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This study aims to examine the role of technological capabilities in the competitive advantage of supply chains for the “countryside” hub of São Paulo state.

An interdisciplinary approach was adopted by combining the strategic orientation of supply chain management as a link between market and operational activities, with technological capabilities as an essential factor for competitive advantage. This exploratory, mixed-method study was conducted in the Campinas technology hub, which has become important for its available skilled workforce, and broad and structured supply chain. The study sample included ten companies and dealt with the techniques of content analysis and non-parametric statistics – the Mann–Whitney U technique.

The results revealed that strategic supply chain management has an impact on technological capabilities and competitive advantage.

They also showed that technological capabilities have a partial mediating effect on competitive advantage.

The purpose of this paper is to evaluate the economic benefits of managing an outpatient appointments system with technological innovations.

This study uses a quantitative methodological procedures aiming to evaluate the cost-benefit relation and also the payback of the management and operation of an outpatient appointments system with technological innovations.

This study found a great benefit-cost relation of 30.6 showing the great economic value and social impact of managing an outpatient appointments regulation system with technological innovations.

This study presents contribution to the literature discussion about the economic evaluation of the benefits of managing and operating more effective outpatient appointments systems because of important technological innovations.

This paper presents and discusses the most important and commonly used strategies and technological innovations to deal with and to manage an outpatient appointment regulation system aiming to reduce the patient no-show rates.

The findings of this study show a great benefit-cost relation of about 30.6 which is being reverted to the society.

There not exist many similar studies in the pertinent literature, mostly with the Brazilian contexts.

This study investigates the impact of derivatives as risk management strategy on the value of Malaysian firms. This study also examines the interaction effect between derivatives and managerial ownership on firm value.

The study examines 200 nonfinancial firms engaged in derivatives for the period 2012–2017 using the generalized method of moments (GMM) to establish the influence of derivatives and managerial ownership on firm value. The study refers to two related theories (hedging theory and managerial aversion theory) to explain its findings. Firm value is measured using Tobin's Q with return on assets (ROA) and return on equity (ROE) as robustness checks.

The study found evidence on the positive influence of derivatives on firm value as proposed by the hedging theory. However, the study concludes that managers less hedge when they owned more shares based on the negative interaction between derivatives and managerial ownership on firm value. Hedging decision among managers in Malaysian firms therefore does not subscribe to the managerial aversion theory.

This study focuses on the derivatives (foreign currency derivatives, interest rate derivatives and commodity derivatives) and managerial ownership that is deemed relevant and important to the Malaysian firms. Other forms of ownership such as state-/foreign owned and institutional ownership are not covered in this study.

This study has important implications to managers and investors. First is on the importance of risk management using derivatives to increase firm value, second, the influence of derivatives and managerial ownership on firm value and finally, the quality reporting on derivatives exposure by firms in line with the required accounting standard.

There is limited empirical evidence on the impact of derivatives on firm value as well as the influence of managerial ownership on hedging decisions of Malaysian firms. This study analyzes the influence of derivatives on firm value during the period in which reporting on derivatives in financial reports is made mandatory by the Malaysian regulator, hence avoiding data inaccuracy unlike the previous studies on Malaysia. This study therefore fills the gap in the literature in relation to the risk management strategies using derivatives in Malaysia.

This study examines the impact of hedging on firm value of Sharīʿah compliant firms (SCFs) in a non-linear framework.

This study employs the system-GMM for dynamic panel data to examine the influence of derivatives usage on firm value (Tobin's Q, ROA and ROE). The sample comprised of 59 non-financial SCFs engaged in derivatives from 2000 to 2017 (18 years). The Sasabuchi-Lind-Mehlum (SLM) test for U-shaped is performed to confirm the existence of the non-linear relationship.

This study concludes that hedging significantly contributes to firm value of SCFs based on the non-linear framework. This study suggests that, first, the non-linear relationship occurs due to the different degree of derivatives usage and risk. Second, firms practice selective hedging to maintain the upside potential of firm value.

This study has important implications. First, the importance of risk management via derivatives to increase firm value, second, the evidence of selective hedging from the non-linear relationship between derivatives and firm value and third, the need for quality reporting on derivatives engagement by firms in line with the required accounting standard on derivatives.

This study fills the gap in the literature in relation to the risk management strategies of SCFs in three aspects. First, re-examines the relationship using recent data. Second, examines the relationship in the non-linear framework as the limited studies found in the literature on Malaysian firms are only based on linear relationship. Third, determines whether hedging undertaken by firms is optimal as this can only be addressed using the non-linear framework. This study is robust to the various definitions of firm value (Tobin's Q, ROA and ROE) and non-linear methodologies.

Though the circular economy (CE) is a current buzzword, this still lacks a precise definition. In the absence of a clear notion of what that term includes, actions taken by the government and companies may not be well informed. In particular, those actions need to consider what people mean when people talk about the CE, either to refocus people's decisions or to undertake a more effective communications strategy.

Since people voice people's opinions mainly through social media nowadays, special attention has to be paid to discussions on those media. In this paper, the authors focus on Twitter as a popular social platform to deliver one's thoughts quickly and fast. The authors' research aim is to get the perceptions of people about the CE. After collecting more than 100,000 tweets over 16 weeks, the authors analyse those tweets to understand the public discussion about the CE. The authors conduct a frequency analysis of the most recurring words, including the words' association with other words in the same context and categorise them into a set of topics.

The authors show that the discussion focuses on the usage of resources and materials that heavily endanger sustainability, i.e. carbon and plastic and the harmful habit of wasting. On the other hand, the two most common good practices associated with the CE and sustainability emerge as recycling and reuse (the latter being mentioned far less). Also, the business side of the CE appears to be relevant.

