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This study aims to analyze how the interplay between hard and soft elements of total quality management (TQM) produces the conditions for sustaining success in the quest for quality.

A qualitative analysis (Gioia method) was carried out on an original dataset collected through both direct and indirect methods (i.e. archival sources, interviews and observations) to generate a new interpretive framework.

The interpretative framework identifies four categories of elements: trigger elements create the starting conditions for a quality virtuous cycle; benchmarking tools set the standards of performance; improvement tools enable exploration of the space of possible alternative practices and finally, catalytic forces allow the institutionalization of effective techniques discovered in this search process into new standards.

The findings the authors present in this paper are derived by a single case study, limiting the generalizability of our results in other settings.

This study has three implications: first, the design of trigger elements is critical for the success of any TQM initiative; second, the interplay of improvement and benchmarking tools at several levels should be coherent and third, to exploit the potential of TQM, efforts should be devoted to the dissemination of new effective practices by means of catalyzing elements.

The model provides a more specific understanding of the nature and purpose of the hard and soft elements of TQM and the dynamic interaction between the two classes of elements over time.

Metropolitan areas suffer from frequent road traffic congestion not only during peak hours but also during off-peak periods. Different machine learning methods have been used in travel time prediction, however, such machine learning methods practically face the problem of overfitting. Tree-based ensembles have been applied in various prediction fields, and such approaches usually produce high prediction accuracy by aggregating and averaging individual decision trees. The inherent advantages of these approaches not only get better prediction results but also have a good bias-variance trade-off which can help to avoid overfitting. However, the reality is that the application of tree-based integration algorithms in traffic prediction is still limited. This study aims to improve the accuracy and interpretability of the models by using random forest (RF) to analyze and model the travel time on freeways.

As the traffic conditions often greatly change, the prediction results are often unsatisfactory. To improve the accuracy of short-term travel time prediction in the freeway network, a practically feasible and computationally efficient RF prediction method for real-world freeways by using probe traffic data was generated. In addition, the variables’ relative importance was ranked, which provides an investigation platform to gain a better understanding of how different contributing factors might affect travel time on freeways.

The parameters of the RF model were estimated by using the training sample set. After the parameter tuning process was completed, the proposed RF model was developed. The features’ relative importance showed that the variables (travel time 15 min before) and time of day (TOD) contribute the most to the predicted travel time result. The model performance was also evaluated and compared against the extreme gradient boosting method and the results indicated that the RF always produces more accurate travel time predictions.

This research developed an RF method to predict the freeway travel time by using the probe vehicle-based traffic data and weather data. Detailed information about the input variables and data pre-processing were presented. To measure the effectiveness of proposed travel time prediction algorithms, the mean absolute percentage errors were computed for different observation segments combined with different prediction horizons ranging from 15 to 60 min.

This study examines whether certification improves the dimensions of total quality management (TQM) and whether the impact of certification is similar across companies of different sizes and industries. The benefits of certification for companies have been widely discussed in recent years. The general debate has been partly marked by the dispute about whether companies will benefit more from certification or the implementation of TQM. This debate has led to numerous studies on the benefits of certification; however, few studies simultaneously have examined traditional TQM issues and the requirements of the new quality standard, ISO 9001: 2015, as well as the updated European Foundation for Quality Management (EFQM) criteria.

This study was conducted via a survey of Finnish SMEs and covered both industrial and service companies. The study comprehensively compared industrial companies with service companies and small companies with medium-sized companies.

In industrial and small enterprises, certification clearly has a positive effect on the dimensions of TQM, but a similar effect was not observed in medium-sized enterprises or in the service sector.

This is one of the first studies to examine the effect of certification on TQM in different types of SMEs while simultaneously considering EFQM and ISO 9001:2015 in Finland. The significant originality of this research lies in the formation of a comprehensive research framework for the dimensions of TQM.

This paper aims to evaluate the technical efficiency of the health-care systems in 21 selected middle-income countries during the period (2000–2017) and determine the source of inefficiency whether it is transient (short run) or persistent (long run).

The study uses the stochastic frontier analysis technique through employing the generalized true random effects model which overcomes the drawbacks of the previously introduced stochastic frontier models and allows for the separation between unobserved heterogeneity, persistent inefficiency and transient inefficiency.

