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In today’s rapidly evolving work landscape, the design of office spaces is a crucial concern for organizations. Companies are redefining offices as collaboration hubs to entice employees back to in-person work. However, the understanding of how employees choose their workspaces, especially for collaborative activities, and how this should inform office design is lacking. Workers’ collaborative activity patterns can help better understand workspace choice behavior (WCB). In two studies, this paper aims to explore which characteristics of collaborative activities to consider when reshaping offices.

Data collected in a cross-sectional study design at a research institution (n = 285) and a university (n = 352) were used for confirmatory factor analyses and regression analysis.

The first study shows that collaborative activities can be classified into three distinct types: coordinative activities (planned and formal), deep collaboration (planned and complex) and spontaneous communication (informal and short encounters). The second study revalidates this classification and reveals patterns impacting WCB. Frequency and location preference of spontaneous communication and work environment satisfaction are strong predictors of on-site work. Personal characteristics like gender, age, managerial position or commute time are less consequential than assumed.

The results pinpoint guidelines for office designers and leaders in shaping effective workspaces and policies.

This paper provides new insights into classifying collaborative activities and personal characteristics, activity characteristics and environmental factors influencing WCB.

Surprisingly little is known of the various methods of security analysis used by financial analysts with industry-specific knowledge. Financial analysts’ industry knowledge is a favored and appreciated attribute by fund managers and institutional investors. Understanding analysts’ use of industry-specific valuation models, which are the main value drivers within different industries, will enhance our understanding of important aspects of value creation in these industries. This paper contributes to the broader understanding of how financial analysts in various industries approach valuation, offering insights that can be beneficial to a wide range of stakeholders in the financial market.

This paper systematically reviews existing research to consolidate the current understanding of analysts’ use of valuation models and factors. It aims to demystify what can often be seen as a “black box”, shedding light on the valuation tools employed by financial analysts across diverse industries.

The use of industry-specific valuation models and factors by analysts is a subject of considerable interest to both academics and investors. The predominant model in several industries is P/E, with some exceptions. Notably, EV/EBITDA is favored in the telecom, energy and materials sectors, while the capital goods industry primarily relies on P/CF. In the REITs sector, P/AFFO is the most commonly employed model. In specific sectors like pharmaceuticals, energy and telecom, DCF is utilized. However, theoretical models like RIM and AEG find limited use among analysts.

This is the first paper systematically reviewing the research on analyst’s use of industry-specific stock valuation methods. It serves as a foundation for future research in this field and is likely to be of interest to academics, analysts, fund managers and investors.

This study investigates the progress of the Broad-Based Black Economic Empowerment (B-BBEE) Act 2003, and its associated Chartered Accountancy Profession Sector Code. In doing so, we explore why B-BBEE affirmative action has not yet achieved the Code's representational intentions, including systemic subtleties inhibiting this success.

Using semi-structured interviews, we explore the lived experiences of Black aspirant Chartered Accountants (CAs) undertaking articles in global audit firms. The experiences are thematically analysed, embracing a comprehensive theoretical approach that encompasses professional and social closure, as well as boundary work, to adequately understand why affirmative transformational endeavours persistently face uphill tasks. The utilisation of multifaceted theorisation is deemed essential for a more nuanced portrayal of the intricacies inherent in the CA profession in South Africa.

The narratives presented by Black aspiring CAs unveil a complex web of exclusionary practices entrenched in institutionalised historical, professional, and social contexts. The multifaceted nature of closures, symbolised by racial, cultural, and linguistic factors, significantly impacts the experiences of Black trainees. The findings furthermore show that deliberate intervention beyond compliance with the Government’s framework is necessary for meaningful transformation.

The paper brings to the fore the current lived experiences of underrepresented Black CAs in global auditing firms. In doing so, these firms are empowered with incremental knowledge of the prevailing challenges and can thus make tangible improvements towards authentic transformation. Additionally, the results help in tracking the advancements made through affirmative action, acting as a feedback loop for future developments in transformation policy.

Contributing to the critical accounting literature, our study extends scholarship on the barriers faced by CAs and the limitations in their capacity to challenge these obstacles within global audit firms. We offer practical policy-focused recommendations that, if implemented, can address the complex socio-political realities obstructing the success of affirmative action. By sharing first-hand accounts, our study aims to empower auditing firms and other related stakeholders with actionable insights, enabling them to improve genuine inclusivity and foster equitable representation in the accounting profession.

The purpose of this paper is to compare nine different models to evaluate consumer credit risk, which are the following: Logistic Regression (LR), Naive Bayes (NB), Linear Discriminant Analysis (LDA), k-Nearest Neighbor (k-NN), Support Vector Machine (SVM), Classification and Regression Tree (CART), Artificial Neural Network (ANN), Random Forest (RF) and Gradient Boosting Decision Tree (GBDT) in Peer-to-Peer (P2P) Lending.

