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Examining dominance in influencing leadership perceptions for men and women has received significant scholarly attention. The studies typically show that dominance is beneficial for men in attaining leadership positions but not for women. However, the studies were predominantly conducted more than two decades ago. Given the developments in gender research, this study extends the dominance line of inquiry by probing the impact of dominance need on leader emergence for men versus women in self-managed work teams. Furthermore, this study aims to examine if team dominance needs dispersion posits as a boundary condition for the combined impact of dominance needs and gender on leader emergence.

The authors conducted a longitudinal study that lasted one semester and involved 44 ad hoc self-managed work teams.

This study found that dominance needs facilitated leader emergence regardless of gender, and team dominance needs dispersion. Furthermore, men with high dominance needs were likelier to emerge as leaders than women with high dominance needs in high dominance needs dispersion teams. By contrast, women low in dominance needs received a harsher penalty in their leadership emergence than men low in dominance needs in low dominance needs dispersion teams

These results depart from the usual findings regarding the backlash effects that dominant women face and paint a rosy picture regarding the use of dominance in shaping leader emergence. However, the findings support the notion in gender stereotypes research that women are judged more critically than men in ascending to leadership positions.

Agile project teams are self-managing and self-organizing teams, and these two characteristics are pivotal attributes of emergent leadership. Emergent leadership is thus common in agile teams – however, how these (informal) emergent leaders can be identified in teams remains far from understood. The purpose of this research is to uncover techniques that enable top management to identify emergent agile leaders.

We approached six agile teams from four organizations. We employ social network analysis (SNA) and aggregation approaches to identify emergent agile leaders.

We approached six agile teams from four organizations. We employ SNA and aggregation approaches to identify emergent agile leaders.

Seven emergent leaders are identified using the SNA and aggregation approaches. The same leaders are also identified using the KeyPlayer algorithms. One emergent leader is identified from each of the five teams, for a total of five emergent leaders from the five teams. However, two emergent leaders are identified for the remaining sixth team.

Emergent leadership is a relatively new phenomenon where leaders emerge from within teams without having a formal leadership assigned role. A challenge remains as to how such leaders can be identified without any formal leadership status. We contribute by showing how network analysis and aggregation approaches are suitable for the identification of emergent leadership talent within teams. In addition, we help advance leadership research by describing the network behaviors of emergent leaders and offering a way forward to identify more than one emergent leader in a team. We also show some limitations of the approaches used and offer some useful insights.

Evaluating project success within the construction industry presents challenges due to the unique characteristics of the sector, the complexity of projects, and the involvement of diverse stakeholders. Conducting a bibliometric analysis, this paper aims to unravel the major research themes and methodologies utilised by researchers in studying the critical success criteria for construction projects, as well as extracting these success criteria.

The researchers systematically searched and screened 95 papers from Scopus and Web of Science (WoS) databases. This study conducted research focus parallelship network (RFPN) analysis and keywords co-occurrence network (KCON) analysis using BibExcel and Gephi to cluster the papers, illuminate the relationships among keywords within each cluster, and identify the primary research directions.

Using the RFPN analysis, this study classified the papers into three distinct clusters: infrastructure and public projects success, risk and knowledge management, and contractors and procurement management. Statistical techniques such as structural equation modelling (SEM) and multi-criteria decision-making methods such as analytic hierarchy process (AHP) have been used to analyse project success in the construction industry.

Considering the intensified demand for streamlined digital interactions and the increasing emphasis on sustainability and safety performance, construction companies are recommended to allocate greater investments toward the automation and digitisation of their products and processes. Prioritising modular construction and embracing transformative technologies alongside data science is crucial for enabling well-informed decision-making, and enhancing project success.

This study contributes to the existing body of knowledge by conducting a quantitative and systematic evaluation of the literature on project success criteria in the construction industry and uncovering key research areas. It addresses the pressing need to understand the complexities of construction projects amidst evolving industry dynamics and emerging disruptions. Moreover, by highlighting the implications of digital innovations and modular construction, this study urges deeper exploration into their impact on project performance and stakeholder satisfaction. This research sets a comprehensive framework for investigating the interplay between project complexity, technological advancements, and sustainable practices in the construction sector, paving the way for strategic advancements in the field.

This study aims to create actionable guidelines for pricing decision-making by employing game a theory matrix to forecast the correlation between the average daily rate and the latest ambiance of hotels.

Utilizing a vector error correction model, the research employs game theory to assess the influence of the average daily rate on the hotel's newest atmosphere during both peak season (April–September) and valley season (October–March).

