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This study aims to establish the effect of environmental, social and governance (ESG) practices on Australian energy and utility investment performance.

Conventional and ESG-rated portfolios are constructed using monthly returns and ESG scores of S&P/ASX 300 listed energy and utility firms from 2014 to 2022. Portfolio performance is estimated using a four-factor regression model, controlling for any economic shocks associated with the COVID-19 pandemic.

The findings show that the lower the ESG score associated with the overall ESG and environmental portfolios, the greater the performance compared to the market (but not the conventional and other ESG portfolios). High ESG scores do not appear to influence the performance of the energy and utility portfolios, which contrasts expectations that the uptake of ESG should deliver superior risk-return outcomes for investors. The findings also indicate that a contrarian investment approach may be a reasonable performance indicator for high-rated ESG portfolios. ESG practices did not impact portfolio performance during the COVID-19 pandemic.

This research has contributed to the literature by offering ESG investment insights for policymakers, regulators, fund managers and investors. Consistent with the agency perspective on ESG practices and efficient market hypothesis, the evidence implies that, regardless of ESG scores (either high or low), investors should consider investing passively in diversified energy and utility portfolios or low-cost index fund equivalents.

In construction projects, engineering variations are very common and create breeding grounds for opportunistic claims. This study investigates the complementary effect between an inspection mechanism and a reputation system in deterring opportunistic claims, considering an employer with limited inspection accuracy and a contractor, which can be either reputation-concerned or opportunistic.

This paper applies a signaling game to investigate the complementary effect between the employer's inspection and a reputation system in deterring the contractor's possible opportunistic claim, considering the information-flow influence of claiming prices.

This study finds that in the exogenous-inspection-accuracy case, the employer does not always inspect the claim. A more stringent reputation system complements a less accurate inspection only when the inspection cost is lower than a threshold, but may decline the employer's surplus or social welfare. In the optimal-inspection-accuracy case, the employer always inspects the claim. However, only a sufficiently stringent reputation system can guarantee the effectiveness of an optimal inspection in curbing opportunistic claims. A more stringent reputation system has a value-stepping effect on the employer's surplus but may unexpectedly impair social welfare, whereas a higher inspection cost efficiency always reduces social welfare.

This article contributes to the project management literature by combing the signaling game theory with the reputation theory and thus embeds the problem of inspection mechanism design into a broader socio-economic framework.

The paper aims to clarify the effects of brand differentiation on the platform's formulation of channel strategy and help the online platform formulate the optimal channel strategy, which involves selecting a proper selling mode for each brand.

The paper develops a multistage game model consisting of one online platform and two competing manufacturers with differentiated brands and examines the effects of brand differentiation on these three channel members' profits under each candidate channel strategy.

The results show that the platform prefers to offer the reselling mode for both brands when the brand differentiation is low, and this preference will be enhanced by the decrease in order fulfilment cost. By contrast, when the brand differentiation is high, it will offer the reselling mode for the premium brand but the marketplace service for the economy brand if the order fulfilment cost is not high; or the marketplace mode will be offered to both brands if this cost is high.

This study assumes that the order fulfilment costs of platform and manufacturer are fixed and symmetric. Therefore, researchers are encouraged to consider asymmetric costs of order fulfilment.

The paper guides the online platform to formulate the optimal channel strategy for differentiated brands and provides managerial insights for differentiated brands entering online markets.

This paper explores platforms' optimal channel strategy by jointly considering the effects of brand differentiation and investigates the impacts of brand differentiation on the optimal decision making under four candidate options. Moreover, this paper has been extended to examine the case when the manufacturers' production costs cannot be neglected.

This study presents a framework to estimate throughput and cost of additive manufacturing (AM) as related to process parameters, material thermodynamic properties and machine specifications. Taking a 3D model of the part design as input, the model uses a parametrization of the rate-limiting physics of the AM build process – herein focusing on laser powder bed fusion (LPBF) and scaling of LPBF melt pool geometry – to estimate part- and material-specific build time. From this estimate, per-part cost is calculated using a quantity-dependent activity-based production model.

Analysis tools that assess how design variables and process parameters influence production cost increase our understanding of the economics of AM, thereby supporting its practical adoption. To this aim, our framework produces a representative scaling among process parameters, build rate and production cost.

