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Contemporary stochastic optimal control by synergy of the probability density evolution method (PDEM) and conventional optimal controller exhibits less capability to guarantee economical energy consumption versus control efficacy when non-stationary stochastic excitations drive hysteretic structures. In this regard, a novel multiscale stochastic optimal controller is invented based on the wavelet transform and the PDEM.

For a representative point, a conventional control law is decomposed into sub-control laws by deploying the multiresolution analysis. Then, the sub-control laws are classified into two generic control laws using resonant and non-resonant bands. Both frequency bands are established by employing actual natural frequency(ies) of structure, making computed efforts depend on actual structural properties and time-frequency effect of non-stationary stochastic excitations. Gain matrices in both bands are then acquired by a probabilistic criterion pertaining to system second-order statistics assessment. A multi-degree-of-freedom hysteretic structure driven by non-stationary and non-Gaussian stochastic ground accelerations is numerically studied, in which three distortion scenarios describing uncertainties in structural properties are considered.

Time-frequency-dependent gain matrices sophisticatedly address non-stationary stochastic excitations, providing efficient ways to independently suppress vibrations between resonant and non-resonant bands. Wavelet level, natural frequency(ies), and ratio of control forces in both bands influence the scheme’s outcomes. Presented approach outperforms existing approach in ensuring trade-off under uncertainty and randomness in system and excitations.

Presented control law generates control efforts relying upon resonant and non-resonant bands, and deploys actual structural properties. Cost-function weights and probabilistic criterion are promisingly developed, achieving cost-effectiveness of energy demand versus controlled structural performance.

This study aims to determine whether inter-organisational communication, based on four communicational dimensions (willingness, behaviour, commitment and quality), influences the performance of strategic alliances.

To achieve this objective, from a relational perspective, a qualitative approach was adopted, resorting to five small and medium-sized enterprises (SME)/cases in Portugal. Interviews with the key informants of these SMEs and documentary analysis were used to collect data.

Based on the cases analysed, the results show that communication is fundamental, valued and implemented in the SMEs studied. However, this is informal communication, reflecting the cooperation established and not based on contracts. In these SMEs, communication is the basis for understanding the alliance’s objectives and their fulfilment, which creates satisfaction in the partners and the alliance’s success. Communication also allows an alliance to be maintained and develop continuously, creating bonds between the partners.

Without that communication, alliance performance will not be possible. The study is relevant as it indicates management practices in strategic alliances based on inter-organisational communication, aiming for good performance. Therefore, it contributes to advancing knowledge about strategic alliances through the innovative link with inter-organisational communication and its applicability.

This study is new and innovative because it contributes to the literature in the area of strategic management, as it presents phenomena to do with inter-organisational communication and its relation with strategic alliances in SMEs, as well as advancing knowledge about the relational perspective. In addition, the application and development of inter-organisational communication, in all its communicational dimensions, are the basis for maintaining alliances over time and their performance.

This study aims to evaluate the effectiveness of machine learning models to yield profitability over the market benchmark, notably in periods of systemic instability, such as the ongoing war between Russia and Ukraine.

This study made computational experiments using support vector machine (SVM) classifiers to predict stock price movements for three financial markets and construct profitable trading strategies to subsidize investors’ decision-making.

On average, machine learning models outperformed the market benchmarks during the more volatile period of the Russia–Ukraine war, but not during the period before the conflict. Moreover, the hyperparameter combinations for which the profitability is superior were found to be highly sensitive to small variations during the model training process.

Investors should proceed with caution when applying machine learning models for stock price forecasting and trading recommendations, as their superior performance for volatile periods – in terms of generating abnormal gains over the market – was not observed for a period of relative stability in the economy.

This paper’s approach to search for financial strategies that succeed in outperforming the market provides empirical evidence about the effectiveness of state-of-the-art machine learning techniques before and after the conflict deflagration, which is of potential value for researchers in quantitative finance and market professionals who operate in the financial segment.

This study aims to verify the respective and interactive effects of subnational formal and informal institutions (i.e. legal effectiveness and social trust) on foreign firm performance, and further identify the contingent factor (i.e. institutional experience) that moderates these relationships.

Drawing on the institutional-based view, this study develops several hypotheses that are tested using a comprehensive dataset from four main data sources. The authors’ unit of analysis is foreign firms operating in China. The authors ran ordinary least squares (OLS) regression model to investigate the effects. A series of robustness tests and endogeneity tests were performed.