Findings indicate that during the peak season, when the average daily rate rises in resorts and falls in suburban areas, the hotel’s newest atmosphere is at its best in both types of accommodations. During the off-peak season, the hotel’s newest atmosphere is achieved when both resorts and suburban accommodations increase their average daily rates.

There are two study constraints. One is the assumption that hotel guests in both parties prefer not to change hotels, but in fact they would. Two is a limited sample of two resort and suburban markets.

This suggests that the hotel’s newest atmosphere can draw both leisure and business travelers to suburban areas during the low season and more leisure travelers to resorts during the high season.

The study’s findings have implications for revenue related to the hotel’s newest atmosphere and cleanliness for both suburban and resort hotels, particularly when promoting tourism collaboratively.

The study provides valuable insights for hotel managers in analyzing pricing strategies using matrices.

This paper aims to increase understanding of pertinent exogenous and endogenous antecedents that can reduce data privacy breaches.

A cross-sectional survey was used to source participants' perceptions of relevant exogenous and endogenous antecedents developed from the Antecedents-Privacy Concerns-Outcomes (APCO) model and Social Cognitive Theory. A research model was proposed and tested with empirical data collected from 213 participants based in Canada.

The exogenous factors of external privacy training and external privacy self-assessment tool significantly and positively impact the study's endogenous factors of individual privacy awareness, organizational resources allocated to privacy concerns, and group behavior concerning privacy laws. Further, the proximal determinants of data privacy breaches (dependent construct) are negatively influenced by individual privacy awareness, group behavior related to privacy laws, and organizational resources allocated to privacy concerns. The endogenous factors fully mediated the relationships between the exogenous factors and the dependent construct.

This study contributes to the budding data privacy breach literature by highlighting the impacts of personal and environmental factors in the discourse.

The results offer management insights on mitigating data privacy breach incidents arising from employees' actions. Roles of external privacy training and privacy self-assessment tools are signified.

Antecedents of data privacy breaches have been underexplored. This paper is among the first to elucidate the roles of select exogenous and endogenous antecedents encompassing personal and environmental imperatives on data privacy breaches.

While current research is reporting on a crisis in school leadership aspirations and appointments, this research sheds light on the mediating factors that school middle leaders (MLs) attribute to their appointment as a leader and their aspirations to senior leadership positions.

Qualitative data from a larger, mixed-method, research project were collected using an online survey completed by 2,608 MLs in a large public school system in Australia and is analysed using an inductive thematic process.

The findings suggest that MLs experience varying and non-linear career progressions and that a range of personal and professional factors impact their career aspirations. The support of an “experienced other” such as a principal, mentor or coach and strategic career planning are all important in the identification, support, development and advancement of MLs.

This paper contributes to the scarce body of research on middle leadership identification and aspirations. The study recommends a system approach drawn from the empirical data and associated literature that may be useful to inform policymakers, school systems and school leaders in efforts towards middle leadership identification, development and advancement.

Construction organizations' investment in effective talent-development programs is a key strategy in attracting, developing and retaining staff. Such programs are especially important given the current challenges in the construction workforce, including labor shortages, an aging workforce, generational differences in the workforce, supply chain disruptions and the need to effectively train staff in the skills that are essential in a constrained labor environment. To address these challenges, this study proposes a performance measurement strategy that construction companies can use as input to design their talent development programs.

The strategy intends to assess the performance of project managers and develop criteria that define categories of their performance, including the top performers' category. This enables construction organizations to provide each project manager with individualized training that addresses areas of weakness and in turn, develops the skills that correspond with being top performers. The proposed strategy was developed and tested by surveying the immediate supervisors of 187 project managers working for general and specialty contractors in the United States. Principal component analysis was used to develop a single performance construct from seven performance criteria.

This construct was used to organize the project managers into the categories of top, above-average and below-average performers. According to the findings, top-performing project managers have well-rounded skills in the areas of leadership, communication, technical proficiency and overall job knowledge.

The outcomes of this study can help construction organizations focus their talent-development programs on the skills most associated with PMs being top performers.

This study provides construction organizations with a comprehensive performance-measuring construct to focus their talent-development programs on the skills most associated with top-performing project managers. Researchers can use this study as a foundation for further understanding how performance is related to various construction professions.

Scientific knowledge is rich with literature on the antecedent role of social capital on resilience. However, empirical evidence has overlooked the role of the individual dimensions of bonding and bridging social capital on its outcomes. This study aims to extend empirical research on the influence of social capital facets of bonding social capital and bridging social capital on financial resilience and more specifically in the aftermath of the COVID-19 pandemic where women SMEs mostly need bonding.

