Correlated daily time series and forecasting in the M4 competition

We participated in the M4 competition for time series forecasting and here describe our methods for forecasting daily time series. We used an ensemble of five statistical forecasting methods and a method that we refer to as the correlator. Our retrospective analysis using the ground truth values published by the M4 organisers after the competition demonstrates that the correlator was responsible for most of our gains over the naïve constant forecasting method. We identify data leakage as one reason for its success, due partly to test data selected from different time intervals, and partly to quality issues with the original time series. We suggest that future forecasting competitions should provide actual dates for the time series so that some of these leakages could be avoided by participants.     

Mobile payment and online to offline retail business models

Mobile payments are services that use mobile devices to make payments. When digitalization moves across channel boundaries, online to offline channel retail will expand. Online to offline retailing will become the future retail owner stream and retail operators will move from cross-channel or multi-channel to omni-channel. This study investigates a market survey in Taiwan developing a data mining analytics including clustering analysis and association rules based on a snowflake schema database design. The role of mobile payment is determined in terms of new retail payment mechanism that promotes a better consumer purchase experience in an online to offline business environment.     

Coloring the destination: The role of color psychology on Instagram

Color psychology plays important roles in product packaging, the retail environment, and online marketing. This study analyzed current practices related to color composition in tourism-related photographs on Instagram. Data mining and pictorial content analysis were adopted to investigate how lightness, chroma, and hue may influence Instagram posts' popularity. Generally, individuals appeared more likely to respond to brighter and more saturated destination pictures. Orange, yellow, blue, and violet contributed significantly to post popularity based on different photographic typologies. Overall, color is a complex construct that functions within diverse dimensions and can enrich tourists’ destination-related perceptions. Based on brand post popularity and color theories, the study findings provide valuable implications for effective and efficient destination promotion via Instagram. Relevant limitations and future directions are also discussed.     

The effects of handling outliers on the performance of bankruptcy prediction models

Ratio type financial indicators are the most popular explanatory variables in bankruptcy prediction models. These measures often exhibit heavily skewed distribution because of the presence of outliers. In the absence of clear definition of outliers, ad hoc approaches can be found in the literature for identifying and handling extreme values. However, it is not clear how these different approaches can affect the predictive power of models. There seems to be consensus in the literature on the necessity of handling outliers, at the same time, it is not clear how to define extreme values to be handled in order to maximize the predictive power of models. There are two possible ways to reduce the bias originating from outliers: omission and winsorization. Since the first approach has been examined previously in the literature, we turn our attention to the latter. We applied the most popular classification methodologies in this field: discriminant analysis, logistic regression, decision trees (CHAID and CART) and neural networks (multilayer perceptron). We assessed the predictive power of models in the framework of tenfold stratified crossvalidation and area under the ROC curve. We analyzed the effect of winsorization at 1, 3 and 5% and at 2 and 3 standard deviations, furthermore we discretized the range of each variable by the CHAID method and used the ordinal measures so obtained instead of the original financial ratios. We found that this latter data preprocessing approach is the most effective in the case of our dataset. In order to check the robustness of our results, we carried out the same empirical research on the publicly available Polish bankruptcy dataset from the UCI Machine Learning Repository. We obtained very similar results on both datasets, which indicates that the CHAID-based categorization of financial ratios is an effective way of handling outliers with respect to the predictive performance of bankruptcy prediction models.     

Consumer Brand Hate: Steam rolling whatever I see

This study aims to investigate negative consumer‐brand relationships by developing a “Brand Hate” concept. A hierarchical Brand Hate model is theoretically discussed in light of the psychology and consumer behavior literatures. In Study‐I the Brand Hate concept was tested with two different types of consumer brand haters, true haters, and regular haters. The study found that in the study's proposed multidimensional Brand Hate hierarchy true haters display “Boiling Brand Hate” while regular haters reveal “Seething Brand Hate.” Two additional studies were developed to examine the relationship between consumer personality traits and Brand Hate, exploring which types of consumers are more prone to feel hatred toward targeted brands. Study‐II's findings revealed a relationship between consumers who are high in personality traits of “conscientiousness” and those who Brand Hate. Study‐III's findings indicated that “self‐confident” and “competitive” consumers might also be more prone to feel hatred toward those brands that perform poorly and unethically.     

