Special Section: Digital Strategies for Business Readiness

Introduction

The Special Section presents a set of papers that address digital support for business readiness. This is a process capability that enables firms to effectively deal with strategic and operational issues, and competitive and regulatory challenges in the present and the future economic landscape. The individual papers present contemporary problems that the authors analyze with emerging research perspectives to offer solutions supported by leading scholarship. This issue emphasizes new theoretical ideas from marketing, management science, operations management, and the design of digital artifacts and their new capabilities. The business problems and research perspectives that the authors employ reflect interdisciplinary scholarship on important issues, and leverage methodologies across different disciplines: modeling, propositions, and proofs in IS and economics; advanced econometrics; and systems dynamics modeling and simulation.

Digital Device Bundling Strategy

We all use a variety of digital devices to interact with our software, online services, music, videos, games, news, and other digital content. For instance, Netflix videos and Kindle e-books, initially accessed only on TVs and computers, are now available on smartphones and tablets as well [4]. Print products are distributed and consumed not only in print but also digitally. As a result, for consumers, it is no longer just about choosing which format they consume in; rather, it is about wanting the same content multiple times on multiple devices via different channels. Firms accordingly respond with product line designs that include prices for single-device access as well as bundle discounts for multi-device access [9].

The first article, “Multi-Device Consumption of Digital Goods: Optimal Product Line Design with Bundling,” is authored by Hemant Bhargava, who provides guidelines for product line design and pricing. Bundling, the practice of selling multiple products as one bundle for a single price, is common in markets for information goods. While earlier economic research focused on bundling’s role in reducing consumer heterogeneity [1–3], later research in information systems (IS) has explored idiosyncratic characteristics of information goods and markets (e.g., easy reproducibility, piracy, and network effects) that can confound a seller’s bundling decision [13, 22]. Extending this recent stream, the author examines the factors that can influence the firm’s multi-device bundling decision. Among such factors is the sub-additivity of consumer valuations.

In the bundling literature, a consumer’s valuation for the bundle is assumed to be the sum of his valuations for individual products. In the setting specified in the article, a consumer’s valuation for two different devices or channels (say, smart TV access and mobile app access) is typically less than the sum of his valuations for the individual channels (i.e., valuations for only TV access and only mobile app access). This is because the content is the same for both channels, and only the ease of access increases with the ability to use multiple devices [24].

Formally, the more sub-additive are the valuations, the less is a consumer’s willingness to purchase both channels. To understand the implications of such confounding factors, the author considers a range of strategies, from offering prices for single-device consumption alone (no bundling) to offering a bundle discount (mixed bundling), only multi-device access (pure bundling), and in between (partial bundling).

Picking the best strategy is a hard problem because of the way factors such as sub-additivity impact the relationship between single-device and multi-device demand functions. The best design in the end is tied to the choice of the so-called bundle discount, the amount a consumer saves by buying the bundle instead of buying the channels separately. In a two-channel scenario, a larger bundle discount entices more consumers to buy both channels but also results in a higher lost revenue as consumers who would have purchased both in any case can now do so at a lower price. A surprising insight that emerges from this tradeoff is that enticing consumers through bundle discounts can be profitable even when sub-additivity is high and the intent to consume multiple channels is quite weak. This is because, in such a situation, bundle sales would not occur organically. Less surprisingly, inducing bundle sales via discounts is also profitable when the intent to consume both channels is strong, while it is least effective when the intent is moderate.

The author also considers the case in which the two channels exhibit significantly different demand profiles. So, when one of the devices is an emerging one or has weak demand in the short term, it becomes optimal to offer a partial bundle, so that consumers either purchase this emerging channel or the bundle and do not buy the other channel alone. In addition, as the author notes, “a high level of sub-additivity in bundle valuations makes partial bundling more attractive.” Finally, if the device valuations are such that consumers generally agree on the rank-ordering of the devices (e.g., if a location-based app offers greater value to every consumer on a smartphone than on a tablet), the seller is better off not offering any bundle discount. The author also discusses factors such as correlated valuations and consumer heterogeneity with respect to sub-additivity. As a result, its insights are generalizable and applicable to a number of digital goods markets.

