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The purpose of this paper is to attempt to outline the standard profile of the typical wine consumer, by identifying some relevant features that can influence his/her purchasing choices. Therefore, the purpose of the research is to identify the pre-eminent attributes for wine consumers and the different levels of importance that consumers ascribe to the attributes identified at the time of purchase.

In order to collect the necessary data, an ad hoc questionnaire was utilized. The questionnaire, which was anonymous, was directly distributed with the face-to-face method. In total, 1,500 copies of the questionnaire had been prepared. The data collected were processed through the use of the binary logistic regression model and the ordinal logistic regression model. The first binary logistic regression model allows to evaluate the dependence of the dichotomous variable on some potential predictors. The ordinal logistic regression model, known in literature as a cumulative model of proportional quotas, is generally appropriate for situations in which the ordinal response variable has discrete categories.

The results returned by the elaboration of the binary logistic regression model refer to the influence of the variables sex, age, educational status and income on the “wine consumption” result, which is a dichotomous variable. The only variables found to be statistically significant are gender and educational status. The most significant variables that emerged from the implementation of the ordinary logistic regression model are gender, brand, choice based on price, place of production, harvest and certification. The analysis carried out has shown that with reference to wine as a product, it is essential to focus on several attributes, among which there are of course quality and brand.

Although field experiments are extremely useful for testing behavioral hypotheses, they are often limited by a small sample. Future research in this area might focus on the knowledge level of sustainable wine of the consumer. In relation to the knowledge of the characteristics of the wine, it is possible to estimate the willingness to pay a surplus for a wine produced with sustainable methods by the consumer and the possible level of price premium.

The originality of the research lies mainly in a deeper knowledge of wine consumption trends. This information is useful to better define the wine market and to allow, especially to small businesses, to establish effective marketing strategies in relation to the real preferences of consumers and the decision-making process of choice put in place by them. In order to achieve this, the influence of all the variables on the “satisfaction of wine consumption” result was evaluated. The strength of this paper is the use of an adequate statistical approach based on the use of models, typical of inferential statistics, to reach conclusions that can be extended to the entire population of wine growers.

Amidst the scarcity of resources, it is undisputable that an effective public procurement performance measurement system (PMS) is required particularly in county governments, especially for Kenya to realize its ambitions in devolved governance system. County governments cannot be effectively evaluated on their performance if the long-term, strategic impact of public procurement processes and projects is not captured. Arising from this backdrop, this study aims to determine the predictors of strategic procurement performance metrics (SPPM) adoption in public procurement PMS of county governments.

Anchored on institutional theory and public sector scorecard model, a survey research design was adopted where data were collected through census from 115 respondents working in procurement, finance and stores department of Kakamega county government. Data were collected using questionnaire (75.56% response rate) and key informant interviews, and analyzed by using multiple regression model and ordinal logistic regression models.

Multiple regression model and ordinal logistics regression revealed that national government support negatively and significantly, and regulatory framework positively and significantly affects the adoption of SPPM.

There is need for formal mechanism that will enable the national government in partnership with the council of governors to be proactively involved in developing procurement performance measurement capacity of county governments. This study’s findings also provide suggestions for a working regulatory framework required for the adoption of SPPM by county governments.

This work adds value to the prevailing body of knowledge on public procurement PMS in the public sector.

The significant benefits associated with cruise tourism have mobilized port industry, as progressively, a large number of ports are developing cruise operations. Although increasing cruise traffic is a major goal for cruise ports, homeporting constitutes a strategic target of the majority of ports due to its greater economic benefits for both the port itself and its hinterland. The establishment of homeport traffic in a cruise port is subjected to a variety of port internal and external factors. Taking these into account, the paper aims at defining elements that affect the potential of a cruise port to become a homeport.

A sample of 47 Mediterranean ports is selected to form the basis for the implementation of an ordinal regression model which links the likelihood of ports to attract homeport traffic with seven explanatory variables which emerged from relevant literature and are split in the main categories of ports’ and hinterlands’ characteristics. To fit the model into the paper’s data, ports are divided into three categories based on their homeport cruise traffic.

The results of the empirical model signify that both internal and external factors affect the potential of a port to become a cruise homeport. Concerning the internal factors, adequate infrastructure allowing the facilitation of the last generation of cruise ships and the presence of a private enterprise in ports’ operation seems to foster homeport traffic. Additionally, efficiency in operations seems to be a crucial element. On the other hand, the connectivity of port’s; hinterlands, tourist infrastructure and the level of economic growth are proved to be the hinterlands’ elements which increase the likelihood of a port to attract additional homeport traffic.

The model forms a comprehensive evaluation basis for whether a cruise port should intensify its pursuit of homeport traffic, as the estimated coefficients could support port and local authorities to understand their competitive position against other ports and spot their strengths and weaknesses.