For exemplary alloys and LPBF system specifications, predictions reveal the underlying tradeoff between production cost and machine capability, and look beyond the capability of currently commercially available equipment. As a proxy for build quality, the number of times each point in the build is re-melted is derived analytically as a function of process parameters, showcasing the tradeoff between print quality due to increased melting cycles, and throughput.

Typical cost models for AM only assess single operating points and are not coupled to models of the representative rate-limiting process physics. The present analysis of LPBF elucidates this important coupling, revealing tradeoffs between equipment capability and production cost, and looking beyond the limits of current commercially available equipment.

This chapter explores the complex intersection between students with disabilities and bullying prevention within educational settings. While bullying impacts all students, those with disabilities face unique challenges that make them more vulnerable to such experiences (Rose & Gage, 2016; Rose et al., 2011). By examining the underlying factors contributing to the heightened risk of bullying among students with disabilities, this chapter aims to provide a more comprehensive understanding of the issue. It delves into the specific ways in which students with disabilities are targeted and engage in bullying behaviors, such as through verbal, relational, or physical, and highlights the negative consequences on their overall well-being and academic performance. Moreover, this chapter examines existing interventions and strategies employed to prevent bullying among students with disabilities. It critically evaluates the effectiveness of individual, classroom, and school-wide interventions, highlighting the need for a comprehensive approach that addresses the unique needs and challenges faced by this subset of students. The importance of collaboration between educators, parents, and other stakeholders in implementing evidence-based practices is also emphasized. By promoting awareness, fostering inclusive school environments, and implementing targeted interventions, we can strive toward creating a safe and supportive atmosphere that enables students with disabilities to thrive academically and socially, free from bullying involvement.

This paper aims to assess gender bias in Italian university student mobility controlling for the field of study. It uses data from the Italian National Student Archive (Anagrafe Nazionale degli Studenti – ANS) for the cohort of freshmen enrolled in the 2017 academic year. The macro-regional comparison unfolds across the following areas: North and Centre, Southern Italy and main Islands (Sicily and Sardinia).

The analysis is firstly carried out at the national level, and secondly, it focusses on macro-geographical areas. University mobility choices are thus investigated from a gender perspective, conditioning upon other theoretically relevant characteristics collected for the prospective first-year university student population enrolled in 2017. The authors analyse data in a regression setting (logit models) within the multilevel framework, which considers students at level 1 and the field of study at level 2. Gender differences in the propensity to be a mover – conditional upon the choice of the field of study – were captured by introducing random intercepts to account for clustering of students in fields of study and random slopes to allow the gender effect to differ among them.

Findings show that university student mobility in Italy leads evidence of gender bias. This has been detected using a multilevel random slope approach that allowed the authors to jointly estimate a slope parameter for gender within each field of study. Moreover, using a regression setting allowed the authors to control for heterogeneity in geographical, educational and socio-demographic characteristics across students. In line with previous empirical findings, the authors' data highlight the presence of a relevant mobility flow of university students from the South toward the North-Centre of Italy and lower mobility of female students compared to male students from the South and Islands.

To the best of the authors' knowledge, there are no studies in Italy, which investigate if families' investment in higher education in terms of selection of no-local universities are affected by gender bias and if geographical differences in this behaviour between macro-areas are in place. Thus, investigating students' choices in tertiary education allows the authors to shed light on the presence of gender bias in families' education strategies addressed to increase the endowment of students' assets for future job opportunities.

Drawing upon person–supervisor fit theory, a model is developed to illustrate how leader–member trait mindfulness (in)congruence may impact leader–member exchange (LMX) and how such trait mindfulness (in)congruence can indirectly influence taking charge.

Polynomial regression and response surface methodology are used to analyze 237 valid matched leader–member dyads.

LMX increases as leaders' and members' trait mindfulness become more aligned; LMX is higher when leader–member dyads are congruent at high levels (vs low levels). In the case of incongruence, LMX is higher when the member's trait mindfulness exceeds that of the leader. Furthermore, the relationship between leader–member trait mindfulness (in)congruence and taking charge is mediated by LMX.

The joint and interactive role of high trait mindfulness in leader–member dyads can help them to generate high-quality interpersonal exchange, as well as to cope with challenges posed by present and future changes.

The linear, nonlinear, simultaneous and interactive effects of dyadic trait mindfulness expand previous research, clarifying that the evaluation of leader–member congruence and incongruence at various degrees, and for various patterns of trait mindfulness, is more informative than examining the direct effect alone.

