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Wine packaged tours as a specific aspect of wine tourism have so far been neglected in research, for this reason, the purpose of this study is to study the key elements for the success of the wine tour in Tuscany (Italy), evaluating the points of strength and weakness.

The study combines approaches of text mining, sentiment analysis and natural language processing, drawing on data from the TripAdvisor platform, obtaining through an automatic procedure 9,616 reviews from 600 tours in the years 2010–2020.

The authors identified six elements of successful wine tours expressed by research subjects: tour guide; logistical aspects; the quality of the wine; the quality of the food; complementary tourist and recreational activities; the landscape and historic villages. The key strength associated with success was the integration of the leading wine product with food, landscape and historic villages, while the main criticisms were concerned with the organization and planning of the tour. Furthermore, the tour guide also plays a fundamental role in consumer satisfaction.

The limitations of the method were linked to the origin of the data used. The main one is that TripAdvisor does not allow you to have social and personal information about the tourist who wrote the review; therefore, the methods are substantially complementary to the traditional survey through questionnaires.

The proposed model can be used both by professionals to improve the quality of their products and by policymakers to promote the territorial development of quality wine-growing areas.

The proposed model can be useful for policymakers to promote the territorial development of quality wine-growing areas.

The methodology we tested is easily transferable to many countries and to the authors’ knowledge, for the first time attempts to combine multidimensional scaling, sentiment analysis and natural language processing approaches.

This study aims to examine foreign direct investment (FDI) factors and develops a rational framework for FDI inflow in Western European countries such as France, Germany, the Netherlands, Switzerland, Belgium and Austria.

Data for this study were collected from the World development indicators (WDI) database from 1995 to 2018. Factors such as economic growth, pollution, trade, domestic capital investment, gross value-added and the financial stability of the country that influence FDI decisions were selected through empirical literature. A framework was developed using interpretable machine learning (IML), decision trees and three-stage least squares simultaneous equation methods for FDI inflow in Western Europe.

The findings of this study show that there is a difference between the most important and trusted factors for FDI inflow. Additionally, this study shows that machine learning (ML) models can perform better than conventional linear regression models.

This research has several limitations. Ideally, classification accuracies should be higher, and the current scope of this research is limited to examining the performance of FDI determinants within Western Europe.

Through this framework, the national government can understand how investors make their capital allocation decisions in their country. The framework developed in this study can help policymakers better understand the rationality of FDI inflows.

An IML framework has not been developed in prior studies to analyze FDI inflows. Additionally, the author demonstrates the applicability of the IML framework for estimating FDI inflows in Western Europe.

In this study, three models were empirically compared, the DeLone and McLean model, the Seddon model and the Modified Seddon model, by measuring the impact of a business intelligence system (BIS) in companies in Peru. After that, the mediators and dependent constructs were analysed to determine if they were behaving properly (a good level of variance explanation and significant relations with others constructs). The study used a sample of 104 users of the BIS, from companies in several important economic sectors, in a quasi-voluntary context and with six constructs: information quality, system quality, service quality, system dependence (system use), user satisfaction and perceived usefulness (individual impact).

To interpret the results, the authors used structural equations. The idea was to look for the best fit and explanations for the outcomes. The main difference in these models is that the DeLone and McLean model considers system dependence (system use) as a part of information system success, but in the Seddon model, it is a consequence of it.

The Seddon model seems to show the best fit and explanation for the outcomes. After that, a review of the system use construct was realised, because of its limited variance explained and the few significant relations with other constructs, to improve its explanation power in future research.

It is estimated that the sample includes more than 15 per cent of all the companies that use a BISs in Peru, so the size of the sample is adequate, but it is not entirely random and therefore limits the generalizability of outcomes. Besides that, a sample size that is bigger could be better for the sake of making a more detailed analysis, permitting the use of some items with less power, or the use of another statistical procedure for structural equations such as the Asymptotical Distribution Free, permitting a more detailed analysis (Hair et al., 2006).

Business intelligence (BI), one of the most important components of information systems (IS), is playing a very relevant role in business in this time of high competition, high amounts of data and new technology. Currently, companies feel pressured to respond quickly to change and complicated conditions in the market, needing to make the correct tactical, operational and strategic decisions (Chugh and Grandhi, 2013). BI is one of the most important drivers of the decade (Gartner, 2013). Big companies of IS are creating special units specialised in BI, helping companies become more efficient and effective in daily operations.