The paper contributes in the research dealing with the identification of crucial elements of homeporting from the port’s point of view. Although, it should be mentioned that previous efforts targeting on revealing the characteristics affecting the homeporting potential of ports mostly have been based on questionnaires and expert judgements or empirical models in which the total – and not the homeport traffic – was used as the dependent variable. With the proposed empirical model, home-porting choice analysis is transferred, on the one hand, from the stated preferences level to the revealed preferences level and, on the other hand, from an indirect to a direct approximation of the issue.

The purpose of this paper is to evaluate the influence of budgeting process elements (budget participation, preparation, implementation and evaluation) on budget performance of government.

A cross-sectional survey was administered among budget officers from government agencies, departments, and units in Malaysia. Descriptive and regression analyses were used to examine the relationship between the budgeting process and budget performance.

The findings revealed the significant influence of the two predictors: (1) budget participation and (2) budget implementation and evaluation, on budget performance. Both have positive and significant impacts on budget performance. However, budget preparation appeared to have no significant relationship with budget performance, although there is positive effect.

This study provides empirical evidence on the budgeting process factors that influence budget performance. The findings hopefully are of interest to government officials, especially frontline bureaucrats, who seek to ensure that budget performance meets expectations in Malaysia and other countries.

This study analyzes antecedents explaining the lack of resilience in family-owned firms. Our model suggests that family-owned firms’ strategic behaviors and heterogeneity explain a particular crisis outcome: a lack of recovery.

Our evidence is based on a sample of 842 European family-owned firms. We complement regression analysis results with fuzzy-set qualitative comparative analysis (fsQCA).

Our results show that lack of resilience is relevant. In fact, in our sample, 60% of family firms (FFs) failed to recover their sales. This evidence supports the role played by exploitation and exploration behavior as well as family heterogeneity in explaining the lack of recovery.

Our results may offer guidance to practitioners and policymakers on the pathways that explain the lack of resilience.

Although it is unlikely that an external crisis such as COVID-19 will occur again to the same extent, other threatening events may occur and impact FFs. Understanding how FFs can avoid non-recovery is crucial: it can inform managers on how to deal with stressful events and provide guidance to economic authorities on how to help FFs around the world avoid non-recovery, which affects the economy.

First, the study contributes to FF research by offering a theoretical explanation for the different effects of FF attributes on non-recovery in the context of a global crisis. Second, it contributes to the literature on organizational resilience by examining explorative and exploitative behaviors as antecedents of FF non-recovery. Third, we show the usefulness of combining fsQCA and regression analysis to understand complex phenomena.

本研究擬分析家族企業缺乏復原力的原因。我們的模型暗示了家族企業的策略性行為和異質性是可闡明一個特殊的危機後果：企業未能成功恢復。

我們的證據是建基於一個涵蓋842間歐洲家族公司的樣本。我們以模糊集質性比較分析，去補充迴歸分析的結果。

研究結果顯示，缺乏復原力是有相關性的。事實上，在我們的樣本裡，有百分之六十的家族企業未能恢復其銷售量。研究結果提供了證據，確認了若要闡明缺乏復原力的原因，我們必須瞭解開發和探索行為、以及家族異質性所扮演的角色。

我們的研究結果，或許可為從業人員和政策制訂者提供引導，幫助他們找出缺乏復原力的原因。

雖然像2019冠狀病毒病的外部危機以同樣的程度發生的機會是很微的，但其它有威脅性的事件或會發生，並會影響家族企業; 因此，瞭解家族企業如何能避免缺乏復原力的弊端是非常重要的。這可讓經理瞭解如何處理令人憂慮的事件，亦可為經濟事務當局的高層人員提供引導，幫助他們瞭解如何協助世界各地的家族企業，去避免影響經濟的復原乏力。

首先，本研究在家族企業的探討上作出了貢獻。研究人員在一個全球危機的背景下提出了理論解釋，讓人們明白家族企業的屬性，如何會對復原乏力產生各種影響。其次，本研究在探討組織彈性的文獻方面作出了貢獻，因研究人員對作為引發家族企業復原乏力因素的探索和開發行為、進行了探討和研究。最後，研究人員展示了若要瞭解複雜的事物或現象，把模糊集質性比較分析和迴歸分析結合起來運用是有效的。

The operation safety of the high-speed railway has been widely concerned. Due to the joint influence of the environment, equipment, personnel and other factors, accidents are inevitable in the operation process. However, few studies focused on identifying contributing factors affecting the severity of high-speed railway accidents because of the difficulty in obtaining field data. This study aims to investigate the impact factors affecting the severity of the general high-speed railway.

A total of 14 potential factors were examined from 475 data. The severity level is categorized into four levels by delay time and the number of subsequent trains that are affected by the accident. The partial proportional odds model was constructed to relax the constraint of the parallel line assumption.

The results show that 10 factors are found to significantly affect accident severity. Moreover, the factors including automation train protection (ATP) system fault, platform screen door and train door fault, traction converter fault and railway clearance intrusion by objects have an effect on reducing the severity level. On the contrary, the accidents caused by objects hanging on the catenary, pantograph fault, passenger misconducting or sudden illness, personnel intrusion of railway clearance, driving on heavy rain or snow and train collision against objects tend to be more severe.

