This study explores the trust dynamics in financial forecasting by comparing how individuals perceive the credibility of AI and human experts during significant structural market changes. We specifically examine the impact of two types of structural breaks on trust: Additive Outliers, which represent a single yet significant anomaly, and Level Shifts, which indicate a sustained change in data patterns. Grounded in theoretical frameworks such as attribution theory, algorithm aversion, and the Technology Acceptance Model (TAM), this research investigates psychological responses to AI and human advice under uncertainty. This experiment involved 157 participants, recruited via Amazon Mechanical Turk (MTurk), who were asked to forecast stock prices under different structural break scenarios. Participants were randomly assigned to either the AI or human expert treatment group, and the experiment was conducted online. Through this controlled experiment, we find that, while initial trust levels in AI and human experts are comparable, the credibility of advice is more severely compromised following a structural break in the Level Shift condition, compared to the Additive Outlier condition. Moreover, the decline in trust is more pronounced for human experts than for AI. These findings highlight the psychological factors influencing decision making under uncertainty and offer insights into the behavioral responses to AI and human expert systems during structural market changes.

Purpose: Despite substantial efforts to understand the determinants of environmental, social and governance (ESG) performance in manufacturing firms, the role of supply chain-related factors, such as customer concentration, remains underexplored. Drawing on resource dependence theory, we aim to develop a theoretical framework that examines the relationship between customer concentration, manufacturer industry competition, and ESG performance. Design/methodology/approach: To test hypotheses, this study utilizes a panel ordinary least squares regression using 10,348 samples from manufacturing firms in the U.S. between 2002 and 2020. Two-stage least squares (2SLS) regression and propensity score matching (PSM) analysis are also conducted for robustness, in addition to a supplementary analysis that disaggregates ESG into its subdimensions. Findings: While customer concentration negatively impacts manufacturing firms’ ESG performance, this relationship is moderated by manufacturer industry competition. Furthermore, depending on the level of manufacturer industry competition, customer concentration may either hinder or enhance ESG performance. Specifically, customer concentration hinders ESG performance under high manufacturer industry competition but enhances it under low manufacturer industry competition. Originality/value: This study underscores the critical yet nuanced role of customer concentration in shaping manufacturing firms’ ESG performance by introducing an external factor that frames the context for various dynamics in the supply chain.

This study investigates the relationship between institutional investors and the inventory performance of companies engaged in mergers and acquisitions (M&As). Specifically, we examine the role of institutional ownership in the selection of target firms and their post-M&A performance. Additionally, we analyze how the effects of institutional investors vary based on their investment horizons and strategies. Our regression analysis reveals that acquiring firms with a large proportion of short-term institutional investors are more inclined to target firms with low inventory levels. This tendency stems from the preference of short-term institutional investors for better current operational performance, which drives decision-makers to merge with leaner firms. In contrast, acquiring firms with long-term institutional investors are more focused on performance improvement, even if the target firms have large inventories at the time of the M&A.

Digital platforms face a fundamental strategic decision between subscription-only, advertising-only, and freemium (hybrid) monetization models. We develop a game-theoretic framework that unifies these strategies, explicitly modeling consumer heterogeneity in both willingness-to-pay and advertising disutility, while incorporating network effects through the platform’s valuation of user-base size. Our analysis yields closed-form solutions identifying optimal strategy thresholds based on advertising market conditions. We show that subscription-only dominates when advertising prices are low, advertising-only prevails when prices are high, and freemium emerges as strictly optimal in the intermediate region. Under freemium, we demonstrate strategic complementarity: both subscription fees and advertising intensity exceed their levels in pure strategies because each instrument’s effectiveness is amplified by the other through user reallocation across tiers. Network effects universally reduce monetization intensity but alter instruments’ relative sensitivities differently across regimes—when advertising prices are moderate, freemium adjusts ad length more aggressively, while the opposite holds at high prices. Critically, freemium’s profitability requires sufficient consumer heterogeneity in ad tolerance. As consumer preferences converge, the screening mechanism fails and freemium collapses to the superior pure strategy. These results provide operational guidance for platform monetization decisions and clarify when hybrid models create value beyond traditional approaches.

BACKGROUND The mobile health (mHealth) and fitness app market has grown substantially, with global downloads of health and fitness applications reaching 3.6 billion in 2024. Despite this growth, the motivational dynamics that shape within-session engagement in mission-based fitness apps remain poorly understood. Although goal-gradient theory predicts effort acceleration as users approach a reward-contingent endpoint, it is unclear whether nonlinear motivational patterns emerge across the sequential stages of a single structured exercise session. OBJECTIVE This study aimed to examine whether within-session engagement in a mission-based fitness app exhibits (a) nonlinear motivational dynamics across sequential sets and (b) goal-proximal acceleration as users approach a reward-contingent completion boundary. METHODS We analyzed large-scale behavioral log data from Daysquat, a mission-based squat fitness app in which users complete four sets of ten squats per day to earn redeemable points. Stopping behavior across the structured four-set daily mission was modeled using a discrete-time proportional hazard model with covariates including set position, previous-day activity, user tenure, and platform-level engagement. A total of 221,461 user-day observations with at least one completed set, drawn from 10,725 registered users over a 187-day observation period, were included. Motivational intensity was additionally indexed by inter-set completion intervals among sessions in which all four sets were completed (n = 132,688). RESULTS The results revealed a non-monotonic, reverse-J-shaped engagement pattern: stopping risk declined across the first three sets (HR = 0.58 at set 3 vs. set 2) but increased sharply at the reward-contingent completion boundary (HR = 49.74 at set 4). Timing analyses converged with these findings; mean inter-set intervals peaked at the midpoint transition (set 2→3: 532 s) but declined substantially during the final transition (set 3→4: 143 s), consistent with goal-gradient acceleration. Higher previous-day activity (HR = 0.28), higher platform-level engagement (HR = 0.98), and longer user tenure (HR = 1.09) were also significantly associated with stopping risk. CONCLUSIONS Salient goal structure and contingent micro-incentives systematically shape motivation within a single exercise session. These findings advance understanding of within-session engagement dynamics in mHealth apps and suggest that graduated reinforcement strategies and optional continuation missions may help sustain engagement beyond minimum prescribed exercise doses.