The author uses data from P2P Lending Club (LC) to assess the efficiency of a variety of classification models across different economic scenarios and to compare the ranking results of credit risk models in P2P lending through three families of evaluation metrics.

The results from this research indicate that the risk classification models in the 2013–2019 economic period show greater measurement efficiency than for the difficult 2007–2012 period. Besides, the results of ranking models for predicting default risk show that GBDT is the best model for most of the metrics or metric families included in the study. The findings of this study also support the results of Tsai et al. (2014) and Teplý and Polena (2019) that LR, ANN and LDA models classify loan applications quite stably and accurately, while CART, k-NN and NB show the worst performance when predicting borrower default risk on P2P loan data.

The main contributions of the research to the empirical literature review include: comparing nine prediction models of consumer loan application risk through statistical and machine learning algorithms evaluated by the performance measures according to three separate families of metrics (threshold, ranking and probabilistic metrics) that are consistent with the existing data characteristics of the LC lending platform through two periods of reviewing the current economic situation and platform development.

The purpose of our study is to investigate the effects of politically-connected boards (PCBs) on over-(under-)investment in labor. We also examine the impacts of the supervisory board (SB)’s optimal tenure on the association between PCBs and over-investment in labor.

We constructed the proxy for PCBs using a dummy variable set to 1 (one) if a firm has politically-connected boards and zero (0) otherwise. For the robustness check, we used the number of politically-connected members on the boards as the proxy for PCBs.

We find that the presence of PCBs reduces over-investment in labor. Consistent with our prediction, we found no significant association between PCBs and under-investment in labor. We also find that the SB with optimal tenure strengthens the negative association between PCBs and over-investment in labor. In our channel analysis, we find that the presence of PCB mitigates over-investment in labor through a higher dividend payout ratio.

Due to the unavailability of data in firms’ annual reports regarding the number of poorly-skilled and highly skilled employees, we were not able to examine the effect of low-skilled and high-skilled employees on over-investment in labor. Also, we were not able to examine over-(under-)investment in labor by drawing a distinction between general (generalist) and firm-specific human capital (specialist) as suggested by Sevcenko, Wu, and Kacperczyk (2022). Generally, it is more difficult for managers to hire highly-skilled employees, specialists in particular, thereby driving the choice of either over- or under-investing in the labor forces. In addition, in the firms’ annual reports, there is no information regarding temporary employees. Therefore, if and when such data become available, this would provide another avenue for future research.

Our study offers several practical implications and insights to stakeholders (e.g. insiders or management, shareholders, investors, analysts and creditors) in the following ways. First, our study highlights significant differences between capital investment and labor investment. For instance, labor investment is considered an expense rather than an asset (Wyatt, 2008) because, although such investment is human capital and is not recognized on the firm’s balance sheet (Boon et al., 2017). In addition, labor investment is characterized by: its flexibility which enables firms to make frequent adjustments (Hamermesh, 1995; Dixit & Pindyck, 2012; Aksin et al., 2015), its non-homogeneity since every employee is unique (Luo et al., 2020), its direct impact on morale and productivity of a firm (Azadegan et al., 2013; Mishina et al., 2004; Tatikonda et al., 2013), and its financial outlay which affects the ongoing cash flows of a firm (Sualihu et al., 2021; Khedmati et al., 2020; Merz & Yashiv, 2007). Second, our findings reveal that the presence of PCBs could help to reduce over-investment in labor. However, if managers of a firm choose to under-invest in labor in order to obtain better profit in the short-term through cost saving, they should be aware of the potential consequences of facing a financial loss when a new business opportunity suddenly arises which requires a larger labor force. Third, our findings help stakeholders to re-focus on the labor investment. This is crucial due to the fact that labor investment is often neglected by those stakeholders because the expenditure of labor investment is not recognized on the firm’s balance sheet as an asset. Instead, it is written off as an expense in the firm’s income statement. Fourth, our findings also provide insightful information to stakeholders, suggesting that an SB with optimal tenure is more committed to a firm, and this factor plays an important role in strengthening the negative association between PCBs and over-investment in labor.

First, our findings provide a valuable understanding of the effects of PCBs on over-(under-)investment in labor. Stakeholders could use information disclosed in the financial statements of a publicly-listed firm to determine the extent of the firm’s investment in labor and PCBs, and compare this information with similar firms in the same industry sector. Second, our findings give a better understanding of the association between investment in labor and political connections , which are human and social capital that could determine the long-term survival and success of a firm. Third, for shareholders, the appointment of board members with political connections is an important strategic decision to build political capital, which is likely to have a long-term impact on the financial performance of a firm; therefore, it requires thoughtful consultation with firm insiders.