The research results are very useful for mitigating the consequences of high-speed rail accidents.

The purpose of this paper is to examine how organisational activities that formally provide employees with work autonomy explain the performance of open innovation (OI).

The study reports the results of mediation analyses conducted on the basis of survey data from 307 firms.

The economic benefits of both inbound and outbound OI are fully captured only if firms provide employees with time, freedom and independence. The results show that employee autonomy fully mediates the relationship between openness and innovation sales, while the adoption of inbound OI is positively associated with the introduction of new products.

The opening of innovation induces managers to provide employees with discretion, as OI requires high levels of flexibility and experimentation.

The paper addresses theoretically and empirically the role of job design in the implementation of OI, while also distinguishing between the effects of inbound and outbound practices on innovation performance.

Although the Net Promoter Score (NPS) index is simple, NPS has weaknesses that make NPS's interpretation misleading. The main criticism is that identical index values can correspond to different levels of customer loyalty. This makes difficult to determine whether the company is improving/deteriorating in two different years. The authors describe the application of statistical tools to establish whether identical values may/may not be considered similar under statistical hypotheses.

Equal NPSs with a “similar” component composition should have a two-way table satisfying marginal homogeneity hypothesis. The authors compare the marginals using a cumulative marginal logit model that assumes a proportional odds structure: the model has the same effect for each logit. Marginal homogeneity corresponds to null effect. If the marginal homogeneity hypothesis is rejected, the cumulative odds ratio becomes a tool for measuring the proportionality between the odds.

The authors propose an algorithm that helps managers in their decision-making process. The authors' methodology provides a statistical tool to recognize customer base compositions. The authors suggest a statistical test of the marginal distribution homogeneity of the table representing the index compositions at two times. Through the calculation of cumulative odds ratios, the authors discriminate against the hypothesis of equality of the NPS.

The authors' contribution provides a statistical alternative that can be easily implemented by business operators to fill the known shortcomings of the index in the customer satisfaction's context. This paper confirms that although a single number summarizes and communicates a complex situation very quickly, the number is ambiguous and unreliable if not accompanied by other tools.

This paper aims to propose a solution for detecting and grading diabetic retinopathy (DR) in retinal images using a convolutional neural network (CNN)-based approach. It could classify input retinal images into a normal class or an abnormal class, which would be further split into four stages of abnormalities automatically.

The proposed solution is developed based on a newly proposed CNN architecture, namely, DeepRoot. It consists of one main branch, which is connected by two side branches. The main branch is responsible for the primary feature extractor of both high-level and low-level features of retinal images. Then, the side branches further extract more complex and detailed features from the features outputted from the main branch. They are designed to capture details of small traces of DR in retinal images, using modified zoom-in/zoom-out and attention layers.

The proposed method is trained, validated and tested on the Kaggle dataset. The regularization of the trained model is evaluated using unseen data samples, which were self-collected from a real scenario from a hospital. It achieves a promising performance with a sensitivity of 98.18% under the two classes scenario.

The new CNN-based architecture (i.e. DeepRoot) is introduced with the concept of a multi-branch network. It could assist in solving a problem of an unbalanced dataset, especially when there are common characteristics across different classes (i.e. four stages of DR). Different classes could be outputted at different depths of the network.

The main aim of this study is to explore the appropriate factors in measuring COVID-19-induced supply chain disruptions and the impact of these disruptions on the economic vulnerability of small-scale farmers in Sri Lanka.

The findings revealed that most of the farmers have continued to cultivate even during the pandemic despite several challenges which affected their economic status. Therefore, it is concluded that COVID-19-induced transportation and demand disruptions exacerbated the economic vulnerability of small-scale farmers over the disruptions in supply and production.

The findings of this study are crucial for formulating novel policies to improve the sustainability of the Sri Lankan agricultural sector and alleviate the poverty level of Agri-communities in the countryside. As farming is a vital sector in the economy, increased attention ought to be given on facilitating farmers with government-encouraged loans or allowances for their financial stability. Further, the respective government authorities should develop programs for importing and distributing adequate quantities of fertilizers among all the farmers at controlled prices so that they can continue their operations without any interruption. Moreover, the government could engage in collaboratively work with private organizations to streamline the Agri-input supply process. There should be a government initiative for critical consideration of the issues of farming families and their continued motivation to engage in agriculture. Thus, farmers' livelihoods and agricultural prosperity could be upgraded through alternative Agri-inputs and marketing strategies, providing financial assistance, encouraging innovative technology, etc.

Despite the significance and vulnerability of the vegetable and fruit sector in Sri Lanka, there is a limitation in the empirical studies conducted on the supply chain disruptions caused by COVID-19 measures and their implications on the farmers' livelihood. Furthermore, previous empirical research has not employed adequate quantitative tools to analyze the situation or appropriate variables in evaluating COVID-19-induced disruptions. Hence, the current study explored the appropriate factors for measuring COVID-19-induced supply chain disruption using exploratory factor analysis. Then, the impact of those factors on the economic vulnerability of the small scale farmers was revealed through the ordinal logistics regression analysis.