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The purpose of this study is to investigate (1) how managers respond to stock price crashes, (2) why they respond and (3) how their responses affect shareholders.

This study employs a panel regression with various firm-level controls and firm- and year-fixed effects. The sample is comprised of 101,532 firm-year observations with 11,727 unique firms from 1950 to 2019. Using mutual fund flow redemption pressure as an exogenous variable to stock price crashes, the paper provides further evidence of the causality of documented findings.

Management becomes more focused on improving transparency, raising investment efficiency, reducing agency conflicts and regaining the trust of shareholders by investing in social capital and employee welfare. These actions increase firm value. This study also suggests that management undertakes these actions out of concern for their tenure of employment.

The catalysts of stock price crashes are well documented, but much less is known about what happens following stock price crashes. This study provides more insights into the understanding of corporate crisis management practices following adverse events.

This study explores the complex impact of COVID-19 on India's financial sector, moving beyond simplistic public health vs. economy views. We assess market vulnerabilities and analyze how public sentiment, measured through Google Trends, can predict stock market fluctuations. We propose a novel framework using Google Trends for financial sentiment analysis, aiming to improve understanding and preparedness for future crises.

Hybrid approach leverages Google Trends as sentiment tool, market data, and momentum indicators like Rate of Change, Average Directional Index and Stochastic Oscillator, to deliver accurate, market insights for informed investment decisions during pandemic.

Our study reveals that the pandemic significantly impacted the Indian financial sector, highlighting its vulnerabilities. Capitalizing on this insight, we built a ground-breaking predictive model with an impressive 98.95% maximum accuracy in forecasting stock market values during such events.

To the best of authors knowledge this model's originality lies in its focus on short-term impact, novel data fusion and methodology, and high accuracy.• Focus on short-term impact: Our model uniquely identifies and quantifies the fleeting effects of COVID-19 on market behavior.• Novel data fusion and framework: A novel framework of sentiment analysis was introduced in the form of Trend Popularity Index. Combining trend popularity index with momentum offers a comprehensive and dynamic approach to predicting market movements during volatile periods.• High predictive accuracy: Achieving the prediction accuracy (98.93%) sets this model apart from existing solutions, making it a valuable tool for informed decision-making.

This paper investigates the relationship between institutional ownership and idiosyncratic volatility in Chinese and the USA stock markets and explores the potential explanations.

In this paper, the authors use the panel data regressions and the dynamic tests of two-way Granger causality in the panel VAR model to examine the relationship between institutional ownership and idiosyncratic volatility in Chinese and the USA stock markets.

The authors find that the institutional ownership in the Chinese (the USA) stock market is significantly and positively (negatively) related to idiosyncratic volatility through various tests. This paper indicates that institutional investors in the USA are more prudent and risk-averse, while the Chinese institutional investors are not because of high risk-bearing capacity.

This paper deepens the authors’ understanding on the relationship between institutional ownership and idiosyncratic volatility and in the USA and the Chinese stock markets. This paper explains the opposite relationships between institutional ownership and idiosyncratic volatility in the stock markets in China and USA.

The objective of the study is to shed light on the notion of quality investing in the Indian stock market. The study also attempts to combine the value and quality metrics to test their ability to generate a higher risk-adjusted return.

The paper employs asset pricing models to examine the excess risk-adjusted returns and panel regression model (random estimates) to determine the price of quality in the cross-section of Bombay Stock Exchange (BSE) listed stocks from 2003 to 2020.

The results indicate that the quality-only strategy failed to produce substantial risk-adjusted returns in the Indian stock market. The returns to long/short hedging strategy quality-minus-junk (QMJ) are significantly positive with the majority of the returns attributable to the short leg of the stock portfolio. The findings further discovered that the explanatory effect of quality on prices is limited. In particular, a strategy that combines value and quality investing generated positive and significant alphas as well as a higher Sharpe ratio.

The study provides investors and portfolio managers with valuable insights for navigating undervalued high-quality equities in the Indian stock market.

This is the first research of its kind to examine the performance of quality (Q score indicator) combined with value investing in the Indian stock market. As majority of research have concentrated on developed economies, this study offers out-of-sample evidence to validate the strategy’s success in an emerging market.

This study aims to explore how bundling knowledge resources (i.e. knowledge integration mechanisms [KIMs]) and digital resources (i.e. big data-powered artificial intelligence [BDAI]) can enhance supply chain visibility (SCV) capabilities for implementing just-in-case (JIC) practices.

Analysis of survey data from Chinese manufacturers was conducted to test the proposed hypotheses.

The results reveal a significant positive effect of KIMs on BDAI, as well as positive effects of both BDAI and KIMs on SCV. Furthermore, the results suggest that SCV partially mediates the KIMs–JIC relationship and fully mediates the BDAI–JIC relationship.

This study advances the digital SC and inventory management literature by proposing and empirically testing a digital JIC model that explores how to bundle knowledge and digital resources into SC capabilities for managing JIC inventory in uncertain and digital times.

This study investigates the impact of economic policy uncertainty on corporation innovation in innovative cities. The study sheds light on different results from the previous literature by testing the moderator effects of entrepreneurial risk appetite on such impact.