The outcome of this analysis can drive suitable communication strategies by which companies and governments interested in the development of the CE can understand what is actually discussed on social media and call for the attention.

This paper addresses the lack of a standard definition the authors highlighted in the Introduction. The results confirm that people understand CE by looking both at CE's constituent activities and CE's expected consequences, namely the reduction of waste, the transition to a green economy free of plastic and other pollutants and the improvement of the world climate.

This paper aims to review the research on accounting professionalisation in China to develop insights into how the research is developing, offer a critique of the research to date and outline future research directions and opportunities.

This paper adopts a methodological approach of systematic literature review, as suggested by Tranfield et al. (2003) and Denyer and Tranfield (2009), to identify, select and analyse the extant literature on the Chinese public accounting profession. In total, 68 academic works were included in the review process.

This paper finds that the extant literature has produced fruitful insights into the processes and underlying motivation of accounting professionalisation in China, demonstrating that the Chinese experience has differed, to a large extent, from the hitherto mainly Anglo-American-dominated understandings of accounting professionalisation. However, due to the lack of common theoretical vernacular and an agreed upon focus, the extant literature illustrates a fragmented and contradictory picture, making attempts to accumulate prior knowledge in the field increasingly difficult.

This paper focusses only on research published in English. Consequently, the scope of review has been limited as some works published in languages other than English may be excluded.

This paper provides one of the pioneering exercises to systematically review the research on accounting professionalisation in China. It explores significant issues arising from the analysis and provides several suggestions for furthering the research effort in this field.

This study aims to propose a non-linear model to describe the effect of macroeconomic shocks on delinquency rates of three kinds of bank loans. Indeed, a wealth of literature has recognized significant evidence of the linkage between macro conditions and credit vulnerability, perceiving the importance of the high amount of bad loans for economic stagnation and financial vulnerability.

Generally, this linkage was represented by linear relationships, but the strong dependence of bank loan default on the economic cycle, subject to changes in regime, could suggest non-linear models as more appropriate. Indeed, macroeconomic variables affect the performance of bank’s portfolio loan, but such a relationship is subject to changes disturbing the stability of parameters along the time. This study is an attempt to model three different kinds of bank loan defaults and to forecast them in the case of the USA, detecting non-linear and asymmetric behaviors by the adoption of a Markov-switching (MS) approach.

Comparing it with the classical linear model, the authors identify evidence for the presence of regimes and asymmetries, changing in correspondence of the recession periods during the span of 1987–2017.

The data are at a quarterly frequency, and more observations and more extended research periods could ameliorate the MS technique.

The good forecasting performance of this model could be applied by authorities to fine-tune their policies and deal with different types of loans and to diversify strategies during the different economic trends. In addition, bank management can refer to the performance of macroeconomic conditions to predict the performance of their bad loans.

The authors show a clear outperformance of the MS model concerning the linear one.

This study examines the theoretical and empirical relationships between LM&SS, human resource management (HRM), climate for LM&SS and outcomes (employee well-being and performance) in hospitals. As part of this research, the authors examine the interplay between “hard” and “soft” practices for LM&SS and “soft” HR practices.

A cross-sectional, multisite survey study covering all internal service units at all eight Dutch university hospitals was conducted (42 units, N = 218 supervisors, N = 1,668 employees), and multivariate multilevel regression analyses were performed.

A systems approach involving “soft” LM&SS practices that are specifically HR-related has a positive effect (β is 0.46) on a climate for LM&SS. A climate for LM&SS is not related to perceived performance or employee health. It is, however, positively related to employee happiness and trusting relationships (both βs are 0.33). We did not find that a climate for LM&SS had a mediating effect.

This study shows that a balanced approach involving both “hard” and “soft” factors is crucial to achieving the desired breadth and depth of LM&SS adoption at the macro, meso, and micro levels. The authors found that a climate for LM&SS positively affects employee well-being in hospitals.

In their attempt to create mutual gains for both their organization and their employees, hospitals that adopt LM&SS should foster a climate for LM&SS by embracing a balanced approach consisting of both “hard” and “soft” practices, thereby internalizing LM&SS at the macro, meso, and micro levels.

This is one of the first studies to examine in-depth the impact of “hard” and “soft” LM&SS on both employee well-being (subdivided into different components) and performance in healthcare, as well as the role of “soft” HRM in this relationship. Linking LM&SS, HRM and outcomes to a climate for LM&SS is relatively a new approach and has led to a deeper understanding of the mechanisms underpinning the internalization of LM&SS in healthcare.

Social networks have changed the communication patterns significantly. Information available from different social networking sites can be well utilized for the analysis of users opinion. Hence, the organizations would benefit through the development of a platform, which can analyze public sentiments in the social media about their products and services to provide a value addition in their business process. Over the last few years, deep learning is very popular in the areas of image classification, speech recognition, etc. However, research on the use of deep learning method in sentiment analysis is limited. It has been observed that in some cases the existing machine learning methods for sentiment analysis fail to extract some implicit aspects and might not be very useful. Therefore, we propose a deep learning approach for aspect extraction from text and analysis of users sentiment corresponding to the aspect. A seven layer deep convolutional neural network (CNN) is used to tag each aspect in the opinionated sentences. We have combined deep learning approach with a set of rule-based approach to improve the performance of aspect extraction method as well as sentiment scoring method. We have also tried to improve the existing rule-based approach of aspect extraction by aspect categorization with a predefined set of aspect categories using clustering method and compared our proposed method with some of the state-of-the-art methods. It has been observed that the overall accuracy of our proposed method is 0.87 while that of the other state-of-the-art methods like modified rule-based method and CNN are 0.75 and 0.80 respectively. The overall accuracy of our proposed method shows an increment of 7–12% from that of the state-of-the-art methods.