Product Recommendation Strategy and Seller-Biased Recommendations

We now segue from bundling economics to the theory of strategic product recommendation, and the nuances that arise when a product or service provider rethinks its approach about how to push a more profitable product to consumers, who exhibit heterogeneity with respect to their fit with it.

Online platforms and markets provide sellers unique ways of modifying a consumer’s consideration set for what to buy. Using a recommendation system, for example, a seller can nudge potential consumers to a high-margin product, instead of simply recommending the product that would have offered them the highest utility [28, 29]. Although recommender systems and their economic impacts have been studied extensively [10, 16, 21], such seller-biased recommendations have not received adequate attention. In their article titled “Product Recommendation and Consumer Search,” the authors, Vidyanand Choudhary and Zhe (James) Zhang, address this gap by examining how consumers might strategically respond to biased recommendations. They also consider the eventual impact of their responses on the firm’s optimal recommendation strategy, private profits, and the social welfare it creates.

The research is motivated by sophistication online video-streaming sites such as YouTube that make money primarily from advertisements placed within videos [e.g., 5, 7]. Consider such a service and suppose that some of the videos in its collection are high margin, that is, they earn more advertisement revenue per view compared to others. How often should the online service then recommend these high-margin videos to potential consumers? This is a difficult question to answer, and it depends critically on consumers’ search behavior. Specifically, if the recommended video is not of much interest to a consumer, they would likely turn down the recommendation. Not only that, if searching for a suitable alternative is costly to the consumer, they could simply leave the site without watching anything, resulting in a lost sale. This way, the strategic interaction between product recommendation and consumer search becomes paramount to understanding the optimal recommendation strategy. From this perspective, the authors also augment existing research on consumer search [1, 2, 20].

The analytical model the authors use considers two representative products, one of which is high margin and the other low. Preference-wise, consumers could be near one of these products, or somewhere in the middle with no particular affinity for either. The optimal strategy depends both on the search cost faced by consumers as well as the accuracy of information the seller possesses about consumer preferences. The incentive to deliberately recommend the high-margin product to consumers interested in the low-margin video has a non-monotonic relationship with the search cost. As noted by the authors, “An increase in search costs can lead to non-monotonic changes in the firm’s recommendation strategy, causing an increase or decrease in recommendation bias.” Not only the bias, but also the profit is non-monotonic in search costs. When these costs are low, the profit is increasing, but when the costs are high, the relationship is inverted. This is because searching is a double-edged sword. On one hand, if searching is difficult and therefore costly, consumers unwilling to accept the recommendation could simply leave the site instead of looking for alternatives. On the other, if searching is too easy, consumers would always ignore recommendations and search for their preferred video, making it impossible for the streaming service to push the high-margin video to anyone who prefers the low-margin one. An immediate implication is that the service provider will not always have an incentive to improve search functionality on its website [cf. 19].

Regarding the role of consumer information, more accurate knowledge and greater informedness about consumer preferences [17] predictably helps the service provider on the site increase biased recommendations and rake in larger profits, though there are limits to what personal information is accessible (e.g., considering increasingly pervasive doxxing) when individuals lose control of their private information to others. However, counterintuitively, its gain does not necessarily come at the expense of consumers. Thus, information is a double-edged sword. Just as it can help the seller increase biased recommendations, it can also reduce the vendor’s lost sales, thereby mitigating lost consumer welfare. An unexpected implication is that privacy-preserving legislation – such as Europe’s General Data Protection Regulation (GDPR) may not always be in the interest of consumers. These results are interesting, but are also generalizable to multi-period, multi-product practical settings when a consumer visits the same site periodically.

Blockchain-Based Tokenization and Social Media Contribution Incentivization

As popular social networking sites are getting increasingly mired in issues related to censorship and fake news [6], new platforms that leverage blockchain technology are emerging to take their place. In addition to making the platforms more decentralized—and therefore freer—blockchain offers other important benefits, such as full transactional transparency and verifiability of user activities, making it possible to incentivize users to contribute toward both content creation and curation [12]. Since blockchain allows each user to track new posts, up- and down-votes, and cryptocurrency payments, blockchain-based social networking and media sites (BOSMs) have the potential to drive incentive-based ecosystems that generate tremendous value for the individual users as well as broader society. Among the many platforms that fall in this category are Lit, HyperSpace, Sapien, SocialX, Foresting, Peepeth, Minds, and Earn, with some like Steemit having more than a million users.

