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Role congruity theory and gender stereotypes research suggests men are expected to engage in agentic behavior and women in communal behavior as leaders, and that role violation results in backlash. However, extant gender and leadership research does not directly measure expectations–behavior incongruence. Further, researchers have only considered one condition of role incongruence – display of counter-role behavior – and have not considered the outcomes of failing to exhibit role-congruent behavior. Additionally, few studies have examined outcomes for male leaders who violate gender role prescriptions. The present study aims to address these shortcomings by conducting a novel empirical test of role congruity theory.

This experimental study used polynomial regression to assess how followers evaluated leaders under conditions of incongruence between follower expectations for men and women leaders’ behavior and leaders’ actual behavior (i.e. exceeded and unmet expectations). Respondents read a fictional scenario describing a new male or female supervisor, rated their expectations for the leader’s agentic and communal behavior, read manipulated vignettes describing the leader’s subsequent behavior, rated their perceptions of these behaviors, and evaluated the leader.

Followers expected higher levels of communal behavior from the female than the male supervisor, but no differences were found in expectations for agentic behavior. Regardless of whether expectations were exceeded or unmet, supervisor gender did not moderate the effects of agentic or communal behavior expectations–perceptions incongruence on leader evaluations in polynomial regression analyses (i.e. male and female supervisors were not evaluated differently when displaying counter-role behavior or failing to display role-congruent behavior).

In addition to providing a novel, direct test of role congruity theory, the study highlighted a double standard in gender role-congruent behavior expectations of men and women leaders. Results failed to support role congruity theory, which has implications for the future of theory in this domain.

Breakthrough technological innovation is of vital significance for firms to acquire and maintain sustainable competitive advantages. The construction of an innovation ecosystem and the interaction with heterogeneous participants have emerged as a new dominant model for driving sustained breakthrough technological innovation in firms. This study aims to explore the effects of collaborative modes within the innovation ecosystem on firms’ breakthrough technological innovation and the ecological legitimacy mechanisms involved.

The research employs data from 212 innovative firms and conducts empirical research using a two-stage structural equation modeling (SEM) and artificial neural network (ANN) analysis.

The results indicate that firm-firm collaboration (FF), firm-user collaboration (FU), firm-government collaboration (FG), firm-university-institute collaboration (FUI) and firm-intermediary collaboration (FI) all have significant positive effects on breakthrough technological innovation (BTI), with FU being particularly crucial. Furthermore, the results confirm the positive moderating effects of ecological legitimacy (EL) on the relationships between FF and BTI, as well as between FU and BTI. Conversely, EL has a negative moderating effect on the relationship between FUI and BTI, as well as between FI and breakthrough technological innovation. Additionally, EL does not have a significant influence on the relationship between FG and BTI.

Through resource dependence theory (RDT), this study unveils the black box of how collaboration modes within innovation ecosystems impact breakthrough technological innovation. By introducing ecological legitimacy as a contextual factor, a new research perspective is provided for collaboration innovation within innovation ecosystems. The study employs a combination of SEM and ANN for modeling, complementing nonlinear relationships and obtaining robust results in complex mechanisms.

The explanatory study aimed to understand the global perspective of quality education and describe transformative strategies that empower teacher educators and equip the education system and future teachers to act as change agents by fostering learning processes that support students towards a sustainable future.

The explanatory study used a descriptive qualitative design. The researchers conducted four semi-structured group interviews of the 23 participants including pre- and in-service teachers, teacher educators, and policymakers from Austria, Germany, Italy, Canada, the United States, and South Africa during the Teach4Reach 1.0 project on SDG4. The inductive thematic analysis of the data was guided by Kuckartz’s six-phase approach for constructing data-driven categories.

The study identified SDG4 as pivotal in supporting the overall UN Agenda 2030. Furthermore, three distinct themes unequivocally emerged and were developed concerning the imperative nature of envisioning transformation for sustainability: (1) collaboration, (2) well-being and context of individuals, and (3) strategies for skill development.

Due to the qualitative nature of the study, the participants’ comprehensive understanding of the SDGs and their perceptions remained conditional to methodological subjectivity.

