of market failure where remote areas will never be served by adequate connectivity without public support. The main contribution is the demonstration that the benefits of public intervention are sometimes required and beneficial to correct market distortions and generate positive spillovers in terms of collaboration in Open Innovation ecosystems. Another relevant contribution is a comprehensive analysis of the consistency of the evolution of the public policies that supported the IT Infrastructure in Scotland. Therefore, pivotal is the study of this case study that can be easily generalised to many other contexts where the Government addressed market failures and, at the same time, contributed to generating collaborative environments.

Machine learning (ML) has seen a substantial increase in its role in improving operations for staff and customers in different industries. However, there appears to be a somewhat limited adoption of ML by farm businesses, highlighted by a review of the literature investigating innovative behaviors by rural businesses. A review of the literature identified a dearth of studies investigating ML adoption by farm businesses in rural regions of the United Arab Emirates (UAE), especially in the context of family-owned farms. Therefore, this paper aims to investigate the drivers and barriers to ML adoption by family/non-family-owned farms in rural UAE. The key research questions are (1) what are the drivers and barriers for rural UAE farms adopting ML? As well as (2) is there a difference in the drivers and barriers between family and non-family-owned farms? Twenty semi-structured interviews were conducted with farm businesses across several rural regions in the UAE. Then, through a Template Analysis (TA), drivers and barriers for rural UAE-based farm owners adopting ML were identified. Interview findings highlighted that farms could benefit from adopting ML in daily operations to save costs and improve efficiency. However, 16 of 20 farms were unaware of the benefits related to ML due to access issues (highlighted by 12 farms) in incorporating ML operations, where they felt that incorporating ML into their operations was costly (identified by 8 farms). It was also identified that non-family-owned farms were more likely to take up ML, which was attributed to local culture influencing family farms (11 farms identified culture as a barrier). This study makes a theoretical contribution by proposing the Machine Learning Adoption Framework (MLAF). In terms of practical implications, this study proposes an ML program specifically targeting the needs of farm owners in rural UAE. Policy-based implications are addressed by the findings aligning with the United Nations’ Sustainability Development Goals 9 (Industry, Innovation, and Infrastructure) and 11 (Sustainable Cities and Communities).

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