Journal of Economics & Management Strategy最新文献

We examine how public firms listed in North American stock exchanges acquire technology companies during 2010–2020. Combining data from Standard and Poor's (S&P), Refinitiv, Compustat, and Center for Research in Security Prices, and utilizing a unique S&P taxonomy that classifies tech mergers and acquisitions (M&As) by tech categories and business verticals, we show that 13.1% of public firms engage in any tech M&A in the S&P data, while only 6.75% of public firms make any (tech or nontech) M&A in Refinitiv. In both data sets, the acquisitions are widespread across sectors of the economy, but tech acquirers in the S&P data are on average younger, more investment efficient, and more likely to engage in international acquisitions than general acquirers in Refinitiv. Within the S&P data, deals in each M&A-active tech category tend to be led by acquirers from a specific sector; the majority of target companies in tech M&As fall outside the acquirer's core area of business; and firms are, in part, driven to acquire tech companies because they face increased competition in their core areas.

We characterize the degree of price authority that competing upstream principals award their downstream agents in a setting where these agents own private information about demand and incur nonverifiable distribution costs. Principals cannot internalize these costs through monetary incentives and design “permission sets” from which agents choose prices. The objective is to understand the forces shaping delegation and the constraints imposed on equilibrium prices. When principals behave noncooperatively, agents are biased toward excessively high prices because they pass on distribution costs to consumers. Hence, the permission set only features a price cap that is more likely to bind as products become closer substitutes, in sectors where distribution is sufficiently costly, and when demand is not too volatile. By contrast, when principals behave cooperatively, the optimal delegation scheme is richer and more complex. Because principals want to charge the monopoly price, the optimal permission set features a price floor when the distribution cost is sufficiently low, it features instead full discretion for moderate values of this cost, and only when it is high enough, a price cap is optimal. Surprisingly, while competition (as captured by stronger product substitutability) hinders delegation in the noncooperative regime, the opposite occurs when principals maximize industry profit.

This paper examines the prevalent mechanism of financing advertising and temporary price reductions through trade spend budgets. A manufacturer and a retailer interact for a number of periods with a plan to hold a sale in the last period. During the nonpromotional periods, the retailer accumulates the funds in this budget in proportion to the size of its order from the manufacturer. In the sale period, the budget is used to finance the discount offered by the manufacturer and advertising. I find that the manufacturer drops its price in the sale period to increase the profitability of promotions for the retailer. To be able to sell more units during the sale period, the retailer needs to accumulate a larger trade spend. This is accomplished by setting a smaller mark-up over the manufacturer's price in the regular periods. The manufacturer takes advantage of the retailer's softer pricing by increasing its regular wholesale price. As long as such trade spends are used to finance advertising, the total profits of each firm increase. Using fixed trade spends, where the manufacturer allocates a fixed amount for the retailer, does not lead to an increase in profits.

We use an incentivized experimental game to uncover heterogeneity in social preferences among salespeople in a large Austrian retail chain. Our results show that the majority of agents take the welfare of others into account but a significant fraction reveal selfish behavior. Matching individual behavior in the game with firm data on sales performance shows that agents with social preferences achieve a significantly higher revenue per customer. However, at the same time, they achieve fewer sales per day. Both effects offset each other, so that the overall association with total sales revenue becomes insignificant. Our findings highlight the nuanced role of selfish versus social preferences in sales contexts with important implications for economic research.

Early commercial applications of artificial intelligence technologies (AITs) were narrow but extremely profitable. Comparable uses of those technologies throughout the economy would lead to a growth boom. Firms which emulated the early applications successfully would make tremendous strategic gains. This is a situation familiar from earlier rounds of information and communication technology. However, for AITs to become a general-purpose technology across many commercial applications sectors will require some new innovations. This paper examines the innovation paths that could lead to that desirable outcome, the ones that have stalled, the ones in the process now, and the ones that might occur in the future. Strikingly, early AIT use, both commercial and with technical customers, occurred where Digital Transformation was not required for it to succeed. The innovation paths all require Digital Transformation as key steps.

