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Drawing on the conservation of resources theory, this study examines how proactive skill development (PSD) influences job performance and mediating and moderating mechanisms underlying the above relationship.

Data were collected from a sample of 261 full-time workers in three waves, spaced by a six-week interval (Time 1, N = 360; Time 2, N = 320; Time 3, N = 261).

The results confirmed that career stress mediated the relationship between PSD and job performance. Additionally, high career decidedness strengthened this negative relationship between stress and performance. Furthermore, career decidedness significantly moderated the indirect PSD–performance relationship via career stress, accentuating the indirect effect when decidedness is higher.

This study sheds light on the important role of proactive skills development in influencing job performance and what factors can affect this relationship. It offers practical implications by highlighting how targeted training can boost employees' proactivity and performance.

This paper proposes the “fields and weapons of the competition model applied to business networks” – CAC-Redes (in Portuguese, Campos e Armas da Competição – Redes de negócio), an extension of the fields and weapons of the competition model (CAC) – to study the competition and competitiveness of companies operating in business networks in a competitive environment while integrating organizational competencies, interorganizational ties and company positioning to provide competitive advantage.

CAC-Redes is born from the cross-fertilization process of various theoretical perspectives, namely, industrial organization, traditional view of operational activities and resources, relational view, strategic alignment, transaction cost theory and social perspectives in networks, structured according to systems theory and under the mantle of competitive advantage theory. To discover the structure of existing models of competitiveness in networks, a bibliographic search was conducted in the Scopus database. Quali-quantitative empirical research was undertaken in companies from six different economic sectors through structured questionnaires and personal interviews to understand how companies competed and discover the determining factors of their competitive advantage.

Only seven models of competitiveness in network were found, and their structures and characteristics are quite different from those of CAC-Redes. Empirical research confirms all the hypotheses that support CAC-Redes, which, combined with those of CAC, indicate the CAC-Redes corroboration.

CAC-Redes does not apply to networks without intercompany competition, studies on network governance and corporate strategy formulation.

CAC-Redes is effective in studying complex competitiveness phenomena because it considers multiple influences; provides a process based on qualitative and quantitative variables that increase the probability of formulating successful competitive strategies; simplifies the differentiation of skills from core competencies and determines them; proposes a competitive advantage criterion to select suppliers; creates a unifying language to represent the different strategic specificities of companies, competitors, suppliers, customers and the company environment and provides a library containing 181 weapons (resources) and dozens of interorganizational ties that can be used in empirical studies with other methodologies.

CAC-Redes, due to its originality and peculiarities, theoretically contributes to theory of resources because it dispenses with the assumption, “unique resource, source of competitive advantage”; to relational view because it considers interorganizational relationships as a competence and treats it quali-quantitatively and to core competencies because if the strategy changes, different core competencies will be needed. Furthermore, it is an alternative to the dynamic capabilities perspective, and it transforms the five manufacturing performance objectives into nine for the entire company.

CAC-Redes is an original model because its structure and characteristics comparatively differ from those of existing models, and 14 singularities are detected.

The purpose of this study is to investigate how mock jurors’ experiences of deliberations are impacted by the defendant having a personality disorder.

This study used a qualitative approach to explore mock jurors’ experiences during the deliberations of a fictional defendant, Sarah Priest. Ten participants formed two mock juries, and each mock jury were given two case studies to deliberate. Case study one described Priest as having “Severe Personality Disorder, Borderline Pattern” whereas case study two described Priest as having “Complex Mental Health Problems”. There were no changes to the content of the case studies aside from the change in language used to describe the defendant.

An inductive thematic analysis identified two main themes relating to juror experience: “Interaction with Other Mock Jurors” and “Language as a Barrier to a Verdict”. Participants constructed that prosocial interactions with other mock jurors in the deliberations helped them make a verdict decision, but some of these interactions led to disagreements between participants due to a wide variation of opinion. Second, the different description of the defendant in each case study were constructed to have made the deliberations and decision-making difficult, but for different reasons. In case study one, a lack of knowledge surrounding BPD was the reason for this difficulty, and in case study two, participants thought that the applicability of diminished responsibility criteria were unclear, making it hard to reach a verdict.

The findings have key implications for the judicial system; common experiences can be identified and recorded to implement procedures to protect jurors from adverse experiences.

There is a lack of studies that have investigated juror experience in the UK, and the few studies available have used a quantitative methodology. The approach taken in the current study is, therefore, unique in a UK context. The findings have key implications for the judicial system; common experiences can be identified and recorded to implement procedures to protect jurors from adverse experiences.

Overconfidence bias is considered to be a very influential decision-making bias in the business environment. This paper aims to identify the susceptibility of management accountants to overconfidence-related overplacement bias and to determine its pervasiveness among these professionals.

Two international samples of management accountants were surveyed using overplacement bias elicitation questions. The hypothesis that bias susceptibility varies between management accountants in different hierarchical employment positions was tested employing binary logistic regression.

