Marius Florin Niculescu

In software markets, the sheer number of available applications make it rather challenging for any given new one to stand out and be noticed by consumers. Moreover, a push towards privacy by regulators and consumers is making it harder to target consumers. As such, firms have to rely on more non-targeted go-to-market strategies. We explore two popular strategies through which developers can catalyze adoption by helping consumers directly or indirectly learn the value of their products-seeding (free full-feature product giveaways to a subset of the consumer base) and time-limited freemium (TLF). Seeding, as a business strategy, existed for a long time. On the other hand, the feasibility to offer market-wide TLF became mainstream more recently, with the advent of the Internet and a plethora of digital tools. Thus, a natural question emerges-if TLF represents nowadays a feasible and easily implementable strategy for software applications, has seeding approach been rendered irrelevant in these markets? In this study, we provide managerial recommendations on when each of these strategies with a free full-feature-consumption component is optimal, based on social and self-learning dynamics, consumer priors, adoption costs, and individual product value depreciation. To that end, under a multi-period parsimonious unifying framework, we show that S becomes dominated as free trials enter the picture. We identify two specific market factors that, when present, can induce seeding to be optimal when consumers initially underestimate true product value - (i) user adoption costs and/or (ii) individual depreciation of value by usage. Moreover, we show that these two factors have a moderating effect on the impact of word-of-mouth (WOM) effects on the optimality of seeding. In the absence of these factors, stronger WOM effects alone cannot give seeding an edge against the other business strategies. However, once either depreciation or adoption costs are accounted for, strong WOM effects increase the relevance of seeding (enlarging its optimality region in the parameter space). Our results remain qualitatively consistent under a battery of robustness checks.

In software markets, the sheer number of available applications makes it rather challenging for any given new one to stand out and be noticed by consumers. Moreover, a push towards privacy by regulators and consumers is making it harder to target consumers. As such, firms have to rely on more non-targeted go-to-market strategies. We explore two popular strategies through which developers can catalyze adoption by helping consumers directly or indirectly learn the value of their products— seeding (free full-feature product giveaways to a subset of the consumer base) and time-limited freemium ( TLF ). Seeding, as a business strategy, existed for a long time. On the other hand, the feasibility to offer market-wide TLF became mainstream more recently, with the advent of the Internet and a plethora of digital tools. Thus, a natural question emerges—if TLF represents nowadays a feasible and easily implementable strategy for software applications, has seeding approach been rendered irrelevant in these markets? In this study, we provide managerial recommendations on when each of these strategies with a free full-feature-consumption component is optimal, based on social and self-learning dynamics, consumer priors, adoption costs, and individual product value depreciation. To that end, under a multi-period parsimonious unifying framework, we show that S becomes dominated as free trials enter the picture. We identify two specific market factors that, when present, can induce seeding to be optimal when consumers initially underestimate true product value—(i) user adoption costs and/or (ii) individual depreciation of value by usage. Moreover, we show that these two factors have a moderating effect on the impact of word-of-mouth (WOM) effects on the optimality of seeding. In the absence of these factors, stronger WOM effects alone cannot give seeding an edge against the other business strategies. However, once either depreciation or adoption costs are accounted for, strong WOM effects increase the relevance of seeding (enlarging its optimality region in the parameter space). Our results remain qualitatively consistent under a battery of robustness checks.

Cryptocurrencies have prompted a shift away from classic security attacks toward ransomware-based extortion. To better understand the impact of cryptocurrencies on the cybersecurity landscape, we conduct a comparative analysis of cybersecurity metrics prior to and after the adoption of cryptocurrency using a series of connected software-use models in the presence of security externalities. In this framework, we endogenize the actions of both heterogeneous consumers and attackers, with entry of the latter being driven by both the size of the unpatched consumer population and, as a subset of it, the size of the ransom-paying consumer population. We first examine users’ adoption and patching behavior under both security scenarios. We explore how changes in attacker entry costs impact outcomes under both conventional and post-crypto ransomware threat landscapes. We show that ransomware scenarios may be more desirable than conventional ones when attacker entry costs are low, provided that the gains from entering with standard attacks under the ransomware scenario are not too high. However, under such scenarios, social welfare can increase under the same conditions that lead to larger ransoms being demanded and a higher expected total ransom being paid, which presents a conundrum to policymakers. We also examine the impact of market parameters associated with security losses from conventional attacks and residual losses when victims pay in ransomware attacks. This paper was accepted by Kay Giesecke, finance. Funding: This work was partially supported by Insung Research Grant of KUBS, the LG Yonam Foundation (of Korea), and an award from the Georgia Institute of Technology Center of International Business Education and Research as part of its funded research program. Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2023.00969 .