We study the impact of ultra-broadband (UBB) internet connections on firm entry and exit dynamics. These connections are based on optical fiber cables that link telecommunication operators to final users, allowing a significantly higher performance compared with traditional copper-line networks. We leverage on a unique comprehensive dataset collecting municipality-level information on broadband diffusion and firm turnover in Italy for the period of 2012–2019. Our empirical strategy exploits the staggered roll-out of UBB, starting from 2015. Our identification strategy is based on an instrumental variable approach that exploits plausibly exogenous variation in the physical and geographical peculiarities of the telecommunication infrastructure. Results suggest that UBB increases firm exit, particularly for small firms. On the contrary, firm entry rises only in digital intensive sectors and in the most developed geographical areas. Our findings have important implications for the ongoing debate around the massive investments in high-speed digital infrastructures, as they argue against the conventional idea that business activities equally benefit from last-generation broadband technologies.

Analyses of artificial intelligence (AI) adoption focus on its adoption at the individual task level. What has received significantly less attention is how AI adoption is shaped by the fact that organizations are composed of many interacting tasks. AI adoption may, therefore, require system-wide change, which is both a constraint and an opportunity. We provide the first formal analysis where multiple tasks may be part of an interdependent system. We find that reliance on AI, a prediction tool, increases decision variation, which, in turn, raises challenges if decisions across the organization interact. Reducing inter-dependencies between decisions softens that impact and can facilitate AI adoption. However, it does this at the expense of synergies. By contrast, when there are mechanisms for inter-decision coordination, AI adoption is enhanced when there are more inter-dependencies. Consequently, we show that there are important cases where AI adoption will be enhanced when it can be adopted beyond tasks but as part of a designed organizational system.

The government may delegate two sequential tasks (e.g., building and operating an infrastructure) to the same or different agents (i.e., partnership vs. sequential contracts). Agents are risk-neutral but face financial constraints, whereas the government's contractual capacity may be limited by the renegotiation-proofness and fiscal constraints. By relying on history-dependent incentives, the partnership contract corrects moral hazard more effectively than sequential contracts. Thus, it is socially preferred unless bundling different tasks deteriorates the agent's financial conditions. Our results shed new light on the role of firms' financial and government's fiscal conditions in driving the cost–benefit analysis of public–private partnerships.

The behavior of artificial intelligence (AI) algorithms is shaped by how they learn about their environment. We compare the prices generated by AIs that use different learning protocols when there is market interaction. Asynchronous learning occurs when the AI only learns about the return from the action it took. Synchronous learning occurs when the AI conducts counterfactuals to learn about the returns it would have earned had it taken an alternative action. The two lead to markedly different market prices. When future profits are not given positive weight by the AI, (perfect) synchronous updating leads to competitive pricing, while asynchronous can lead to pricing close to monopoly levels. We investigate how this result varies when either counterfactuals can only be calculated imperfectly and/or when the AI places a weight on future profits. Lastly, we investigate performance differences between offline and online play.

Motivated by recent research on product differentiation, we conduct laboratory experiments to study how demand uncertainty influences firms' incentives to differentiate. We ground our experiment on a discrete version of the standard location-then-price game introduced by Hotelling (1929), and we consider different levels of demand uncertainty. We first derive the game equilibrium assuming risk-neutral firms, and obtain the standard prediction that a high level of demand uncertainty generates more differentiation. Second, we extend the analysis to consider non-risk neutral firms and markets with asymmetric risk profiles. We show that the game equilibrium can differ substantially according to the attitude to risk. Third, we compare our predictions with the experimental data and find that demand uncertainty acts as a differentiation force in the context of both symmetric markets composed of risk-neutral or risk-lover subjects and asymmetric markets. We find support also for the agglomeration effect arising from demand uncertainty for sufficiently risk-averse subjects. Overall, these results might explain the opposite product differentiation strategies frequently observed in markets with fast-evolving tastes (i.e., minimum or maximum differentiation). Finally, the data confirm that subjects differentiate to relax price competition and provide evidence of a strong positive relationship between differentiation and prices.