Management accountants are found to be susceptible to overplacement bias, yet its pervasiveness among the samples is similar to other sample populations in comparable studies. Management accountants in the position of Chief Financial Officer (CFO) were found to be more susceptible to overplacement bias than their colleagues in other management accountant and business management positions.

The use of convenience sampling represents a limitation of the research.

The findings confirm that there is a need for syllabi and continual professional development projects to educate management accountants on this bias. CFOs are especially at risk of being overconfident, which may not be in the best interest of the business.

This is the first paper to assess overplacement bias in management accountants as a group of decision-makers, especially within the context of their increasing involvement in business decision-making.

This research aims to propose a model for the complex decision-making involved in the ecological restoration of mega-infrastructure (e.g. railway engineering). This model is based on multi-source heterogeneous data and will enable stakeholders to solve practical problems in decision-making processes and prevent delayed responses to the demand for ecological restoration.

Based on the principle of complexity degradation, this research collects and brings together multi-source heterogeneous data, including meteorological station data, remote sensing image data, railway engineering ecological risk text data and ecological restoration text data. Further, this research establishes an ecological restoration plan library to form input feature vectors. Random forest is used for classification decisions. The ecological restoration technologies and restoration plant species suitable for different regions are generated.

This research can effectively assist managers of mega-infrastructure projects in making ecological restoration decisions. The accuracy of the model reaches 0.83. Based on the natural environment and construction disturbances in different regions, this model can determine suitable types of trees, shrubs and herbs for planting, as well as the corresponding ecological restoration technologies needed.

Managers should pay attention to the multiple types of data generated in different stages of megaproject and identify the internal relationships between these multi-source heterogeneous data, which provides a decision-making basis for complex management decisions. The coupling between ecological restoration technologies and restoration plant species is also an important factor in improving the efficiency of ecological compensation.

Unlike previous studies, which have selected a typical section of a railway for specialized analysis, the complex decision-making model for ecological restoration proposed in this research has wider geographical applicability and can better meet the diverse ecological restoration needs of railway projects that span large regions.

In modern urban governance, rescue materials storage points (RMSP) are a vital role to be considered in responding to public emergencies and improving a city's emergency management. This study analyzes the siting of community-centered relief supply facilities.

Combining grey relational analysis, complex network and relative entropy, a new multi criteria method is proposed. It pays more attention to the needs of the community, taking into account the use of community hospitals, fire centers and neighborhood offices to establish small RMSP.

The research results firstly found suitable areas for RMSP site selection, including Hanyang, Qiaokou, Jiangan and Wuchang. The top 10 nodes in each region are found as the location of emergency facilities, and the network parameters are higher than ordinary nodes in traffic networks. The proposed method was applied in Wuhan, China and the method was verified by us-ing a complex network model combined with multi-criteria decision-making for emergency facility location.

This method solves the problem of how to choose the optimal solution and reduces the difficulty for decision makers. This method will help emergency managers to locate and plan RMSP more simply, especially in improving emergency siting modeling techniques and additionally in providing a reference for future research.

The method proposed in this study is beneficial to improve the decision-making ability of urban emergency departments. Using complex networks and comprehensive evaluation techniques, RMSP is incorporated into the urban community emergency network as a critical rescue force. More importantly, the findings highlight a new direction for further research on urban emergency facilities site selection based on a combination of sound theoretical basis as well as empirical evidence gained from real life case-based analysis.

1. Material reserve points are incorporated into the emergency supply network to maintain the advantage of quantity.

2. Build emergency site selection facilities centered on urban communities.

3. Use a complex network model to select the location of emergency supplies storage sites.

Material reserve points are incorporated into the emergency supply network to maintain the advantage of quantity.

Build emergency site selection facilities centered on urban communities.

Use a complex network model to select the location of emergency supplies storage sites.

Expanding on the real-world financial market framework and considering the current market turmoil, with cryptocurrencies (where contracts for difference (CFDs) are extremely common) (Hasso et al., 2019) displaying unprecedented volatility, the authors aim to test in an online laboratory setting whether displaying a risk warning message is truly effective in reducing the level of risk taken and whether the placement of this method makes a difference.

To explore the impact of risk disclosure framing on risk-taking behavior, the authors conducted an online pair-wise lottery choice experiment. In addition to manipulating risk awareness through the presence or absence of risk warning messages of varying intensity, the authors also considered dynamic inconsistency, cognitive ability and questionnaire-based financial risk tolerance (FRT) scores. The authors aimed to identify potential relationships between these variables and experimentally elicited risk aversion. The authors' study offers valuable insights into the complex nature of risky decision-making and sheds light on the importance of considering dynamic inconsistency in addition to risk awareness and aversion.

The authors' results provide statistical evidence for the efficacy of informative and very salient messages in mitigating risky decision, hinting at several policy implications. The authors also provide some statistical evidence in support of the relationship between cognitive abilities and risk preferences. The authors detect that individual with low cognitive abilities scores display great risk aversion.

