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School principals make sense of multiple messages, policies, and contexts within their school environments. The purpose of this paper is to examine specifically how school leaders make sense of hiring and subjective evaluation of on‐the‐job teacher performance.

This qualitative study drew from 42 interviews with 21 principals from a mid‐sized Florida school district. Two rounds of semi‐structured interviews (one to two hours each) were conducted with the informants over two summers (2005‐2006). The multi‐year study allows the authors to assess the consistency across principal participants.

Principals' personal beliefs, background, and experiences were found to shape their conceptions and preferences for teacher characteristics. School type (e.g. elementary, secondary, levels of poverty) also influenced principals' perceptions of and preferences for specific applicant and teacher characteristics. Principals in the sample, however, showed surprising consistency towards certain characteristics (caring, subject matter knowledge, strong teaching skills) and job fit (person‐job). Sampled principals reported that each vacancy is different and is highly dependent on the position, team, and individuals. Regardless of the position or school setting, federal, state, and district mandates strongly influenced how principals made sense of the hiring process and on‐the‐job performance.

The findings underscore the complexity of the human resource functions in education and raise important questions of how school leaders reconcile personal preferences and building‐level needs with demands from the district, state, and federal levels.

The authors' findings offer important insight into the complex conceptualizations that principals hold and the balances that must be struck in the face of policy and hiring constraints. How principals make sense of teacher quality, however, has not been examined. This study contributes to the extant research and makes a theoretical contribution to studies using a cognitive frame to understand school leadership.

The purpose of this study is to analyse the role played by institutional investors in a firm’s decision to hire sustainability assurance services and to determine the benefits of sustainability assurance for the functioning of the capital market. This analysis is complemented by examining the quality of the sustainability assurance service that institutional investors demand.

The authors selected a sample of 1,564 multinational firms from 2002 to 2017. Panel data logit and generalised method of moments (GMM) regressions were estimated to consider decisions about hiring sustainability assurance services or not, and the assurance quality indexes constructed by a checklist based on the academic literature, respectively.

Institutional pressures associated with the environmental and social impacts of a firm’s activities lead to the convergence of institutional investor attitudes towards corporate sustainability, so that, regardless of their investment horizon, they promote the hiring of sustainability assurance services by corporate boards, which favours analyst precision and a reduction in the cost of capital. Long-term (LT) institutional investors exert influence through a selection mechanism, whereas short-term (ST) institutional investors exert influence through their presence on the board. Once the company has decided to provide assurance about its sustainability report, both types of institutional investors promote a higher quality of such service, although this is not well valued by the stock market.

This paper extends research on the monitoring role of institutional investors into the sustainability assurance context. Researchers may benefit from this paper’s findings when they examine the factors that drive the hiring of sustainability assurance services and their characteristics. This paper also shows that sustainability assurance services are a significant weakness due to the lack of standardisation in comparison with financial auditing, which complicates the assessment of their quality by stock market participants, thereby penalising those companies that provide more complete sustainability assurance reports.

Considering this paper’s findings, it seems advisable that regulators establish a normative framework to standardise sustainability assurance processes. The results can also be used as an orientation for both companies, to design their sustainability disclosure policies and regulators, to improve the running of the capital market.

Sustainability assurance services have a positive effect on the running of the capital market and improve external stakeholder decision-making by providing more reliable information, which, in turn, will favour the implementation of more sustainable actions that contribute to the attainment of sustainable development goals.

This is one of the first papers to analyse the effect of institutional ownership on a firm’s decision to hire sustainability assurance services and consider the effect of the institutional investors’ investment horizon – LT versus ST – and the channel – selection methods and/or active engagement – used by them to exert their influence. The authors also propose several measures of sustainability assurance quality to demonstrate the relevance of the contents of the assurance statement for the capital market in general and the institutional investors in particular.

This paper aims to investigate the effect that ownership structure (public vs private) has on the demand for high-quality auditors, specifically in the US banking industry.