Persistent efficiency is lower than the transient efficiency; hence, there are more efficiency gains that can be made by the selected countries by adopting long-term policies that aim at reforming the structure of the health-care system in the less efficient countries such as South Africa and Russia. The most efficient countries are Vietnam, Mexico and China which adopted a social health insurance that covers almost the whole population with the aim of increasing access to health-care services. Also, decentralization in health-care has assisted in adopting health-care policies that are suitable for both the rural and urban areas based on their specific conditions and health-care needs. A key success in the implementation of the adopted long-term policies by those countries is the continuous monitoring and evaluation of their outcomes and comparing them with the predefined targets and conducting any necessary modifications to ensure their movement in the right path to achieve their goals.

Although several studies have evaluated the technical efficiency both across and within countries using non-parametric (data envelopment analysis) and parametric (stochastic frontier analysis) approaches, to the best of the authors’ knowledge, this is the first attempt to evaluate the technical efficiency of selected middle-income countries during the period (2000–2017) using the generalized true random effects stochastic frontier analysis model.

The aim of this paper is to provide an account of the period prior to the creation of the Global Reporting Initiative (GRI): a body that was critical to the institutionalization of sustainability reporting (SR). By examining this “pre-history,” we bring to light the actors, activities and ways of thinking that made SR more likely to be institutionalized once the GRI entrepreneurship came to the fore.

The paper revisits a time period (the 1990s) that has yet to be formally written about in any depth and traces the early development of what became SR. This material is examined using a constructivist understanding of regulation.

The authors contend that a convergence of actors and structural conditions were pivotal to the development of SR. Specifically, this paper demonstrates that a combination of actors (such as epistemic communities, carriers, regulators and reporters) as well as the presence of certain conditions (such as the societal context, analogies with financial reporting, environmental reporting and reporting design issues) contributed to the development of SR which was consolidated (as well as extended) in 1999 with the advent of the GRI.

This paper theorizes (through a historical analysis) how SR is sustained by a network of institutional actors and conditions which can assist reflection on future SR development.

This paper brings together empirical material from a time that (sadly) is passing from living memory. The paper also extends the use of a conceptual frame that is starting to influence scholarship in accounting that seeks to understand how norms develop.

While the economic literature mostly tackled discrimination looking at labour costs, this work focuses on its relation to labour productivity, arguing that discrimination may worsen the performance of female employees. In this view, it represents a source of allocative inefficiency, which contributes to reducing output.

Female discrimination is both a social and an economic problem. In social terms, consolidated gender stereotypes impose constraints on women’s behaviour, worsening their overall well-being. In economic terms, women face generally worse labour market conditions. Using long-run Italian data spanning from 1861 to 2009, the authors propose a novel measure of female discrimination based on the observed frequency of discriminating epithets. Following social capital theory, the authors distinguish between structural and voluntary discrimination, and use Data Envelopment Analysis for time series data to assess the extent of inefficiency that each component of discrimination induces in the production process.

The results draw the trajectory of female discrimination in Italy and provide evidence in favour of the idea that female discrimination reduces productive efficiency. In particular, the structural component of female discrimination, although less sizeable than the voluntary component, plays a major role, especially in recent years, where more stringent beauty standards fuel looks-based discrimination.

The contribution of this work is twofold. First, based on contributions from social sciences different from economics, it proposes a novel theoretical framework that explores the effect of discriminatory language on labour productivity. Second, it introduces a novel and direct measure of female discrimination at the country level, based on the bidirectional link between language and culture. The indicator is easily understood by policymakers and may be used to evaluate the effectiveness of anti-discrimination policies.

The purpose of this study is to explore the impact of sustainability (environmental, social and governance or ESG) practices on the financial performance (FP) of the Nordic financial industry.

The study covers a sample selection of observations for a total of 152 firm-years for 39 financial companies within the Nordic region (Sweden, Denmark, Finland and Norway) for the business years including 2015–2019. Data regarding ESG and FP indicators were extracted from the Thomson Reuters Eikon database in July 2020. This is a quantitative study using regression and a generalized method of moments.

Using static and dynamic estimators, the authors found both positive and negative impacts of sustainability practice on FP. The authors identified a negative relationship between ESG practices and FP (return on invested capital, return on equity and earnings per share). The authors identified a positive relationship between governance and return on assets.

A key contribution to the accounting literature is the finding that there is a risk for financial firms in adopting sustainability practices, as they follow a logic that contradicts the purely economic rationale. On the other hand, the positive relationship between governance and FP helps not only companies but also regulators and researchers to understand the positive impact of a good governance structure.