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Past research has generally purported and tested for a positive linear relationship between psychological capital and organizational outcomes such as firm performance. Yet, recent conceptual work has started to recognize that for certain outcomes, too much psychological capital can be as detrimental as too little. In this study, the author hypothesizes that during a major crisis, organizational psychological capital (OPsyCap) may in fact exhibit an inverted U-shaped relationship with performance.

T leverages the revelatory power of a recent major crisis (the COVID-19 pandemic) to gather a pre-crisis and post-crisis matching sample of 952 earnings conference calls held by 476 S&P 500 firms with corresponding market performance data and use computer-assisted text analysis (CATA) methodology to assess OPsyCap from call transcripts.

T finds that OPsyCap has a statistically significant inverted U-shaped relationship with market performance after the crisis, but not prior—thereby suggesting that moderate OPsyCap is more beneficial to market performance than either insufficient or excessive OPsyCap in times of crisis.

Top managers should not display overly excessive psychological capital after a major crisis, as shareholders may interpret such cues as unwarranted optimism, overconfidence and an inability to accept the new reality brought about by the crisis.

This study's findings contribute to extant literature by being the first to empirically highlight a curvilinear relationship between psychological capital and an important outcome variable—market performance. Furthermore, this study's lack of results prior to a major crisis, but not after, may suggest a new boundary condition.

In the rapidly evolving digital economy, businesses face formidable pressures to maintain their competitive standing, prompting a surge of interest in the intersection of artificial intelligence (AI) and digital transformation (DT). This study aims to assess the impact of AI technologies on corporate DT by scrutinizing 3,602 firm-year observations listed on the Shanghai and Shenzhen stock exchanges. The research delves into the extent to which investments in AI drive DT, while also investigating how this relationship varies based on firms' ownership structure.

To explore the influence of AI technologies on corporate DT, the research employs robust quantitative methodologies. Notably, the study employs multiple validation techniques, including two-stage least squares (2SLS), propensity score matching and an instrumental variable approach, to ensure the credibility of its primary findings.

The investigation provides clear evidence that AI technologies can accelerate the pace of corporate DT. Firms strategically investing in AI technologies experience faster DT enabled by the automation of operational processes and enhanced data-driven decision-making abilities conferred by AI. Our findings confirm that AI integration has a significant positive impact in propelling DT across the firms studied. Interestingly, the study uncovers a significant divergence in the impact of AI on DT, contingent upon firms' ownership structure. State-owned enterprises (SOEs) exhibit a lesser degree of DT following AI integration compared to privately owned non-SOEs.

This study contributes to the burgeoning literature at the nexus of AI and DT by offering empirical evidence of the nexus between AI technologies and corporate DT. The investigation’s examination of the nuanced relationship between AI implementation, ownership structure and DT outcomes provides novel insights into the implications of AI in the diverse business contexts. Moreover, the research underscores the policy significance of supporting SOEs in their DT endeavors to prevent their potential lag in the digital economy. Overall, this study accentuates the imperative for businesses to strategically embrace AI technologies as a means to bolster their competitive edge in the contemporary digital landscape.