Recently, the development of ransomware strains and changes in the marketplace for malware have greatly reduced the entry barrier for attackers to conduct large-scale ransomware attacks. In this paper, we examine how this mode of cyberattack impacts software vendors and consumer behavior. When victims face an added option to mitigate losses via a ransom payment, both the equilibrium market size and the vendor’s profit under optimal pricing can actually increase in the ransom demand. Profit can also increase in the scale of residual losses following a ransom payment (which reflect the trustworthiness of the ransomware operator). We show that for intermediate levels of risk, the vendor restricts software adoption by substantially hiking up price. This lies in stark contrast to outcomes in a benchmark case involving traditional malware (non-ransomware) where the vendor decreases price as security risk increases. Social welfare is higher under ransomware compared with the benchmark in both sufficiently low- and high-risk settings. However, for intermediate risk, it is better from a social standpoint if consumers do not have an option to pay ransom. We also show that the expected ransom paid is nonmonotone in risk, increasing when risk is moderate despite a decreasing ransom-paying population. For ransomware attacks on other vectors (beyond patchable vulnerabilities), there can still be an incentive to hike price. However, market size and profits instead weakly decrease in the ransom amount. When studying a generalized model that includes both traditional and ransomware attacks, our results remain robust to a wide range of scenarios, including threat landscapes where ransomware has only a small presence. This paper was accepted by Kartik Hosanagar, information systems. Funding: This work has been supported by the Haskayne School of Business' Dean's Research Grant and by an award from the Georgia Institute of Technology Center of International Business Education & Research as part of its funded research program. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4300 .

In this paper, we explore the strategic decision of an incumbent to open a proprietary technology platform to allow same-side co-opetition in a market characterized by network effects. We propose a game-theoretic model that analytically conceptualizes the interplay among the degree of same-side platform openness, the absorptive capacity of the entrant, and the intensity of network effects. Our analysis uncovers interesting new results. First, when entrant product quality is exogenous, under very strong network effects, the incumbent closes the technology. Moreover, we discuss various interesting open-platform co-opetition outcomes that arise under a fully covered market. When the entrant chooses the quality level and the incumbent is strategic in its platform opening decision, we find that intense network effects make new players shun the market, so intellectual property (IP) sharing is not possible in equilibrium. When the network effects are of intermediate intensity, the incumbent opens the technology to the entrants who have a sufficiently high absorptive capacity, calibrating the amount of sharing to the entrant’s absorptive capacity level to ensure that the duopoly setting is mutually beneficial. Our key findings and insights are robust to several model extensions, including scenarios when the incumbent is uncertain of the entrant’s absorptive capacity, or when the entrant incurs a general non-linear development cost structure. We also compare and contrast bounded versus unbounded market scenarios. We further explore the ability of the incumbent to engineer the strength of network effects in the market and uncover non-trivial alternating-monotonicity patterns for the optimal intensity of network effects with respect to the entrant’s absorptive capacity. We also show that a model with exogenous network effects could drastically underestimate the range of entrants’ absorptive capacity values for which the incumbent should open its platform, causing the latter to miss valuable co-opetition opportunities. We also discuss various managerial implications of our theoretical framework.The online appendix is available at https://doi.org/10.1287/isre.2017.0756 .

In this paper, we explore the strategic decision of an incumbent to open a proprietary technology platform in order to allow same-side co-opetition in a market

Software producers are making greater use of customer error reporting to discover defects and improve the quality of their products. We study how software development differences among producers (e.g., varying levels of process maturity) and software class and functionality differences (e.g., operating system versus productivity software) affect how these producers coordinate software release timing and pricing to optimally harness error reporting contributions from users. In settings where prices are fixed, we characterize the optimal release time and demonstrate why in some cases it can actually be preferable to delay release when customer error reporting rates increase. The manner in which a firm's optimal release time responds to increases in software functionality critically hinges on whether the added functionality enhances or dilutes user error reporting; in both cases, the effect of added functionality on release timing can go in either direction, depending on both firm and product market characteristics. For example, when processing costs are relatively large compared to goodwill costs, firms with lower process maturity will release earlier when per module error reporting contributions become diluted and release later when these contributions become enhanced. We also examine how a firm adapts price with changes in error reporting levels and software functionality, and finally provide implications of how beta testing influences release timing.

In this paper we explore the underlying consumer heterogeneity in competitive markets for subscription-based IT services that exhibit network effects. Insights into consumer heterogeneity with respect to a given service are paramount in forecasting future subscriptions, understanding the impact of price and information dissemination on market penetration growth, and predicting adoption path for complementary products that target the same customers as the original service. Employing a continuous-time utility model, we capture the behavior of a continuum of consumers who are differentiated by their intrinsic valuations from using the service. We study service subscription patterns under both perfect and imperfect information dissemination. In each case, we first specify the conditions under which consumer rational behavior supported by the utility model can explain a general observed adoption path, and if so, we explicitly derive the analytical closed-form expression for the consumer valuation distribution. We further explore the impact of awareness and distribution skewness on adoption. In particular, we highlight the practical forecasting importance of understanding the information dissemination process in the market as observed past adoption may be explained by several distinct awareness and heterogeneity scenarios which may lead to divergent adoption paths in the future. Moreover, we show that in the later part of the service lifecycle the subscription decision for new customers can be driven predominantly by information dissemination instead of further price markdowns. We also extend our results to time-varying consumer valuation scenarios. Furthermore, based on our framework, we advance a set of heuristic methods to be applied to discrete-time real industry data for estimation and forecasting purposes. In an empirical exercise, we apply our methodology to the Japanese mobile voice services market and provide relevant managerial insights from the analysis.