For Chinese companies that cross-list in Chinese A share and Hong Kong (H share) markets, the H share price has been consistently lower than the A share price by an average of 85% in recent years. This is puzzling because most institutional differences between the two markets have been eliminated since 2007. The purpose of this study is to explain the puzzle of the price difference of A+H companies.

Using all A and H share Chinese firms in the period 2007–2013 and a simultaneous equations approach, this study identifies three new explanations for the recent price difference.

First, utilizing a unique earning quality measure that is directly related to non-persistent components of fair value accounting under International Financial Reporting Standards (IFRS), this study finds that the lower the earnings quality, the lower the H share price relative to the A share price, and hence the greater the price difference. Second, the higher the myopic investor ownership in A share firms, the larger the A share price relative to the H share price. Third, the short-selling mechanism introduced to the A share market since 2010 helps reduce the price difference.

First, this study identifies three new explanations for the puzzle of the AH price difference which remains substantial even after the institutional and accounting standards differences between the two markets were eliminated. Second, we examine the impact of the implementation of fair value accounting under IFRS in an emerging market on the pricing difference of cross-listed shares and reveal that it can induce an unintended negative consequence on the pricing difference of cross-listed shares. Third, this study contributes to the literature on short sales by providing its mitigating role in pricing differences across two different markets. Finally, this study makes improvements in research design, which utilizes a unique measure of earnings quality that is directly related to the implementation of IFRS and a simultaneous equations approach that minimizes endogeneity concern.

This paper aims to investigate whether professional data analysts’ trust of black-box systems is increased by explainability artifacts.

The study was developed in two phases. First a black-box prediction model was estimated using artificial neural networks, and local explainability artifacts were estimated using local interpretable model-agnostic explanations (LIME) algorithms. In the second phase, the model and explainability outcomes were presented to a sample of data analysts from the financial market and their trust of the models was measured. Finally, interviews were conducted in order to understand their perceptions regarding black-box models.

The data suggest that users’ trust of black-box systems is high and explainability artifacts do not influence this behavior. The interviews reveal that the nature and complexity of the problem a black-box model addresses influences the users’ perceptions, trust being reduced in situations that represent a threat (e.g. autonomous cars). Concerns about the models’ ethics were also mentioned by the interviewees.

The study considered a small sample of professional analysts from the financial market, which traditionally employs data analysis techniques for credit and risk analysis. Research with personnel in other sectors might reveal different perceptions.

Other studies regarding trust in black-box models and explainability artifacts have focused on ordinary users, with little or no knowledge of data analysis. The present research focuses on expert users, which provides a different perspective and shows that, for them, trust is related to the quality of data and the nature of the problem being solved, as well as the practical consequences. Explanation of the algorithm mechanics itself is not significantly relevant.

The purpose of the paper is to describe and analyze differences in patients' quality perceptions of private and public primary care centers in Sweden.

The article explores the differences in quality perceptions between patients of public and private primary care centers based on data from a large patient survey in Sweden. The survey covers seven dimensions, and in this paper the measure Overall impression was used for the comparison. With more than 80,000 valid responses, the survey covers all primary care centers in Sweden which allowed for a detailed analysis of differences in quality perceptions among patients from the different categories of owners.

The article contributes with a detailed description of different types of private owners: not-for-profit and for profit, as well as corporate groups and independent care centers. The results show a higher quality perception for independent centers compared to both public and corporate groups.

The small number of not-for-profit centers (21 out of 1,117 centers) does not allow for clear conclusions for this group. The results, however, indicate an even higher patient quality perception for not-for-profit centers. The study focus on describing differences in quality perceptions between the owner categories. Future research can contribute with explanations to why independent care centers receive higher patient satisfaction.

The results from the study have policy implications both in a Swedish as well as international perspective. The differentiation between different types of private owners made in this paper opens up for interesting discussions on privatization of healthcare and how it affects patient satisfaction.

The main contribution of the paper is the detailed comparison of different categories of private owners and the public owners.

During the Communist Party of China's endeavors over the past century, China has created “two miracles,” namely, large-scale and rapid economic development and long-term social stability.

The causes for China's achieving the “two miracles” lie in the adherence to the Party's leadership as the political guarantee, the scientific theoretical guidance as the ideological guarantee, the socialist system as well as the national governance system as the institutional guarantee and giving full play of people's creativity under the Party's leadership as the driving force guarantee.