A static panel estimator is applied to a Chinese sample of 416 firm-year observations from 2010 to 2019. This paper uses regression model to test the impact of uncertainty on enterprise innovation in innovative cities, and to test the regulatory role of entrepreneurial risk appetite. For a series of robustness analysis conducted by the author to deal with endogeneity, the results are robust.

The author finds reliable evidence that the economic policy uncertainty can promote corporations to invest more in R&D in innovative cities. In addition, the role of the entrepreneurial initiative is significant, and there is a positive moderating effect of entrepreneurial risk appetite between policy uncertainty and corporation innovation.

From a practical point of view, this study examines the impact of economic policy uncertainty on corporation innovation in innovative cities for the first time. It emphasizes the role of entrepreneurial risk-taking in the development of corporation innovation in Shenzhen, an innovative city. This research is of great significance to the formulation of government policies and the innovative choice of entrepreneurs. In addition, the research shows that the entrepreneurial risk appetite in innovative cities can have a positive impact on enterprise innovation. Therefore, when formulating policies, the government should take the subjective factors of entrepreneurs into account and support enterprises with innovation potential. The evidence of this study also helps entrepreneurs make innovative decisions and enhance their confidence in enterprise development.

By studying the impact of economic policy uncertainty on enterprise innovation under the regulation of enterprise risk appetite, this study shows the subjective and positive role of entrepreneurs in risk grasp in innovative cities for the first time. In addition, it fills the gap of the impact of policy uncertainty on innovative urban enterprises. In fact, although it is traditionally believed that economic policy uncertainty has a negative impact on enterprise innovation, the sensitive findings of this study reveal completely different results from previous studies.

The article aims to give an overview of the history and the achieved status of information science in the German Democratic Republic (GDR) with an emphasis on the organisation of information science and practice in the GDR and on the theoretical foundations of information science.

Primarily, this article is based upon critical literature studies, especially German-language books and journal articles, but the empirical basis also includes some unpublished sources (e.g. letters from information scientists from the GDR).

There are interesting results concerning the roots of information science in cybernetics, philosophy and the practical area of documentation. The naming of this knowledge field as “informatics”, “informatics of science” or “information and documentation science” is partly very distinct from Western conceptions. We found different theoretical foundations for information science including the approaches of Bonitz, Engelbert, Koblitz and Groß and Fuchs-Kittowski. In the GDR, information science and information practice were centralised, but through the information system science and technology, they were consistently accessible at all levels of professional work. With German reunification, information practice and its institutions, as well as GDR’s information science efforts, disappeared.

The article gives hints on the importance on and the survival of some GDR approaches in contemporary information science, but those developments should be analysed in much more detail.

This is the first overview article on the state and entire development of information science in the GDR.

This study delves into the nuanced implications of short-sale constraints on stock prices within the context of stock market efficiency. While existing research has explored this relationship, inconsistencies persist in their findings. The purpose of this study is to conduct a comprehensive review of literature to elucidate the reasons behind these disparities.

A systematic review of existing theoretical and empirical studies was conducted following the PRISMA method. The analysis centered on discerning the factors contributing to the divergence in projected stock prices due to these constraints. Key areas explored included assumptions related to expectations homogeneity, revisions, information uncertainty, trading motivations and fluctuations in supply and demand of risky assets.

The review uncovered multifaceted reasons for the disparities in findings regarding the influence of short-sale constraints on stock prices. Variations in assumptions related to market expectations, coupled with fluctuations in perceived information uncertainty and trading motivations, were identified as pivotal factors contributing to differing projections. Empirical evidence disparities stemmed from the use of proxies for short-sale constraints, varied sample periods, market structure nuances, regulatory changes and the presence of option trading.

This study emphasizes the significance of not oversimplifying the impact of short-sale constraints on stock prices. It highlights the need to understand these effects within the broader context of market structure and methodological considerations. By delineating the intricate interplay of factors affecting stock prices under short-sale constraints, this review provides a nuanced perspective, contributing to a more comprehensive understanding in the field.

This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?

A mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.

The effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.

The findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.

The findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?

Firm performance remains at the heart of strategic management. In the quest to refine the field’s contribution, Venkatraman and Ramanujam (1986) argued that reliance upon single measures of firm performance is risky and firm performance should be treated as a multidimensional construct. Subsequently, researchers have examined trends in firm performance measurement ever since. Over a decade since the last examination of this issue, this study aims to add to the ongoing conversation.

The authors investigated 1,972 research papers published in five premier management journals for the years 2015–2019 to determine if multidimensional measurement of firm performance has improved.

The findings suggest that approximately two-thirds of papers that measure firm performance are published using only a single measure of firm performance, and approximately three-fourths do not measure firm performance across multiple dimensions.

This study contributes to the literature by emphasizing the necessity to consider the dimensionality of firm performance, use multiple measures and consistently ground firm performance variables with theory – especially control variables – to keep firm performance as the focus of the strategy field. Evidence and implications are discussed and recommendations for researchers and reviewers are provided.