The study uses an explanatory research design to determine the hypothesized effect of social capital on financial resilience. The authors used regression to test the hypothesized relationship using a sample of three hundred and eight four women-owned SMEs in Kampala registered with Kampala City Traders Association.

According to the findings, the social bonding provides female entrepreneurs with emotive encouragement and inspiration through personal connections and responsibility sharing. Furthermore, women entrepreneurs bridging, which consisted of business networks, made it easier for them to identify new financial opportunities, which ultimately led to an increase in their financial resilience. The findings placed an emphasis on the significance of fellow business owners as sources of knowledge and assets that are crucial to maintaining one's financial resilience.

Data were collected from women owned SMEs, and the application of the findings may be limited to women SMEs in Kampala District. Therefore, future research should replicate the current study findings using a sample drawn from other SMEs owned by both male and female from outside Kampala because of changes in operating environment. The study was cross-sectional, and financial resilience of a firm changes was periodical. This study paves the way for future longitudinal research in the same topic area, which will allow for a more complete comprehension of the financial resiliency of SMEs throughout a range of different time periods.

Research findings shape trajectory for current practitioners of SMEs to establish relevant social bonding and bridging as social capital in preparation for financial resilience in case of any pandemic.

To the best of the authors' knowledge, this study is the first to establish the antecedent role of social capital on financial resilience during an economic crisis induced by the COVID-19 pandemic, using a sample of women-owned medium- and small-sized businesses in Kampala.

The purpose of this study is to explore the effect of backward supply chain information sharing (SCIS) on the performance of different parties along supply chains.

Using data collected from 617 Chinese manufacturers, this study examines the effect of information sharing from manufacturers to suppliers (ISMS) on supplier and manufacturer responsiveness in both two- and three-tier supply chains and the transferrable effect of information sharing from customers to manufacturers (ISCM) on supplier responsiveness. The authors use structural equation modeling and regression analysis to estimate the proposed relationships.

ISMS is positively related to supplier responsiveness in both two- and three-tier SCIS, whereas its effect on manufacturer responsiveness is conditional, indicating that upstream receiving parties benefit more from backward SCIS. ISCM is positively related to supplier responsiveness, demonstrating the transferable effect of backward SCIS.

When designing SCIS strategies, managers should take into account the unequal distribution of benefits from SCIS and the transferable effect of backward SCIS. Specifically, companies should always motivate their downstream partners to engage in SCIS, while they themselves need to be cautious with information sharing. They should also be aware that their information sharing behavior may benefit a third upstream party.

This study provides deep insights into the distribution of the benefits from SCIS among supply chain partners and suggests that the direction and the role supply chain partners play (as receiving or disclosing parties) in SCIS matter.

The crypto market is growing quickly, marked by a lack of fundamentals, and the risks are not yet fully comprehended by participants. Our goal is to investigate overconfidence in this market and analyze the role that risk propensity and certain demographics play.

We conducted a survey in Brazil and Portugal, leveraging an online questionnaire disseminated via social media channels to engage a diverse adult population. We collected a total of 826 responses, addressing ethical considerations throughout the process. The data analysis was conducted using SPSS statistical software and logit regression modeling.

Our study reveals that overconfidence is a notable bias that distinguishes individuals who invest in cryptocurrencies from those who do not. Although overconfidence and risk propensity are closely linked, they originate from distinct personal characteristics. Furthermore, our findings indicate that age and market experience positively correlate with overconfidence and negatively correlate with risk propensity. Financial knowledge, interestingly, did not prove to be a significant factor for cryptocurrency investment.

Our research augments the existing literature on overconfidence, delving into this phenomenon in a new subdomain, and in doing so, it enriches our comprehension of the unique and still relatively under-researched cryptomarket. Moreover, we illuminate individual factors that sway the decision to invest in cryptocurrencies and should be considered by market participants.

• (1)

  Pioneering work examining the presence of overconfidence bias among crypto-investors, using a robust data set collected from a binational survey.

• (2)

  Verifies the relations among overconfidence, risk propensity, and demographics.

• (3)

  Examines the influence of age and experience on investment decisions, revealing a positive relationship with overconfidence and a negative correlation with risk propensity.

• (4)

  Logistic regression is used to determine the combined effect of overconfidence, risk propensity, and demographics on the decision to invest in cryptocurrencies.

Pioneering work examining the presence of overconfidence bias among crypto-investors, using a robust data set collected from a binational survey.

Verifies the relations among overconfidence, risk propensity, and demographics.

Examines the influence of age and experience on investment decisions, revealing a positive relationship with overconfidence and a negative correlation with risk propensity.

Logistic regression is used to determine the combined effect of overconfidence, risk propensity, and demographics on the decision to invest in cryptocurrencies.