From a political economy point of view, the theoretical logic behind the creation of the “two miracles” is that the combination of the state capacity and the scaling up of markets under the Party's leadership contributes to the rapid economic development and further the long-term social stability based on the financial foundation laid by rapid economic development. The historical experience of the “Two Miracles” can be summed up as the cultivation of state capacity under the leadership of the Party, the synergy and complementarity between the central government and local governments, the combination of development planning and market mechanisms, and the coordination of selective, functional and inclusive industrial policies.

It is necessary to judge future development trends from a medium and long-term development perspective, further promote the co-evolution of the state and the market, reshape the growth regime for high-quality development, fully tap the potential of domestic demand and create a “people-centered” economic development model so as to continue the “two miracles” and achieve a miracle of high-quality development in the second century.

Through geographical indications, the European Union aims to stimulate economies, especially in lagging rural regions, and to help consumers recognise and locate quality products from specific regions. The highly uneven distribution of geographical indications, and with that the unequal benefits of this policy, have been identified and discussed in the scientific literature on food and rural development.

Using a statistical analysis of the distribution of geographical indications, the paper tests the validity of several theoretical explanations that are offered in the literature for the uneven spatial distribution.

From this assessment, the paper concludes, amongst others, that common single-cause explanations for the uneven distribution of labels in Europe have weak explanatory value. Rather, the uneven distribution is based on a complex set of causes, with different effects at national and regional level. Moreover, the findings highlight that in contrast to its aim, the policy does not seem to benefit especially lagging rural regions.

The analysis of the uneven distribution of labels in Europe offered here suggests that a distinction should be drawn between the mechanisms resulting in regional food products versus the mechanisms resulting in regional food labels, such as geographical indications.

Professional service firm (PSF) performance depends on the accumulation and application of specialist knowledge to find customised solutions to customer problems. However, available research has not examined whether knowledge acquired from external sources affects PSF outcomes by strengthening professionals’ beliefs rather than only by increasing technical competency. Drawing on self-efficacy theory, this study tests a model that links the quality of content acquired from external sources and the credibility of those sources to professionals’ self-efficacy and, in turn, to PSF outcomes (solution quality and firm performance). In particular, this paper aims to consider the case of professional content exchanged through professional social media.

A cross-sectional research design was applied. Data were collected from a sample of 208 accountants, auditors and lawyers who used professional social media and were analysed using covariance-based structural equation modelling.

When accessing professional content from external sources, source credibility and content quality are significant antecedents of professionals’ self-efficacy, which, in turn, has positive effects on PSF outcomes (solution quality and PSF performance).

Self-efficacy plays a key role in the link between knowledge acquired from external sources (professional content) and PSF outcomes.

This study provides recommendations and actionable insights for PSFs, professionals and other actors who create and exchange professional content. Professional associations may also take an active role by contributing and sharing credible and high-quality content, using, for example, professional social media.

This paper advances the current understanding of the effects of professionals’ access to content from external sources on PSF outcomes. It provides an explanation of these effects based on the enhancement of professionals’ beliefs instead of their technical competencies, as indicated in previous research. In addition, it is the first research effort to consider professional social media as a communication channel to exchange content that affects the self-efficacy of PSF professionals.

In the COVID-19 pandemic, consumers increasingly opt for, or are forced to, use home delivery services. The authors study retailers' decisions regarding “delivery mode”, which is about outsourcing (vs. insourcing) the delivery service to a traditional delivery company or an unbranded carrier and its effects on consumers' perceived overall quality, perceived hygienic quality, and subsequently, willingness to stay with the firm beyond the pandemic.

A pre-test, an experiment and a post-test were conducted with participants from the UK (Total N = 380).

The results of this study show that (1) in a pandemic, perceived hygienic quality overshadows perceived service quality as a key determinant of consumers' choices, and (2) while consumers have a relatively negative view of the hygienic level of unbranded carriers, they do not differentiate between traditional delivery carriers and retailer-branded carriers. Thus, they are equally interested in using the services of the latter ones.

This study shows that during a health crisis, consumers change their hierarchy of motivations to reflect the new protection motivations. The authors usher perceived hygienic quality as a variable that should be seriously considered as both a tactical and a strategic variable affecting the attractiveness of alternative home delivery methods and consumers' intentions to continue using them after the pandemic.