The authors predict that public banks are more likely to hire a high-quality auditor than private banks and pay a higher audit fee premium for that high-quality auditor (due to higher agency costs, more demand for financial information and higher litigation risk). The authors analyze 2008–2014 banking data from the Federal Reserve using probit and OLS regression analysis to examine if there is a higher probability that public banks choose higher quality auditors and pay higher audit fees when they do so.

The results show that private banks are less likely to hire Big 4 auditors and industry-expert auditors than public banks. The authors also find that both private and public banks pay higher audit fees for Big 4 and industry-expert auditors, and that public banks pay a higher premium for Big 4 auditors and industry experts than private banks.

The findings may not be fully generalizable to other types of firms, as banking is a heavily regulated and complex industry. However, inferences from this study may be generalizable to other similar industries such as insurance or health care.

The results of this paper imply that public and private banks have differing priorities when hiring their financial statement auditor. This may be of interest to investors and auditing regulators.

The findings of this paper underscore the value of hiring an industry-expert auditor in an industry that is highly complex and regulated. This may be of interest to managers and policymakers.

Due to data restrictions, the emphasis of prior literature on the banking industry has been on public banks. This study is the first to analyze the differences between public and private banks’ demand for audit services.

This paper presents a typology exploring employers’ perceptions of the quality of available applicants and employers decisions to buy qualified staff vs. to hire available workers and then make i.e. develop them via employer-supported training.

This study uses 2015 survey data from Southwestern Ontario, Canada, based on responses from 834 employers regarding their hiring, separations, training and other HRM policies.

Among surveyed employers, 10% are “Reliants” who found the quality of available applicants to be low, yet these employers do not provide employee training. Almost half of employers (at 45%) are “Developers” who find the quality of applicants to be low but they do provide employee training. Approximately, 7% of employers are “Poachers” who find that the quality of applicants is high and do not provide employee training, while 38% are Refiners, who find the quality of applicants is high and they provide employee training.

Employers need to make their training decisions in alignment with their assessment of the quality of job applicants to whom they have access. In this paper, decisions on training and applicant quality are considered concurrently. From an academic viewpoint, the findings raise the issue as to whether other stakeholders (such as educational institutions) are sufficiently helping individuals gain the skills, credentials and work experiences that employers are seeking. If job openings are remaining unfilled because employers are unwilling to hire those available, then applicants lose, employers lose and societies lose.

Our proposed machine learning model contributes to improving the quality of Hire by providing a more nuanced and comprehensive analysis of candidate attributes. Instead of focusing solely on obvious factors, such as qualifications and experience, our model also considers various dimensions of fit, including person-job fit and person-organization fit. By integrating these dimensions of fit into the model, we can better predict a candidate’s potential contribution to the organization, hence enhancing the Quality of Hire.

Within the scope of the investigation, the competencies of the personnel working in the IT department of one in the largest state banks of the country were used. The entire data collection includes information on 1,850 individual employees as well as 13 different characteristics. For analysis, Python’s “keras” and “seaborn” modules were used. The Gower coefficient was used to determine the distance between different records.

The K-NN method resulted in the formation of five clusters, represented as a scatter plot. The axis illustrates the cohesion that exists between things (employees) that are similar to one another and the separateness that exists between things that have their own individual identities. This shows that the clustering process is effective in improving both the degree of similarity within each cluster and the degree of dissimilarity between clusters.

Employee competencies were evaluated within the scope of the investigation. Additionally, other criteria requested from the employee were not included in the application.

This study will be beneficial for academics, professionals, and researchers in their attempts to overcome the ongoing obstacles and challenges related to the securing the proper talent for an organization. In addition to creating a mechanism to use big data in the form of structured and unstructured data from multiple sources and deriving insights using ML algorithms, it contributes to the debates on the quality of hire in an entire organization. This is done in addition to developing a mechanism for using big data in the form of structured and unstructured data from multiple sources.

This study aims to examine the effects of audit quality on earnings management and cost of equity capital (COE) considering the impact of two owner types: government ownership and foreign ownership.