Productivity efficiency analysis of the airlines in China after deregulation

Using data envelopment analysis and the Malmquist productivity index, this paper studies productivity efficiency of various airlines in China after the Chinese government deregulated the aviation industry in 2005. The results show that the productivity efficiency of non-state-owned airlines improves rapidly and eventually exceeds state-owned airlines after the deregulation policy. Among the state-owned airlines, the productivity of the local airlines and their technical changes are better than those of the central airlines.     

A comparative efficiency analysis of Spanish and Turkish airports

AENA in Spain and DHMI in Turkey operate a large majority of the airports in their respective countries. These two airport operators share some similar characteristics, but also present many differences with respect to their management strategies. For instance, the Turkish DHMI introduced a Build-Operate-Transfer (BOT) model and concession agreements, which enables active private participation in airport management. In contrast, management and operation responsibilities at all airports in Spain –with a few exceptions-have remained with AENA. This paper utilizes a data envelopment analysis (DEA) to compare the relative efficiency of airports within AENA and DHMI for the years between 2009 and 2011. Based on the efficiency scores, it further identifies the sources of inefficiencies resulting from various management strategies and other external factors. The results indicate higher average efficiency levels at Spanish airports, but private involvement enhances efficiency at Turkish airports. The majority of the airports in Spain and Turkey operate under increasing returns to scale. Certain policy options, including a higher private involvement and improvement of the airport network by closing some inefficient airports, should be considered in order to increase the airport efficiency in both countries.     

Internet of Things and Big Data as enablers for business digitalization strategies

Digitization blurs the lines between technology and management, facilitating new business models built upon the concepts, methods and tools of the digital environment. The purpose of this study is to investigate the role of the Internet of Things (IoT) and Big Data in terms of how businesses manage their digital transformation. The paper argues that the outbreak of IoT and Big Data has resulted in a mass of disorganized knowledge. In order to make sense of the noise, a literature review was carried out to examine the studies, published in the last decade (2008–2019), that analyzed both the Internet of Things and Big Data. The results show that IoT and Big Data are predominantly reengineering factors for business processes, products and services; however, a lack of widespread knowledge and adoption has led research to evolve into multiple, yet inconsistent paths. The study offers interesting implications for managers and marketers, highlighting how the digital transformation enabled by IoT and Big Data can positively impact many facets of business. By treating IoT and Big Data as faces of the same coin, this study also sheds light on current challenges and opportunities, with the hope of informing future research and practice.     

Efficiency,total factor productivity and returns to scale in a sustainable perspective: An analysis in the European Union at farm and regional level

For global sustainability it is imperative to find a balance across the three main components of sustainable development which are the economic, social and environmental aspects. However, it is not a simple task to make these contexts compatible, usually because of economic pressures which transform them into opposed objectives. This framework occurs across several dimensions within society and the economy, where the agricultural sector is not an exception. The objective of this study is to analyse the efficiency, total factor productivity and returns to scale in an economic, social and environmental perspective in farms of the European Union (EU) regions through Data Envelopment Analysis (DEA) approaches. The research concerning the returns to scale will be complemented by the Keynesian models. Data obtained from the European Union Farm Accountancy Data Network (FADN) was considered. The results show that in maintaining or improving the levels of production in farms, it is often possible to greatly reduce, in some cases, the consumption of fertilizers and crop protection products. On the other hand, from a social perspective, some European Union regions are more generous in the salaries paid to farming workers and absorb more labour, which in a European context of unemployment, may be an interesting way to realistically look at and be engaged in the agricultural planning in a sustainable way, founding a balanced trade-off among the economic, social and environmental dimensions.     

An estimation of returns to scale of airport airsides under multiple optimal solutions in DEA

The returns to scale (RTS) nature of 37 Chinese airport airsides are investigated in this paper. Multiple optimal solutions in DEA (Data Envelopment Analysis) models may lead to error RTS estimation. To address this problem, we use the (Zhu and Shen, 1995) RTS method. The empirical study shows that all those airsides with two runways operate under decreasing RTS and those airsides with only one runway either operate in the area of increasing RTS or in the area of constant RTS.