A recent inquiry into how Journal of Marketing Analytics articles are faring with our reader base indicates that our editorials are being downloaded quite frequently. Anyone who has published research, written a book, tweeted an idea, posted a picture, or made any attempt to be read, seen, or heard knows that numbers ultimately provide comparative information. Newer forms of information (likes, loves, anger emojis, and so on) in addition to comments allow people to get feedback, whether it is warranted or not. No matter what realm we investigate, quantitative indicators (key performance metrics) help clarify whether performance met expectations. The recent Krishen and Petrescu (2019) editorial entitled, Data-driven decision making: Implementing analytics to transform academic culture, mentions performance and productivity (merit-based allocation), demographics (intersectional and inclusive), and creativity and motivation (transformational culture) as pillars of resource allocation in organizations, specifically in universities. Expanding beyond that domain, individuals are constantly challenged to produce meaningful knowledge, spread it either locally or globally, and tie it to a performance-based reward. Organizations must then commit to fair and equitable methods of utilizing metrics and analytics over time to allocate resources.

Sharing knowledge is one of the most persistent challenges of humanity and newer methods of doing so are constantly surfacing over time. Knowledge-seeking consumers utilize traditional methods including journals, news outlets, and websites as well as social media tools such as Facebook, Twitter, and LinkedIn to gather and disseminate knowledge. In the process of sharing their knowledge, contributors and managers pay special attention to metrics such as downloads, re-tweets, likes, shares, and so on (Huang et al. 2019; Shiau et al. 2018). Social media analytics such as “likes” are trusted by consumers, a finding that can have both negative and positive consequences (Seo et al. 2019). Research also indicates that social network structure plays a major role in connecting knowledge seekers to knowledge providers and ultimately diffusing knowledge (Qiao et al. 2019). The challenge therefore lies not only in producing knowledge, but also in sharing and promoting it outside of a small network of friends. This call also expands to the production of engaged scholarship; this is the scholarship that first addresses management gaps and issues and second can be mobilized and applied in organizations (Elbanna et al. 2019; Wolfberg and Lyytinen 2017).

Figure 1 presents an expansion of the IDIKC (intersectional diverse and inclusive knowledge creation) framework (Krishen et al. 2019) which moves from knowledge creation to knowledge sharing/dissemination. This process can also be unfortunate since the creation and dissemination of knowledge from diverse and inclusive perspectives is difficult due to institutionalized power structures (Grier 2019; King et al. 2018). In this framework, even if analytics are not used as a sole determinant of the importance of knowledge, they do provide a data-driven metric (Baron & Russell-Bennett, 2016).

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Spreading knowledge with data-driven decisions

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