Designing an effective blockchain-based incentive system is easier said than done though. First, one ought to ask if tokenization is generally effective in its various applied contexts [8] for social media participation and contribution incentives. Second, if it is effective, does it impact humans and bot users in a similar way? Perhaps more importantly, how do humans and bots influence each other? In their article “The Impact of Bot Involvement in an Incentivized Blockchain-based Online Social Media Platform,” the authors, Fatemeh Delkhosh, Ram D. Gopal, Raymond A. Patterson, and Niam Yaraghi, investigate these questions, uncovering not only the empirical relationship between rewards and user activities but also the interrelationship between human and bot activities. Thanks to their rich dataset collection from multiple platforms, the authors are able to use sophisticated econometric estimation methods such as panel vector autoregression, which elegantly capture the dynamics of content curation, including up-voting and down-voting following a new post.

Evidently, both humans and bots respond to pecuniary incentives in an expected manner. Up-voting and down-voting by bots significantly influence subsequent human voting. This way, bots have a lasting impact on the net number of up- or down-votes. Interestingly, bots also engage in front-running, like traders at the highly technological hedge firms [11, 18], who get ahead of other of other traders to get the best prices [14, 15]. They do this by voting before human users start reacting in formidable numbers; this allows the bots to collect a significant portion of the total reward allotted for content curation.

At the same, bot activity is not significantly impacted by human users, suggesting that bots act in a somewhat premeditated manner. Humans, in contrast, are more reactive, in the sense that they react not only to bot activity but also to voting by other human users. A related finding is that reducing the reward for authoring can diminish overall bot participation in curation. This suggests that some authors probably pay bots for up-votes, and when the reward for authoring is reduced, such payments to bots may decrease. Thus, the rewards for authoring and curating play a critical role in determining the interrelationship between bot and human activities, and the overall success of the curation process. Such insights are not only generalizable, but they also form the basis for designing and fostering BOSM ecosystems.

Platform Launch and Participant Ecosystem Development

The final article in this section was contributed by Edward G. Anderson, Jr., Geoffrey G. Parker, and Burcu Tan. The authors characterize the problem of launching and growing a new platform to support a firm’s business, while working to simultaneously grow the accompanying ecosystem of buyers and suppliers, to ensure that it remains strategically sustainable and confers benefits on its participants. Their article is entitled “Strategic Investments for Platform Launch and Ecosystem Growth: A Dynamic Analysis.” The reason they believe this is a hard problem for management is that “ … multi-sided platforms must make decisions on both pricing and engineering investment and must continually adjust them as the platform scales over its lifecycle.”

The authors employ a blend of economic modeling and business dynamics analysis [25, 26], which is underlain by the earlier analytic methods associated with systems dynamics and the modeling of complex and wickedly difficult real-world problems [23]. To make an argument on the relevance of these methods, John Sterman [27], a leading methodology author, has written:

Thoughtful leaders increasingly recognize that we are not only failing to solve the persistent problems we face but are in fact causing them. System dynamics is designed to help avoid such policy resistance and identify high‐leverage policies for sustained improvement. What does it take to be an effective systems thinker, and to teach system dynamics fruitfully? Understanding complex systems requires mastery of concepts such as feedback, stocks and flows, time delays, and nonlinearity. Research shows that these concepts are highly counterintuitive and poorly understood. It also shows how they can be taught and learned. Doing so requires the use of formal models and simulations to test our mental models and develop our intuition about complex systems. Yet, though essential, these concepts and tools are not sufficient.

The present article’s authors identified an important gap in the past literature: ongoing engineering investments in a platform’s standalone functionality and capabilities are necessary to effectively harvest the benefits of network effects the platform creates. But this also requires the integration of support tools and resources on the platform operator’s boundary that can effectively connect it with other third-parties that create content for the platform. This, in turn, is attractive for other platform participants. As a result, a “gray area” for platform-focused strategic thinking is attributable to the related network effects not being included in most models of platform growth and the well-being of their participant ecosystems. Accordingly, the authors argue that it is appropriate to spend time for gauging “how to best balance tradeoffs between different strategic decisions throughout the entire platform lifecycle.” They point out that their primary purpose is to address the gap by developing a continuous model that enables their exploration of different ecosystem conditions that characterize a platform’s installed base for participants in its ecosystem.