The study enabled a dialogue between educational stakeholders (pre- and in-service teachers, teacher educators, and policymakers), whereby an awareness and encouragement of SDG4 regarding quality education and training of quality future teachers occurred. A further implication includes increased collaboration and dialogue regarding the well-being of individuals (educational stakeholders), the context of individuals, and strategies for skill development.

The study elaborates on reaching the SDGs and identifies themes essential for sustainability transformation in teacher education. It also highlights further research and clarity about the responsibilities of educational stakeholders for quality education, for instance, (1) how to increase the supply of quality teachers by focusing on collaboration, well-being, and the contexts of individuals and (2) how value-embedded skills can support quality education for sustainability.

This paper presents an experimental investigation in establishing the relationship between FDM process parameters and tensile strength of polycarbonate (PC) samples using the I-Optimal design.

I-optimal design methodology is used to plan the experiments by means of Minitab-17.1 software. Samples are manufactured using Stratsys FDM 400mc and tested as per ISO standards. Additionally, an artificial neural network model was developed and compared to the regression model in order to select an appropriate model for optimisation. Finally, the genetic algorithm (GA) solver is executed for improvement of tensile strength of FDM built PC components.

This study demonstrates that the selected process parameters (raster angle, raster to raster air gap, build orientation about Y axis and the number of contours) had significant effect on tensile strength with raster angle being the most influential factor. Increasing the build orientation about Y axis produced specimens with compact structures that resulted in improved fracture resistance.

The fitted regression model has a p-value less than 0.05 which suggests that the model terms significantly represent the tensile strength of PC samples. Further, from the normal probability plot it was found that the residuals follow a straight line, thus the developed model provides adequate predictions. Furthermore, from the validation runs, a close agreement between the predicted and actual values was seen along the reference line which further supports satisfactory model predictions.

This study successfully investigated the effects of the selected process parameters - raster angle, raster to raster air gap, build orientation about Y axis and the number of contours - on tensile strength of PC samples utilising the I-optimal design and ANOVA. In addition, for prediction of the part strength, regression and ANN models were developed. The selected ANN model was optimised using the GA-solver for determination of optimal parameter settings.

The proposed ANN-GA approach is more appropriate to establish the non-linear relationship between the selected process parameters and tensile strength. Further, the proposed ANN-GA methodology can assist in manufacture of various industrial products with Nylon, polyethylene terephthalate glycol (PETG) and PET as new 3DP materials.

Completing Right-of-Way (ROW) acquisition process on schedule is critical to avoid delays and cost overruns on transportation projects. However, transportation agencies face challenges in accurately forecasting ROW acquisition timelines in the early stage of projects due to complex nature of acquisition process and limited design information. There is a need of improving accuracy of estimating ROW acquisition duration during the early phase of project development and quantitatively identifying risk factors affecting the duration.

The quantitative research methodology used to develop the forecasting model includes an ensemble algorithm based on decision tree and adaptive boosting techniques. A dataset of Georgia Department of Transportation projects held from 2010 to 2019 is utilized to demonstrate building the forecasting model. Furthermore, sensitivity analysis is performed to identify critical drivers of ROW acquisition durations.

The forecasting model developed in this research achieves a high accuracy to predict ROW durations by explaining 74% of the variance in ROW acquisition durations using project features, which is outperforming single regression tree, multiple linear regression and support vector machine. Moreover, number of parcels, average cost estimation per parcel, length of projects, number of condemnations, number of relocations and type of work are found to be influential factors as drivers of ROW acquisition duration.

This research contributes to the state of knowledge in estimating ROW acquisition timeline through (1) developing a novel machine learning model to accurately estimate ROW acquisition timelines, and (2) identifying drivers (i.e. risk factors) of ROW acquisition durations. The findings of this research will provide transportation agencies with insights on how to improve practices in scheduling ROW acquisition process.

The main objectives of this study are to (1) develop and test a cost contingency learning model that can generalize initially estimated contingency amounts by analyzing back the multiple project changes experienced and (2) uncover the hidden link of the learning networks using a curve-fitting technique for the post-construction evaluation of cost contingency amounts to cover cost risk for future projects.

Based on a total of 1,434 datapoints collected from DBB and DB transportation projects, a post-construction cost contingency learning model was developed using feedforward neural networks (FNNs). The developed model generalizes cost contingencies under two different project delivery methods (i.e. DBB and DB). The learning outputs of generalized contingency amounts were curve-fitted with the post-construction schedule and cost information, specifically aiming at uncovering the hidden link of the FNNs. Two different bridge projects completed under DBB and DB were employed as illustrative examples to demonstrate how the proposed modeling framework could be implemented.