Our findings highlight the role of PCBs in reducing over-investment in labor. These findings are significant because both investment in labor and political connections as human and social capital can play an important role in determining the long-term survival and success of a firm.

The purpose of this paper is to investigate the most recurrent variables characterizing the collaborative relationships of industrial symbiosis (IS) (hereinafter also referred to as “anatomic” variables) established in the attempt to adopt circular economy (CE) by collecting evidence from a rich empirical set of implementation cases in Italy.

The current literature on IS was reviewed, and a content analysis was performed to identify and define the “anatomic” variables affecting its adoption in the circular economy. We followed a multiple-case study methodology investigating 50 cases of IS in Italy and performed a content analysis of the “anatomic” variables characterizing each case.

This research proposes the “anatomic” variables (i.e. industrial sectors involved, public actors involvement, governmental support, facilitator involvement and geographical proximity) explaining the cases of IS in the circular economy. Each “anatomic” variable is discussed at length based on the empirical evidence collected, with a particular reference to the impact on the different development strategies (i.e. “bottom-up” and “top-down”) in the cases observed.

Current literature on IS focuses on a sub-set of variables characterizing collaboration in IS. This research builds on extant literature to define a new framework of five purposeful “anatomic” variables defining IS in the circular economy. Moreover, we also collect and discuss a broad variety of empirical evidence in what is a still under-investigated context (i.e. Italy).

The purpose of this study is to provide empirical evidence on the impact of deviations from the long-run sustainable real exchange rate (RER) equilibrium on real economic growth rate applying panel autoregressive distributed lag model (ARDL) (Pooled Mean Group, Mean Group and Dynamic Fixed Effects estimators) in a dynamic heterogeneous panel setting and panel NARDL for the largest database covering 104 countries during 1995–2022 period developed by Couharde et al. (2017).

The EQCHANGE database makes available not only the equilibrium RER but also misalignments according to the Behavioral Equilibrium Exchange Rate approach for each country. One of the main objectives is to examine whether undervaluation or overvaluation RER can imply different responses on economic performance trying to differentiate between short and long run effects. Additionally, the authors consider the World Bank (WB)’s income classifications to compare the asymmetries attending to high-income, upper-middle-income, lower-middle-income and low-income levels.

Applying the panel ARDL technique, the results suggest that the RER misalignments have a negative but not significant effect on the short-run, nevertheless a negative and highly significant impact on real economic growth rate is detected on the long-run. Considering the panel NARDL, the asymmetric relationship between RER misalignment and economic growth rate is supported considering all countries in the long-run (in the short-term is not significant). In the long run is detected that undervaluation can promote economic growth rate, rather than overvaluation which can harm the economic performance. Additionally, the WB and the International Monetary Fund (IMF) income’s classifications have been applied and the long-run symmetry test is strongly rejected regardless of income group.

To the best of the author knowledge, this is the first time the non-linear panel ARDL methodology has been applied for analyzing the impact of deviations from the long-run sustainable RER equilibrium on real economic growth. This allows us to see the asymmetric effect not seen before. The panel ARDL estimation can efficiently performed regardless of the integration level of the variables, additionally, it is consistent even in the presence of endogeneity. Besides, another advantage of this method is that it is possible to reflect not only the short but also the long-run dynamics. Moreover, this analysis offers a comparison between linear panel ARDL and non-linear to compare the advantages from the former. Additionally, this study covers the largest database, in particular, 104 countries during the 1995–2022 period implemented with the Couharde et al. (2017) EQCHANGE database. Finally, it is compared the asymmetries attending to different income classifications.

The purpose of this paper is twofold: first, to propose a methodology to empirically investigate the longitudinal development of social media content concerning buildings characterized by iconic architecture and second, to report on the application of this methodology.

We collected and analyzed empirical data of social media content shared via Instagram between 2011 and 2019 on 16 buildings that can be considered iconic architecture projects. Using an automated pipeline, we collected and processed 264,000 posts and 140,000 images from Instagram for the selected case studies. By studying the posting activity of Instagram users through time series analysis and conducting content analysis of the social media posts by means of both image classification and topic modeling, we report on the development of users’ capturing and reception of the selected case studies on Instagram over time.

First, we identify two distinct time patterns of social media content: instantly popular buildings whose popularity fades over time and buildings that gradually gain popularity over time. Second, we distinguish differences in the content of social media posts: some buildings are primarily covered for their architectural features and others for their cultural function and facilities.

Using empirical investigation of Instagram data on iconic architectural projects, we have identified a correlation: buildings primarily posted for their architecture are generally also the ones to gain instant online popularity that subsequently faded over time. In contrast, buildings primarily posted for their function and facilities slowly gained popularity on the social media platform over time.