Online shoppers make product purchase decisions based on product information shown on a retailer's website and potentially in comparison to that seen on competitors' websites. Insufficient, poor quality or missing information about a product can lead to reduced retailer sales. Measuring online product information quality (PIQ) is therefore an essential element in helping retailers maximize their potential success. This paper aims (1) to identify directly quantifiable PIQ criteria, (2) to assess the effects of PIQ and (3) to evaluate the moderating effect of product involvement.

The authors conducted a scenario-based experiment within 3,544 do-it-yourself (DIY) online shoppers from the United Kingdom (UK). Within an 8 × 2 × 2 between-subjects design, the authors manipulated the factors PIQ criteria (8), PIQ level (2) and product type (2).

The findings support that poor PIQ has a negative impact on consumers online shopping outcomes. The authors also found that the effects of PIQ differ between the various criteria, the product category and the level of consumer involvement in the selling process. In the context of product depiction, title readability and product attribute comparability with other retailers' websites a high level of PIQ is required. Moreover, high involvement products need a higher level of PIQ than low involvement products.

This research expands website quality and service failure literature by introducing PIQ criteria and its effects in the context of online retailing. The authors also establish actionable managerial recommendations to assist retailers to embrace and utilize PIQ to better understand their own potential website and thus business improvements.

This study aims to explore the gap between research and practice on supply chain risks due to COVID-19 by exploring the changes in global emphasis on supply chain risk research.

This work designed a research framework to compare the research of supply chain risks before and during the COVID-19 pandemic based on machining learning and text clustering and using the relevant publications of the web of science database.

The results show that scholars' attention to supply chain crisis has increased in the wake of the COVID-19 outbreak, but there are differences among countries. The United Kingdom, India, Australia, the USA and Italy have greatly increased their emphasis on risk research, while the supply chain risk research growth rate in other countries, including China, has been lower than the global level. Compared with the pre-pandemic period, the research of business finance, telecommunications, agricultural economics policy, business and public environmental occupational health increased significantly during the pandemic. The hotspots of supply chain risk research have changed significantly during the pandemic, focusing on routing problem, organizational performance, food supply chain, dual-channel supply chain, resilient supplier selection, medical service and machine learning.

This study has limitations in using a single database.

This work compared the changes in global and various countries' supply chain risk research before and during the pandemic. On the one hand, it helps to judge the degree of response of scholars to the global supply chain risk brought about by COVID-19. On the other hand, it is beneficial for supply chain practitioners and policymakers to gain an in-depth understanding of the relationship between the COVID-19 pandemic and supply chain risk, which might provide insights into not only addressing the supply chain risk but also the recovery of the supply chain.

The initial exploration of the changing extent of supply chain risk research in the context of COVID-19 provided in this paper is a unique and earlier attempt that extends the findings of the existing literature. Secondly, this research provides a feasible analysis strategy for supply chain risk research, which provides a direction and paradigm for exploring more effective supply chain research to meet the challenges of COVID-19.

Prior studies use two measures of firm-specific return variation (FSRV): idiosyncratic volatility in absolute and relative terms, the latter of which is also termed stock price nonsynchronicity. Whereas most research focuses on investigating the idiosyncratic volatility puzzle, the authors carry out comparison of these two measures and further investigate which of the two constituents of nonsynchronicity explain the association between FSRV and stock returns, emphasising the importance of assessing which component drives stock returns.

The authors use the US individual stock returns from 1925 to 2016 and define the two measures of FRSV based on the Fama and French (1993) model. Specifically, the authors decompose the relative measure into two components: (i) absolute idiosyncratic volatility and (ii) systematic volatility. The authors conduct various tests based on high-minus-low, zero-investment quintile portfolio sorts and perform the Fama–MacBeth analysis by singling out each component.

The authors find a positive return on the portfolio sorted on relative idiosyncratic volatility or on systematic volatility, but find a negative return sorted on absolute idiosyncratic volatility. The results are robust after controlling for size, BM and other risk characteristics using a double-sorting approach. The Fama–MacBeth regression results show that a positive association between the relative measure and stock returns is driven primarily by the low-systematic-volatility anomaly across firms. The findings are robust to controlling for return residual momentum, skewness, jumps and information discreteness.

Extant research posits the idiosyncratic volatility puzzle and the low-volatility anomaly. The authors emphasize the importance of integrating these two streams of research. This study enhances the understanding of the driving force underlying the relationship between FSRV and cross-sectional stock returns.