This study investigates the impact of risk warning messages on investment decisions in an online laboratory setting – a unique approach. However, the authors go beyond this and also examine the potential influence of dynamic inconsistency on decision-making, adding further value to the literature on this topic. To ensure a comprehensive understanding of the participants, the authors collect data on cognitive ability and FRT using questionnaires. This study provides a simple and cost-effective framework that can be easily replicated in future research – a valuable contribution to the field.

This study aims to examine the effect of exploratory innovation offshoring on the level of hierarchical control and how this effect is moderated by transnational and dynamic environments.

This study draws on a sample of 148 Taiwanese multinational enterprises to examine their governance decisions on foreign investments.

Findings show that the more innovation offshoring is exploratory, the higher the level of hierarchical control will be used by multinational enterprises (MNEs) and that transnational and dynamic environments have different moderation effects on the positive exploratory innovation offshoring-hierarchical control relationship.

This study has two theoretical implications. First, this study extends the concept of complexity from a transaction attribute level (problem) to an environmental level (transnational environment) and finds that exploratory innovation offshoring and transnational environments interactively impact governance choices. Second, this study distinguishes between two sources of technological uncertainty – uncertainty due to transaction-level attributes (exploratory innovation offshoring) and external environments (dynamic environments) and finds that exploratory innovation offshoring and dynamic environments interactively impact governance choices.

The practical implication of this study lies in the simultaneous consideration of exploratory innovation offshoring and transnational/dynamic environments, which will allow international decision-makers to adjust/select the governance forms most appropriate for speedy responding to and handling environmental changes.

This study employs the theoretical perspectives of transaction cost economics (TCE) and resource-based view (RBV) to analyze and discuss the impact of operational environments – transnational and dynamic environments – on MNEs’ decisions on the governance structure for a given innovation offshoring.

Given the rapid development in artificial intelligence (AI), the hotel industry is deploying AI-based systems. In line with this important development, this study aims to examine the impact of trust in the hotel and AI-related performance ambiguity on consumers’ engagement with AI-based systems. This study ultimately examined the impact of engagement on consumers’ intentions to stay in hotels offering such systems, and intentions to tip.

This study developed a conceptual model based on the social cognition theory. The study used an online survey methodology and collected data from a nationwide sample of 400 hotel consumers from the USA. The data analysis was conducted with structural equation modeling.

Consumers’ engagement is strongly influenced by their trust in the hotel but not by performance ambiguity associated with AI. In turn, engagement strongly influenced consumers’ intentions to stay in hotels that have such systems and their intentions to tip.

As AI systems capable of making decisions for consumers are becoming increasingly present in hotels, little is known about the way consumers engage with such systems and whether their engagement leads to economic impact. This is the first study that validated a model that explains intentions to stay and tip for services facilitated by autonomous AI-based systems that can make decisions for consumers.

鉴于人工智能领域的快速发展, 酒店业正在部署基于人工智能的系统。为此, 本研究探讨了客人对酒店的信任和与AI相关的性能模糊性对消费者与基于AI的系统互动的影响。最终, 本研究考察了参与度对客人在提供此类系统的酒店住宿意愿和小费意愿的影响。

本研究基于社会认知理论开发了一个概念模型。研究采用在线调查方法, 从美国全国范围的400名酒店消费者中收集数据, 并采用结构方程建模进行数据分析。

消费者的参与度受酒店的信任强烈影响, 但不受与AI相关的性能模糊性的影响。反过来, 参与度强烈影响了消费者在提供此类系统的酒店住宿和给小费的意愿。

随着能够代表消费者做出决策的人工智能（AI）系统在酒店中日益普及, 人们对消费者如何与这类系统互动以及他们的互动是否会产生经济影响知之甚少。这是第一项验证了一个可以解释在自主的基于AI系统的服务下住宿和给小费意愿的模型的研究。

This study explores the extent of undergraduate students' engagement in interdisciplinary learning experiences across their academic journey and its potential correlation with elevated levels of self-efficacy in learning. Furthermore, the research investigates how the clarity of career decisions and future goals contributes to the perception of relevance, value and alignment of interdisciplinary learning experiences among undergraduate students.

Data were collected using a self-report questionnaire in a longitudinal survey administered annually to undergraduate students at a university in northern Taiwan over four waves from 2018 to 2021. The sample analyzed for this study consisted of 123 undergraduate students who willingly and continuously participated in the research throughout the specified period.

The results showed that self-efficacy within interdisciplinary learning experiences could be classified into three clusters: high efficacy, moderate efficacy and fluctuating efficacy. The determinants influencing these clusters include career decisions and years spent in university. Undergraduate students who have determined their career decisions and are in their latter two years of undergraduate studies demonstrate higher self-efficacy in interdisciplinary learning. Conversely, students who have yet to determine their career decisions exhibit a fluctuating pattern of self-efficacy across the three interdisciplinary learning categories.

Two key limitations of this research include a small sample size and a confined university-specific context, potentially constraining the applicability of the results to a broader population.

This study contributes to the interdisciplinary learning experience in higher education by explaining the significance of undergraduates' self-efficacy and career-related factors. Whereas most research has focused on the effects of self-efficacy, this study investigated the factors that influence undergraduates' self-efficacy.