The results show that both legal effectiveness and social trust at subnational level positively affect foreign firm performance respectively. Legal effectiveness and social trust at subnational level have complementary effect in promoting the performance of foreign firms. Foreign firm's institutional experience in target region of emerging economies host country strengthens the positive impact of subnational legal effectiveness on performance, but weakens the positive impact of subnational social trust on performance.

It is important to fully understand the impact of heterogeneous institutional environments of subnational regions in emerging economies on foreign firm performance, which would help foreign firm make a more suitable secondary choice decision of investment destinations at the subnational regional level.

First, drawing on institutional-based view, the authors incorporate the subnational formal and informal institutional factors to investigate their impacts on foreign firm performance by switching the attention from national level to subnational level in emerging economy host countries. Second, this research furthers existing studies by bridging a missing link between both subnational formal and informal institutional environments and foreign firms' outcomes. Third, the authors prove that the model of subnational formal and informal institutions in influencing foreign firms' performance is contingent on their institutional experience in target subnational region of emerging economy host country.

In an era marked by artificial intelligence (AI), virtual reality (VR) and augmented reality (AR), this study presents a research paradigm centered on nurturing fundamental skills crucial for effective digital leadership in a paradoxical age where leaders are ambitious as well as skeptical for the adoption of such technologies. This study offers a strategic framework to seamlessly integrate diverse technologies into leadership development; the objective is to bridge the divide between theoretical understanding and practical implementation, especially through the lens of paradox theory.

This conceptual study delineates essential attributes that digital leaders must cultivate, drawing insights from the corpus of literature encompassing leadership, technology and organizational advancement. Synthesizing theoretical perspectives, the study proposes a comprehensive research framework that provides a systematic approach to harnessing the potential of AI, VR and AR to enhance leadership competencies. This conceptual study significantly contributes to paradox theory through method of “theory adaptation” as elaborated in the literature.

The study unveils a spectrum of foundational proficiencies, including technological acumen, adaptability, strategic acumen, effective communication, collaborative aptitude and ethical acumen, among others. These competencies underscore the multifaceted skill set required of digital leaders. To adeptly traverse the intricate digital terrain, foster innovation and align technological advancements with organizational objectives, these proficiencies are imperative for digital leaders to possess.

The distinctiveness of this study lies in its all-encompassing approach to digital leadership development by offering a paradoxical perspective and hence making a contribution to the body of knowledge for paradox theory. By amalgamating AI, VR and AR into a cohesive framework, the study enhances the comprehension of how these technologies collaboratively nurture leaders capable of cultivating organizational triumph in the digital age. This proposed paradigm serves as a bridge between cutting-edge digital technology usage and leadership proficiency paradox, furnishing pragmatic insights to benefit both academic researchers and industry practitioners.

Large-scale farm management in China has developed rapidly in recent years. Large-scale farmers face substantial operating risks, requiring extensive price risk management. However, the agricultural insurance and futures markets in China are incomplete. This study aims to analyze the price-risk-management behaviors of large-scale farmers under incomplete market conditions, with a focus on the interconnections between large scale farmers' subjective preferences (risk preferences, time preferences), liquidity constraints and their price risk management.

The authors construct an analysis framework to reveal the impact of large-scale farmers' risk preferences, time preferences and liquidity conditions on their price-risk-management behaviors under incomplete market conditions. Using data from field surveys and subjective preference experiments involving 409 large-scale grain farmers in China, an empirical analysis was conducted using the bivariate probit model.

The results show that risk-averse farmers will use risk transfer (such as contract farming) and risk diversification (such as multi-period sales) to avoid price risk. However, farmers subject to liquidity constraints and strong time preferences will not choose risk diversification, and the interaction between time preferences and liquidity constraints will strengthen this decision. The larger the farm-management scale, the greater the impact.

The authors focus on rapidly developed large-scale farm management in China. Appropriate price risk management is required by large-scale farmers due to their substantial operating risks. Considering the incomplete conditions of agricultural insurance and futures markets, the results of this study will help identify behavioral characteristics of large-scale farmers and optimize their price-risk-management strategies, further stabilizing large-scale farm management.

The study examines the potential moderating effects of repeating purchase cues and product knowledge on the relationship between the varying consistency of the review set and causal attribution. This study also investigates how causal attribution correlates with the perceived misleadingness of the review set.

A scenario-based experiment was conducted with 170 participants to explore the relationship between the consistency of the review set and causal attribution and how repeating purchase cues and product knowledge moderates this relationship.