With zero or negative values of change growth experienced, it was concluded that cost contingencies were overallocated at the contract stage. On the other hand, with positive values of change growth experienced, it was evaluated that set cost contingencies were insufficient from the post-construction standpoint. Taken together, this study proposed a tangible post-construction evaluation technique that can produce not only the plausible ranges of cost contingencies but also the exact amounts of contingency under DBB and DB contracts.

As the first of its kind, the proposed modeling framework provides agency engineers and decision-makers with tangible assessments of cost contingency coupled with experienced risks at the post-construction stage. Use of the proposed model will help them evaluate the allocation of appropriate contingency amounts. If an agency allocates a cost contingency benchmarked from similar projects on aspects of the base estimate and experienced risks, a set contingency can be defended more reliably. The main findings of this study contribute to post-construction cost contingency verification, enabling agency engineers and decision-makers to systematically evaluate set cost contingencies during the post-construction assessment stage and achieving further any enhanced level of confidence for future cost contingency plans.

The study aims to determine the impact of perceptual and attitudinal factors on employees’ counterproductive work behaviour (CWB). The study emphasises the verification of the direct, indirect, linear and non-linear effects of several antecedents of CWBs. The moderating role of self-efficacy is also investigated.

Data were collected from 1,215 employees from several industrial companies in Southern Iraq. The study used the hybrid approach to data analysis, based on a dual-stage SEM-ANN, i.e. partial least squares structural equation modelling and artificial neural network approach.

Results indicate that most of the proposed variables predict CWB and that abusive supervision and perceived organisational politics (POP) positively affect job burnout (JB) through job stress. In addition, non-linear relationships, JB, abusive supervision and POP are the most important in predicting CWB. The study confirms that a negative perception of the work environment increases the likelihood of harmful behaviours in the organisation and that self-efficacy can reduce such a perception.

The importance of the current study is summarised in its attempt to verify the antecedents of CWB by relying on a two-step approach to test linear and non-linear relationships. This approach will greatly enhance theories regarding adverse behaviour in the workplace, especially, with a fairly large sample size.

This article tackles the intersection of mothering and labor through the author's own experience as a feminist mother/manager from Istanbul, Turkey. It aims to revisit the first years of motherhood, exploring the struggle to invent a peculiar maternal subjectivity in opposition and negotiation with the patriarchal institution of motherhood, the new definition of maternal labor in a highly digital, neoliberal context and the issue of marital fairness in a dual-income heterosexual marriage.

The article presents an autoethnographic, retrospective and introspective inquiry into the first seven years of the author's mothering experience in order to offer an in-depth exploration of the various aspects of contemporary maternal labor.

The article shows how maternal labor has shifted in nature and expanded in scope in a contemporary non-Western context. It investigates the dissolution of the spatial, temporal and sensorial boundaries between the managerial labor dedicated to the workplace, and to the family. Highlighting the similarities of the two forms of labor, the article manifests the materiality, tangibility and visibility of maternal labor.

Further intersectional studies shall be beneficial to redefine maternal labor in different contexts.

Departing and diverting from the terms “invisible labor” and “mental load”, the article suggests a shift in terminology to stress the multifaceted medley of managerial tasks mothers undertake today.

The article provides an original take on maternal labor through the first-hand experience of a middle-class, professional mother from Istanbul, Turkey.

The purpose of this paper is to examine the degree of persistence in four precious metal prices (i.e. gold, palladium, platinum and silver) during the last four US recessions.

Using daily price data for gold, palladium, platinum and silver running from July 2, 1990, to March 21, 2022, and dating of business cycles in the USA provided by NBER (2022), the paper uses fractional integration to test the degree of persistence of precious metal prices.

The empirical analysis shows the unrelenting prominence of gold in relation to other precious metals (palladium, platinum and silver) as a hedge against market uncertainty in the post-pandemic new era.

Two are the main contributions of the paper. Firstly, the authors contribute to the commodity markets and finance literature on precious metal price modelling. Secondly, the authors also contribute to the literature on commodity markets and business cycles with a special focus on recessionary periods.