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The purpose of this study is to investigate conceptually and empirically the direct and indirect relationships between university social responsibility (USR), university social innovation strategy (USIS) in terms of social awareness (SA), intention for social innovation (ISI), organisational structure for social innovation (SSI) and innovativeness in social value creation (ISVC) and gaining a sustainable competitive advantage (SCA) at quality-accredited faculties of an emerging market.

A conceptual model was presented and a mixed-methods approach was exploited to fill a research gap detected in strategic corporate social innovation literature. The authors formed a data collection team that contacted all the quality-accredited public and private/international faculties, of which 109 faculties in 11 Egyptian governorates responded and their quality units filled questionnaires that were analysed by structural equation modelling. For comprehensive understanding, qualitative interviews were set to gather data from managers/leaders and teaching staff working at those faculties in quality management and community engagement practices as well as students.

Results demonstrated that USR positively and significantly influenced SCA and USIS. Further, USIS (in terms of ISI, SSI and ISVC) positively and significantly influenced SCA. However, USIS (in terms of SA) had a positive yet insignificant influence on SCA. Indirectly, USIS was found to be partially mediating USR–SCA relationship.

University leaders/staff can gain insights on how to adopt differentiation strategies, which enable their institutions to shift from being just socially responsible to becoming socially innovative by presenting solutions to social, economic, cultural, environmental and health-care problems/challenges within their communities in general and during pandemics. This can be sustained through developing innovative quality-based processes/programmes/services related to education, research and community outreach that better serve social needs to be quality-accredited and unique over their rivals.

Satisfying social needs through promoting innovative processes/services can reinforce a favourable social change.

From a cross-disciplinary perspective, the authors interwove conceptually sparse literature of strategic, operations, knowledge capacity and innovation management that studied university social innovation research area. Also, to the best of the authors’ knowledge, this is the first research that examined empirically USR–USIS–SCA relationships of quality-accredited faculties in an emerging economy during Covid-19 pandemic.

This paper explores if and how Artificial Intelligence can contribute to marketing strategy formulation.

Qualitative research based on exploratory in-depth interviews with industry experts currently working with artificial intelligence tools.

Key themes include: (1) Importance of AI in strategic marketing decision management; (2) Presence of AI in strategic decision management; (3) Role of AI in strategic decision management; (4) Importance of business culture for the use of AI; (5) Impact of AI on the business’ organizational model. A key consideration is a “creative-possibility perspective,” highlighting the future potential to use AI not only for rational but also for creative thinking purposes.

This work is focused only on strategy creation as a deliberate process. For this, AI can be used as an effective response to the external contingencies of high volumes of data and uncertain environmental conditions, as well as being an effective response to the external contingencies of limited managerial cognition. A key future consideration is a “creative-possibility perspective.”

A practical extension of the Gartner Analytics Ascendancy Model (Maoz, 2013).

This paper aims to contribute knowledge relating to the role of AI in marketing strategy formulation and explores the potential avenues for future use of AI in the strategic marketing process. This is explored through the lens of contingency theory, and additionally, findings are expressed using the Gartner analytics ascendancy model.