Findings suggest that inconsistent review sets lead to more product (vs reviewer) attribution than consistent review sets. The repeating purchase cues mitigate the negative relationship between the consistency of the review set and product attribution, whereas product knowledge mitigates the positive relationship between the consistency of the review set and reviewer attribution. Furthermore, the results indicate that high product attribution and low reviewer attribution are associated with low perceived misleadingness.

This study is novel because it examines the moderating effects of repeating purchase cues and product knowledge on the relationship between the consistency of the review set and causal attribution. It adds to the literature by shedding light on the causal attribution process underlying the formation of perceived misleadingness of online reviews. The findings of this study provide valuable insights for managers on how to enhance the positive effects of consistent review sets and mitigate the negative effects of inconsistent review sets.

The purpose of this study is to investigate the effect of international expansion of emerging markets multinationals (EMNEs) on the home country nonmarket political strategy and why some EMNEs intensify this political tie more than others.

We test our theoretical framework using longitudinal data, with 16 years of observations, in Multilatinas and state loans from Brazil, one of the main outward foreign direct investment (OFDI) players in the world and the OFDI leader in Latin America.

Theoretically grounded on the institution-based view of strategy, it can be postulated that international expansion is a driver of home country nonmarket political strategy. It can also be hypothesized that political tie intensity is affected by the capacity of EMNEs to deal with international expansion issues without having to depend on relationship with homes country nonmarket political actors. The results provide support for the hypotheses presented.

This paper contributes to the EMNE internationalization literature by extending the understanding of the underlying motivations and forces shaping the home country nonmarket political strategy of multinationals from emerging markets and, thus, helping understand why some EMNEs tend to be more politically active than others. Likewise, the study contributes to advancing understanding regarding the home country strategic responses adopted by Multilatinas and the forces behind the nonmarket political strategies they employ in their international expansions, especially during turbulent times.

This study aims to examine how Confucianism influences corporate digital transformation and explore the underlying mechanisms. Meanwhile, this study also seeks to analyze whether the relationship between Confucianism and corporate digital transformation significantly varies under different contextual conditions.

This study utilizes a sample of Chinese listed firms from 2012 to 2021 to empirically examine how Confucianism influences corporate digital transformation and validate the mechanisms of informal hierarchies, agency costs and financing constraints. Moreover, it explores the moderating effects of political connection and overseas culture. Subsample regressions assess the influence of corporate internationalization, property rights and regional marketization.

The findings of this study highlight the crucial role of Confucianism in driving corporate digital transformation. Confucianism contributes to corporate digital transformation by clarifying informal hierarchies, reducing agency costs and alleviating financing constraints. Nevertheless, political connection and overseas culture weaken the positive impact of Confucianism on corporate digital transformation. Further evidence indicates that Confucianism's influence on digital transformation is particularly pronounced in environments characterized by limited internationalization, heightened marketization and among non-state-owned enterprises.

This study elucidates the role of informal institutions in driving corporate digital transformation, enriching the literatures on the intersection of Confucianism and corporate digitalization. Our findings offer a novel perspective and contribute to management practice by exploring the mechanisms and contextual conditions.

Over the past decade, artificial intelligence (AI) technologies have rapidly advanced organizational management, with many organizations adopting AI-based algorithms to enhance employee management efficiency. However, there remains a lack of sufficient empirical research on the specific impacts of these algorithmic management practices on employee behavior, particularly the potential negative effects. To address this gap, this study constructs a model based on the psychological ownership theory, aiming to investigate how algorithmic management affects employees’ knowledge hiding.

This study validates the model through a situational experiment and a multi-wave field study involving full-time employees in organizations implementing algorithmic management. Various analytical methods, including analysis of variance, regression analysis and path analysis, were used to systematically test the hypotheses.

The study reveals that algorithmic management exerts a positive indirect influence on knowledge hiding through the psychological ownership of personal knowledge. This effect is particularly pronounced when employees have lower organizational identification, highlighting the critical role of organizational culture in the effectiveness of technological applications.

This study is among the first empirical investigations to explore the relationship between algorithmic management and employee knowledge hiding from an individual perception perspective. By applying psychological ownership theory, it not only addresses the current theoretical gap regarding the negative effects of algorithmic management but also provides new theoretical and empirical support for the governance and prevention of knowledge hiding within organizations in the context of AI algorithm application. The study highlights the importance of considering employee psychology (i.e. psychological ownership of personal knowledge) and organizational culture (i.e. organizational identification) under algorithmic management. This understanding aids organizations in better managing knowledge risks while maximizing technological advantages and effectively designing organizational change strategies.