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This article reports the results of a randomized field experiment that tested the effects of a new business intervention among managers of small- and medium-sized enterprises (SMEs) in England.

Individual managers (learners) were randomly assigned in clusters (companies) to either an intervention group (265 learners; 40 SMEs) receiving a novel virtual, blended training program designed to stimulate a change in management behavior or a no-intervention group (118 learners; 22 SMEs).

The results show that the primary objective of changing management behavior to use more of an Operational Coaching™ style of management has been achieved (to a statistically significant level), and this is against the backdrop of the devastating COVID-19 pandemic. Positive trends in SME productivity metrics were also observed in the intervention group companies.

These important results could be indicative of the economic and productivity impact that a change in management behavior could have, and they warrant serious further investigation.

This work analyses quality management (through the European Foundation for Quality Management-EFQM-model) and transformational leadership in hotels in Spain.

The study analyses 102 5-star and 5-star large luxury hotels that answer a questionnaire on transformational leadership and the EFQM model. It analyses the degree of importance of quality and transformational leadership in hotels, the significant differences between groups of hotels (according to stars, size, modality and type of product) and the association between transformational leadership and quality.

The results show the levels of quality and transformational leadership, minor significant differences between groups and an association between the two variables. In general, chain-affiliated hotels have a higher level of leadership and a more advanced employee and process management than independent hotels. Also, those hotels that focus on a vacational product show a lower attention to the strategy dimension in the EFQM model. The number of employees is not an important factor to adopt quality. Finally, transformational leadership allows hotels to advance in the development of quality management.

Although there are studies on quality management that show the importance of leadership for quality, there are few studies that examine transformational leadership and quality in the same study, mainly in the tourism industry, and especially in the case of the hotel industry.

The purpose is to detect the nonlinearity wholesale rice price formation process in Italy in the 1995–2017 period.

A nonlinear smooth transition autoregressive (STAR)-type dynamics model is used.

Wholesale rice prices are significantly affected by variations in the international price of rice as well as variations in Arborio price.

The limitations include policy recommendations for the production and commercialization of rice in Italy.

Understanding rice pricing dynamics and nonlinearity behavior is pivotal for the survival of the entire European and Italian rice supply chain.

In the extant literature, no evidence exists on non-linearity of rice prices in Italy.

This research develops a data-driven statistical model capable of predicting a US Army Reserve (USAR) unit staffing levels based on unit location demographics. This model provides decision makers an assessment of a proposed station location’s ability to support a unit’s personnel requirements from the local population.

This research first develops an allocation method to overcome challenges caused by overlapping unit boundaries to prevent over-counting the population. Once populations are accurately allocated to each location, we then then develop and compare the performance of statistical models to estimate a location’s likelihood of meeting staffing requirements.

This research finds that local demographic factors prove essential to a location’s ability to meet staffing requirements. We recommend that the USAR and US Army Recruiting Command (USAREC) use the logistic regression model developed here to support USAR unit stationing decisions; this should improve the ability of units to achieve required staffing levels.

This research meets a direct request from the USAREC, in conjunction with the USAR, for assistance in developing models to aid decision makers during the unit stationing process.

This paper aims to explain real exchange rate fluctuations by means of a model including both standard fundamentals and two alternative measures of inflation expectations for five inflation targeting countries (the UK, Canada, Australia, New Zealand and Sweden) over the period January 1993–July 2019.

Both a benchmark linear autoregressive distributed lag (ARDL) model and a nonlinear autoregressive distributed lag (NARDL) specification are considered.

The results suggest that the nonlinear framework is more appropriate to capture the behaviour of real exchange rates given the presence of asymmetries both in the long and short run. In particular, the speed of adjustment towards the purchasing power parity (PPP) implied long-run equilibrium is three times faster in a nonlinear framework, which provides much stronger evidence in support of PPP. Moreover, inflation expectations play an important role, with survey-based ones having a more sizable effect than market-based ones.

The focus on linearities and the estimation of a NARDL model, which is shown to outperform the linear ARDL model both within sample and out of sample, is an important contribution to the existing literature which has rarely applied this type of framework; the choice of an appropriate econometric method also makes the policy implications of the analysis more reliable; in particular, monetary authorities should aim to achieve a high degree of credibility to manage them and thus currency fluctuations effectively; the inflation targeting framework might be especially appropriate for this purpose.

This methodological paper demonstrates how service firms can use digital technologies to quantify and predict customer evaluations of their interactions with the firm using unstructured, qualitative data. To harness the power of unstructured data and enhance the customer-firm relationship, the use of computerized text analysis is proposed.

Three empirical studies were conducted to exemplify the use of the computerized text analysis tool. A secondary data analysis of online customer reviews (n = 2,878) in a service industry was used. LIWC was used to conduct the text analysis, and thereafter SPSS was used to examine the predictive capability of the model for the evaluation of customer-firm interactions.

A lexical analysis of online customer reviews was able to predict evaluations of customer-firm interactions across the three empirical studies. The authenticity and emotional tone present in the reviews served as the best predictors of customer evaluations of their service interactions with the firm.

Computerized text analysis is an inexpensive digital tool which, to date, has been sparsely used to analyze customer-firm interactions based on customers' online reviews. From a methodological perspective, the use of this tool to gain insights from unstructured data provides the ability to gain an understanding of customers' real-time evaluations of their service interactions with a firm without collecting primary data.

This research contributes to the growing body of knowledge regarding the use of computerized lexical analysis to assess unstructured, online customer reviews to predict customers' evaluations of a service interaction. The results offer service firms an inexpensive and user-friendly methodology to assess real-time, readily available reviews, complementing traditional customer research. A tool has been used to transform unstructured data into a numerical format, quantifying customer evaluations of service interactions.