Not only data: The necessity of abstract thinking to make sense of digital phenomena

We appreciate Grisold et al. (2022) commentary on our article Grover and Lyytinen (2022). There is much to like about the commentary: it offers strong advice and develops plausible solutions and policies of how endorse inductive digital “trace data” based platform research. This engages with and leverages the increased access to large swaths of heterogeneous digital trace data and applies powerful computational methods to analyze it. We stated already in Grover and Lyytinen (2015) such that efforts are laudable and necessary and should be pursued. We note this in (a) the debate article’s section “Barrier 2: Descriptive studies are not valued unless informed by reference theory-based framing” and (b) the section “Barrier 3: Belief that big data obfuscates the need for innovative theory” where we urge scholars to leverage computationally intensive techniques to go beyond the local data to broader and bolder abstractions (Grover and Lyytinen 2022 p 6). For us, investing in community wide efforts to leverage digital (trace) data in reporting and analyzing emergent digital phenomena is thus in line with the pursuit to rebalance the intellectual investments in the IS field (Grover and Lyytinen 2015, 2022). So, most the Grisold et al.’s proposals and policy recommendation do not contradict, nor do they rebuke our position of loosening institutional structures and norms to promote more flexibility in our theorizing approaches. The question for us is: what is the proper way to balance the field’s intellectual effort as we need to deal with deeply digital phenomena. Here, we disagree with Grisold et al. in what ways two alternative pathways—blue ocean theorizing (theory from the mind) vs. inductive/abductive theorizing (theory from data)—need to be juxtaposed during balancing. Simply, what is the right balance between the two pathways for the field’s intellectual effort in the coming decade? We object and challenge the premise of Grisold et al.’s argument to pursue only the latter because innovative theorizing is now ‘moot’ given the high fluidity of digital phenomena. Or, as they put: “we offer an alternative view: we suggest to focus less on explaining phenomena and instead concentrate on making them explainable, by finding ways to capture and represent the complexity and detail about dynamically evolving socio-technical phenomena in a precise and timely manner.such that any researcher, from within our field or from elsewhere, will be better enabled to analyze, reason, or theorize about them. ” (Grisold et al. p.1–2). Clearly, for Grisold et al. the two alternatives form a dualism (with either/or logic) and exclude one another. In contrast, we approach the two as a duality (both/and logic) where both are necessary to rebalance the field’s intellectual effort. We proposed such duality already in Grover and Lyytinen (2015) with the phrase “push to the edges. ” We repeat this idea in Grover and Lyytinen (2022) where we are clear that both approaches are necessary and useful. Though the Grover and Lyytinen (2022) centers on formulating theories of the mind (one pathway), we never posit that this pathway excludes other pathways including even drawing on reference theories when they serve their purpose. Our core justification for honoring this logic is simple. Given the fundamentally different nature of digital phenomena, we need also more embedded constructs and more powerful abstractions. Our modus operandi of relying on reference theories, treated as immutable, is not likely to work for digital phenomena—and we need to promote the tremendous latent potential of abstract thinkers by allowing more flexibility to our theorizing. This flexibility also includes digital trace data, and platform-based research, albeit with some cautionary notes that we describe in Grover, et al. (2020). Therefore, we want to rebuke Grisold et al.’s fixation to dualism. This is mainly backed up by claims of the disutility of innovative theorizing and that this comes at the expense of inductive/abductive data driven research. We back our position with two general notes that put this specific debate in a broader disciplinary perspective. The first one focuses on the general role and impact of data driven research in the evolution of disciplinary fields and what we can learn from it. The second one touches on some premises of Grisold