The study uses a panel data set of 236 Vietnamese firms covering the period 2007 to 2017. Because the two main dependent variables of the COE capital and the absolute value of discretionary accruals receive fractional values between zero and one, the paper uses the generalised linear model (GLM) with a logit link and the binomial family in regression analyses. The paper uses numerous audit quality measures, including hiring Big 4 auditors or the industry-leading Big 4 auditor, changing from non-Big 4 auditors to Big 4 auditors or the industry-leading Big 4 auditor, and the length of Big 4 auditor tenure. Big 4 companies include KPMG, Deloitte, EY and PwC, whereas the non-big 4 are the other audit companies.

The study finds a negative relationship between audit quality and both the COE capital and income-increasing discretionary accruals. The effects of audit quality on discretionary accruals and the COE capital depend on the ownership levels of two important shareholders: the government and foreign investors. Foreign ownership is negatively associated with discretionary accruals; however, the effect is more pronounced in the sub-sample of state-owned enterprises (SOEs), the firms where the government owns 50% or more equity, than in the sub-sample of Non-SOEs.

To the best of the knowledge, no prior similar study exists that used the GLM with a logit link and the binomial family regression. Global investors may be interested in understanding how unique institutional settings and capital markets of each country impact the financial reporting quality and cost of capital. Further, policymakers of developing markets may have incentives to improve the quality of financial reporting and reduce the cost of capital which should result in attracting more foreign investments.

Organizational psychologists and human resource management (HRM) practitioners often have to select the “right fit” candidate by manually scouting data from various sources including job portals and social media. Given the constant pressure to lower the recruitment costs and the time taken to extend an offer to the right talent, the HR function has to inevitably adopt data analytics and machine learning for employee selection. This paper aims to propose the “Quality of Hire” concept for employee selection using the person-environment (P-E) fit theory and machine learning.

The authors demonstrate the aforementioned concept using a clustering algorithm, namely, partition around mediod (PAM). Based on a curated data set published by the IBM, the authors examine the dimensions of different P-E fits and determine how these dimensions can lead to selection of the “right fit” candidate by evaluating the outcome of PAM.

The authors propose a multi-level fit model rooted in the P-E theory, which can improve the quality of hire for an organization.

Theoretically, the authors contribute in the domain of quality of hire using a multi-level fit approach based on the P-E theory. Methodologically, the authors contribute in expanding the HR analytics landscape by implementing PAM algorithm in employee selection.

The proposed work is expected to present a useful case on the application of machine learning for practitioners in organizational psychology, HRM and data science.

Intellectual Capital (IC) is essential to the success of new technology-based firms. A key component of IC is human capital. Human capital is shown to affect firm innovation, growth, and survival positively. This paper investigates the signaling effect of technology-based start-ups’ initial stock of IC on obtaining skilled human capital.

The researcher employs signaling theory to analyze primary data concerning the firm’s initial stock of IC and subsequently hired human capital from founders of 236 technology-based new ventures in the USA Hypotheses are tested through a set of hierarchical linear regressions.

This study demonstrates that the firms’ IC, in the form of quantity of founders with doctorates and intellectual property, correlates with the quality (average education level) of subsequently hired technical and business human capital. In addition, the quantity of founders with doctorates is correlated with the quantity of subsequently hired technical human capital.

The paper collects retrospective data from founders of technology-based new ventures. While human capital is important for technology-based firms’ innovation and growth, little research has investigated potential connections between firms’ initial IC and subsequent hiring of top-level human capital. This paper investigates these connections explicitly.

The purpose of this study is to investigate whether hiring a high-quality auditor (i.e. industry specialist) depends on corporate governance indicators after controlling a different level of agency conflicts (ACs).

This paper uses logistic regressions on 12,449 firm-year samples of Taiwanese public companies from 1998 to 2011 by grouping the samples into three categories (i.e. low, medium and high AC).

The results show that the corporate governance indicators can explain the decision of auditor selection only in low and medium AC groups, which suggest that there may be a complementary relationship between external (i.e. auditors) and internal governance when the ACs are mild.

The paper contributes to the ongoing debate between the complementary and substitutable effects. When the internal ACs are controlled, the internal governance and auditor selection are complemented.