Their modeling approach incorporates economic constructs, along with marketing-related parameters. They use this as a basis for conducting sensitivity analyses that produce several results that apply to their case conditions. In particular, the show the efficacy of trying to perform an optimal control analysis that monitors platform investments and changing price policies over time. They further report that optimal platform strategy requires agile “gear shifts” by the operator, to take advantage of platform participation monetization and how averse market participants are to price changes that occur. They report that their methods suggest each segment is unique in its response to changing conditions. This applies to the platform business segments that they simulate and study, including mobile, social media, the sharing economy, and business-to-business transaction-making platforms. Finally, a platform’s pricing engineering investment decisions are cointegrated to the extent that none can be effectively considered in isolation, or as if passing time isn’t a controlling factor in the operators’ performance outcome.

Final Thoughts

An interesting—but no longer surprising aspect of the authors’ work—is the extraordinary attention they have given to the underlying theories they use to drive the focus of their analyses and craft useful knowledge for management, consultants, tech firm leaders, and university-based researchers. Earlier in our careers, we participated in university seminars and research conferences in which senior faculty expressed their concern about the uniqueness of the theoretical content in IS research, the generalizability of its contributions, and the usefulness of its findings for business leaders and managers. The call in the IS discipline at the time (twenty-plus years ago) was for the development of relevant theory that would span the boundaries of our discipline and be useful to researchers and practitioners in other disciplines.

The contents of this Special Section demonstrate how far the research in the IS discipline has come as of 2023—in its contributions to interdisciplinary scholarship, and the extent to which it offers meaningful thought leadership to business professionals and organizational leaders. Indeed, we expect that readers of JMIS and other leading IS research journals will recognize that the 2010s and the present decade of the 2020s have been extraordinary times for IS scholarship. Theory and applications, modeling and empiricism, and rigor and relevance have come into harmonic balance with one another more than before – across economics and IS, and other interdisciplinary areas of our field, too. Thus, we think it is fitting to title this Special Section in recognition of digital strategies for business readiness that our collective research inquiry now supports, considering the future-focused perspective on strategy, information, technology, economics, and society.

Additional information

Notes on contributors

Robert J. Kauffman

Robert J. Kauffman ([email protected]; corresponding author) holds the Endowed Chair in Digitalization at the Copenhagen Business School and is Emeritus Professor of Information Systems at Singapore Management University. His graduate degrees are from Cornell University and Carnegie Mellon. Over the years, his research has focused on technology and strategy, the economics of IT, financial services and technology, managerial decision-making, sustainability economics, and e-commerce. He previously served as Associate Dean (Faculty) and Associate Dean (Research), and Chair of the IS and Management Area at SMU’s School of IS. He was also the W.P. Carey Chair in IS at Arizona State University, and Professor and Director of the MIS Research Center at the Carlson School of Management of the University of Minnesota, where he chaired the Information and Decision Sciences Department. Dr. Kauffman was a visiting researcher and faculty member at the Economics Department of the Federal Reserve Bank of Philadelphia, the Simon Graduate School of Management at the University of Rochester, the School of Economics and Management at Tsinghua University, and the Tuck School at Dartmouth College. His work has appeared in Organization Science, Management Science, Review of Economics and Statistics, Energy Policy, Information Systems Research, Journal of Management Information Systems, MIS Quarterly, Journal of the Association for Information Systems, IEEE Transactions on Software Engineering, among many others. He has won field research contribution and research awards from professional associations for Information Systems, Engineering Management, and Management Science.

Atanu Lahiri

Atanu Lahiri [email protected] is an associate professor at the Jindal School of Management, University of Texas at Dallas. He received his Ph.D. from the Simon Business School, University of Rochester. Dr. Lahiri’s research interests are at the intersection of IS and economics. His work has appeared in such journals as Information Systems Research, Journal of Management Information Systems, Management Science, MIS Quarterly, INFORMS Journal on Computing, and Manufacturing & Service Operations Management. He has received the Sandra A. Slaughter Early Career Award from the Information Systems Society of INFORMS. Dr. Lahiri serves as an associate editor for Information Systems Research and Journal of the Association of Information Systems.