Generation Y early-career workers have the highest turnover rates ever seen. To better understand this phenomenon, this study combines the P-O values fit with the Cohort perspectives to (1) identify the work-related values of this generation, (2) explore the relation between values and turnover intentions and examine how the field of study influences this relationship and (3) verify if the turnover intentions materialized one year after the first data collection.

We interviewed 71 early-career workers and applied thematic analysis to identify the value categories. A classification decision tree tested whether the field of study influences the relation between values and turnover intentions. A post-test was conducted to determine whether the reported turnover intentions were materialized one year later.

Thematic analysis yielded 285 themes that were grouped into 12 values’ categories. Decision trees revealed that the combination of values that most predicted turnover was substantially different between Finance graduates (more instrumental and future-oriented values) and Innovation and Entrepreneurship graduates (more social and job-oriented values). The post-test confirmed that the number of respondents who reported an intention to quit their jobs during the interview with us and did quit one year later was statistically significant.

To our knowledge, this is the first study that uses critical incident interviews to explore the work-related values of this specific cohort and their relation to turnover. Our findings on the moderating effects of the field of study are unprecedented. We also identified three new work-value categories, and, to our knowledge, this is the first study that used decision trees to explore the relation between values and turnover.

The objectives of this study are as follows: RO1: to determine the key enablers that encourage the adoption of sustainable practices; RO2: to create a structural relationship model between the enablers that have been discovered and RO3: to use dependence and driving power to classify and analyse these factors.

In total, 11 variables were extracted through systematic literature review and experts opinion, and a group interview with more than 15 experts who have an experience of more than 10 years in academics was conducted. Generally odd in numbers, they presented each pair of variables and took their opinion. Using “Interpretative Structural Modelling” and “Cross-Impact Multiplication Applied to Classification” (ISM MICMAC) analysis, a model describing the structural relationship between these factors has been formulated to know the conceptual framework.

The study shows that the digital payment and waste treatment as the linkage or mediating variables. Top management commitment (TMC), regulatory framework and transparency are the drivers or independent variables. Green logistics, sustainable packaging and production are the dependent variables. Further, the resulting hierarchy and contextual linkages among variables shed light on key dimensions for decision-making. Green logistics, sustainable packaging and production are interconnected, playing a significant role, in the framework and influenced by the lower level variables. Transparency should be given due consideration to ensure sustainable competitive advantage.

(1) Owing to a lack of time and resources, the researchers have been able to explore only limited variables; more factors or enablers can be taken in future for further research. (2) Money constraint is also there. (3) One major limitation is that this research includes opinion from different experts which are dynamic in nature and keep on changing from time to time. (4) Primary research can also be done with the help of different software like partial least squares and structural equation modelling (PLS-SEM), covariance-based structural equation modelling (CB-SEM) and Statistical Package for the Social Sciences (SPSS) by taking same variables or merging new variables with the given variables of this paper. (5) With the help of this paper, the author tried to give an overview of some important enablers which are helpful in sustainability. Given a time in future, more research can be done by taking different sectors or a specific sector.

Findings are helpful for policymakers about effective strategies for promoting sustainability. They provide evidence-based insights into the potential impact of policy interventions and help shape the development of environmental regulations and initiatives. Sustainability is a global issue. Thus, this research paper contributes to international discussions and collaborations on environmental conservation and sustainable development goals (SDGs). They may influence diplomatic efforts to address transnational environmental challenges. Companies can use research findings to improve their sustainability practices and develop environmentally friendly products and services. Understanding sustainability enablers can help businesses reduce their ecological footprint while remaining economically viable. Research on sustainability enablers often involves exploring new technologies and innovations. Practical implications include the development and adoption of renewable energy sources, waste reduction techniques and sustainable agricultural practices. Sustainable communities rely on a range of enablers, from renewable energy infrastructure to local governance structures. Research informs community development efforts by identifying best practices and effective strategies for building resilient, environmentally friendly neighbourhood.

This research paper contributes to raising awareness about sustainability issues among the public. It also provides insights into the importance of sustainable practices and their impacts on society. Sustainability enablers can influence individual and collective behaviour, and they may encourage people to adopt more sustainable lifestyles and consumption habits. Sustainability research often addresses issues of equity and social justice. Understanding the enablers of sustainability can help identify strategies to ensure that sustainable practices benefit all segments of society, including marginalized communities.

This research paper endeavour to identify key sustainability development enablers (SDEs) by consolidating numerous factors within a single study. While several research papers have explored various sustainability enablers separately, no previous study has delved into their interactions. Furthermore, there is a dearth of research on classifying enablers based on the fuzzy MICMAC analysis. The ISM-based model presented here serves as a valuable resource for both practitioners and academics, aiding in their comprehension of the framework of relationships.