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The sharing economy is a collection of economic and social activities where participants of the community share properties, resources, time and skills across online platforms. In this chapter, we start by identifying all the stakeholders and their characteristics within such an ecosystem. We then categorise factors leading to success in the sharing economies where the existence of these platforms has disrupted traditional businesses. To do so, demographic information about the community participants, specifications of the business models, enablers of the ecosystem, growth drivers and hindrance factors are explored in detail. From there on, we examine whether such success factors are applicable in the Turkish business environment where Internet retailing is in its infant stages, trust among people is quite low and economic welfare is lower than that of more developed economies. Finally, an assessment of the sharing economy landscape in Turkey is provided at the end of the chapter.

To outline the future of the sharing economy in Turkey, success indicators in the Turkish market are compared and contrasted with those of the United States, the United Kingdom and Brazil. A quick analysis reveals that despite its huge potential, Turkey still has not reached its full capacity in Internet usage, online or mobile retailing. That said, notwithstanding the low levels of trust among people, Turkey has a great potential of sharer base, given the demographic structure of its citizens. Recommendations for policy makers, incumbent firms, the sharing economy startups and marketers are provided in the chapter.

The aim of this chapter is to shed light on the perspectives of organizational innovation in higher education in light of socio-economic transformations as articulated in Vision 2030 for Saudi Arabia. A case study evaluating strategic innovation at the Arabian Gulf University (AGU) in Bahrain was conducted using Balanced Score Cards. A questionnaire was designed to capture enablers and barriers in organizational innovation in higher education. The chapter concluded that institutional inertia, limited implementation and evaluation processes are the key impediments for organizational learning and knowledge management. The study recommends to incorporate organizational innovation to foster entrepreneurship, strategic innovation and learning at higher education institutions (HEI).

The presence and practice of individual and organizational humility has the power to enable organizational growth and change. Humility drives behaviors associated with learning and the ability to embrace the value of existing mental models while valuing the insights offered by new perspectives and approaches. This paradox-savvy practice, observed in humble individuals and organizations, allows them to appropriately value what is working about the existing system while simultaneously embracing the need for change. Our research finds humble behaviors emerging within psychologically safe environments that foster an attitude of inquiry, kinship, extraordinary collaboration, and professional excellence. Humble behaviors, at every organizational level, appear to enhance both individual and group capabilities that drive long term strategic advantage. Five capabilities were identified in our research: diverse networks, shared values, flexibility and adaptability, judgment and decision-making, and organizational learning. We bring these concepts to life by synthesizing established and emerging research, as well as diving deeply into an empirical case study that leverages humble practices in order to effectively drive organizational change. We argue that humility can impact organizing at all levels (individuals, leaders, followers, teams, executives, and organizations) and in so doing create the conditions in which sustainable organizational change can flourish.

This chapter introduces the term “interpreneurship” to refer to entrepreneurship that occurs through inter-organizational alliances, which represent a salient vehicle for combining complementary resources and capabilities across firms in order to gain a competitive advantage. The interpreneurship concept implies the integration of internal (firm) and external (network) resources through alliance formation and management. The purpose of this research is to introduce social structure to the rational action paradigm by examining the complementarity of entrepreneurial and relational resources in achieving organizational goals in an alliance context. In this study, interpreneurial capability is operationalized as the combination of entrepreneurial resources (via an internal growth strategy) with relational resources (via an external growth strategy). These effects are assessed through the examination of three competing research models. The hypothesized interaction-only model tests the impact of complementarity of entrepreneurial and relational resources on firm-level performance for both partners to an alliance. A second model tests relational resources as a mediator of the relationship between entrepreneurial resources and the alliance partners’ performance. Finally, a third model assumes that the two resources have independent effects on the alliance partners’ performance. We find that the interaction-only model yields the strongest relationship to organizational performance, supporting the interpreneurial perspective we proffer in this chapter.

Advances in Big Data, artificial Intelligence and data-driven innovation bring enormous benefits for the overall society and for different sectors. By contrast, their misuse can lead to data workflows bypassing the intent of privacy and data protection law, as well as of ethical mandates. It may be referred to as the ‘creep factor’ of Big Data, and needs to be tackled right away, especially considering that we are moving towards the ‘datafication’ of society, where devices to capture, collect, store and process data are becoming ever-cheaper and faster, whilst the computational power is continuously increasing. If using Big Data in truly anonymisable ways, within an ethically sound and societally focussed framework, is capable of acting as an enabler of sustainable development, using Big Data outside such a framework poses a number of threats, potential hurdles and multiple ethical challenges. Some examples are the impact on privacy caused by new surveillance tools and data gathering techniques, including also group privacy, high-tech profiling, automated decision making and discriminatory practices. In our society, everything can be given a score and critical life changing opportunities are increasingly determined by such scoring systems, often obtained through secret predictive algorithms applied to data to determine who has value. It is therefore essential to guarantee the fairness and accurateness of such scoring systems and that the decisions relying upon them are realised in a legal and ethical manner, avoiding the risk of stigmatisation capable of affecting individuals’ opportunities. Likewise, it is necessary to prevent the so-called ‘social cooling’. This represents the long-term negative side effects of the data-driven innovation, in particular of such scoring systems and of the reputation economy. It is reflected in terms, for instance, of self-censorship, risk-aversion and lack of exercise of free speech generated by increasingly intrusive Big Data practices lacking an ethical foundation. Another key ethics dimension pertains to human-data interaction in Internet of Things (IoT) environments, which is increasing the volume of data collected, the speed of the process and the variety of data sources. It is urgent to further investigate aspects like the ‘ownership’ of data and other hurdles, especially considering that the regulatory landscape is developing at a much slower pace than IoT and the evolution of Big Data technologies. These are only some examples of the issues and consequences that Big Data raise, which require adequate measures in response to the ‘data trust deficit’, moving not towards the prohibition of the collection of data but rather towards the identification and prohibition of their misuse and unfair behaviours and treatments, once government and companies have such data. At the same time, the debate should further investigate ‘data altruism’, deepening how the increasing amounts of data in our society can be concretely used for public good and the best implementation modalities.