Consumer payment choice during the crisis in Europe: a heterogeneous behaviour?

In this research paper we investigate the use of payment media from consumers during a financial crisis. The scene is Europe in 2015 and the aftermath - or the very peak for some countries - of the Eurozone crisis. The contrast in the scene is augmented through researching countries at the centre of Eurozone crisis versus far more stable Economies. In the first group and in order of severity of the crisis' impact: Greece, Cyprus and to a lesser extent Spain. In the latter group Sweden and UK. We deployed a quantitative survey-based study for which the instrument was originally constructed in the medium of English and translated (and back-translated) in Greek and Spanish, and was delivered both hand-to-hand (printout) and online via Survey monkey. Descriptive statistics are presented over the totally 1003 gathered questionnaires and a comparative analysis is performed illustrating indeed an heterogeneous behaviour among the five countries under investigation. All the above comprise the empirical part of our research, that follows naturally and complements the theoretical one: a deductive model of the hierarchy of payment media - and the respective changes of - during periods of financial distress. Within that model our main hypothesis is formed around the regional differences and the impact of the crisis in the use of cash as a payment medium, both confirmed by our empirical evidence to a large extent. So during the Eurozone crisis: a) the use of cash as a payment medium is evident, and b) this is more the case in countries mostly affected from the crisis - most notably Greece

Social Collateral and consumer payment media during the economic crisis in Europe

In this research paper we investigate the relationship between economic crises and the changes in levels of social collateral, as well as the indirect changes in the use of payment media from consumers as a result of the latter. The scene is Europe in 2015 and the Eurozone crisis involving countries mostly hit form the crisis: Greece, Cyprus and to a lesser extent Spain, versus less affected economies like Sweden and UK. We use and analyse questions focusing on social collateral, taken from a much broader research instrument - a questionnaire with 54 questions that have been used in a series of studies focusing in the use of payment media during 2015. From a total of 1003 gathered questionnaires a comparative analysis is performed through time and space focusing on three periods: before the start of the crisis in 2008, after that, and during the last 12 months; in terms of geographical dispersion, the aforementioned five countries are researched. Our empirical results provide some preliminary evidence indicating an heterogeneous behaviour among the five countries under investigation, as well as a clear change over time - partially explained by the impact of the crisi

When the bank is closed, the cash is king; ... not! A qualitative longitudinal study in the payment media used in Greece during and after the three-week period of governmentally-imposed Bank-holiday and respective capital controls

In this research paper we investigate changes in payment media used from consumers as a result of extreme financial restrictions. The motivation comes from the summer of 2015 in Greece where after failure for an agreement between Greece and the Troika (EU, IMF and ECB) for an extension of lending support from the latter, the Greek government decided to close the banks for three weeks; and apply capital controls still in place ten months after the event - however gradually relaxed. Methodologically we adopted grounded theory and through this a fully qualitative and longitudinal study comprised of three series (every six months) of in-depth interviews with individual citizens (on behalf of their households) over a period of one calendar year. We aim to investigate research changes in payment media used during and after the period when the banks were closed, as well as permanent changes in consumer and social behavior. Acknowledging that with this methodological approach reaching statistical significant results is very difficult to be achieved, we do however seek and to a great extend provide insight in what really happened during and after the events, and one thing came out again and again: people turned more into the use of debit cards, and secondary to online banking and to a lesser extent to credit cards; the later came with an inevitable raise of household debt. Cash use was only temporarily increased and more evidently during the three-week event, while all the previous aforementioned results had of a more permanent nature, as illustrated from the longitudinal analysis

Transformation Strategies for the Supply Chain: The Impact of Industry 4.0 and Digital Transformation

This research focuses on the impact of 'Industry 4.0' and 'Digital Transformation' on information sharing and decision making across the supply chain (SC). Following a qualitative approach, the findings are threefold: First, it is shown that the possibility of an entire SC integration based on new technologies is still at distance. Current burdens are the missing willingness to exchange far-reaching information even with long-term partners and the missing technological interface standards in order to enable a trouble-free communication alongside the SC. Second, the impact of Industry 4.0 and the Digital Transformation on decision making is greatly connected to information sharing. An increasing amount of decisions is prepared, recommended or even fully automated by information systems. However, usually, the human being still has the last word. Third, companies' preparations for these impacts differ greatly. Whereas some companies rely on classical phase-based strategies and long-term visions, others do not have a long-term plan at all

Clustering, Forecasting and Forecasting clusters: using k-medoids, k-NNs and random forests to improve forecasting

Data analysts when facing a forecasting task involving a large number of time series, they regularly employ one of the following two methodological approaches: either select a single forecasting method for the entire dataset (aggregate selection), or use the best forecasting method for each time series (individual selection). There is evidence in the predictive analytics literature that the former is more robust than the latter, as in individual selection you tend to overfit models to the data. A third approach is to firstly identify homogeneous clusters within the dataset, and then select a single forecasting method for each cluster (cluster selection). This research examines the performance of three well-celebrated machine learning clustering methods: k-medoids, k-NN and random forests. We then forecast every cluster with the best possible method, and the performance is compared to that of aggregate selection. The aforementioned methods are very often used for classification tasks, but since in our case there is no set of predefined classes, the methods are used for pure clustering. The evaluation is performed in the 645 yearly series of the M3 competition. The empirical evidence suggests that: a) random forests provide the best clusters for the sequential forecasting task, and b) cluster selection has the potential to outperform aggregate selection

Forecasting for Social Good: Relative performance of methods for forecasting major projects

Forecasting for Social good, most notably the socio-economic impact of major high-impact projects - like Olympic games or space exploration -is a very difficult but also extremely important task; not only for the resources allocated in such project but predominantly for the great expectations around them. This study evaluates the performances of Unaided Judgment (UJ), Structured Analogies (SA) and semi-Structured Analogies (s-SA) as well as Interaction Groups (IG) in forecasting the impact of such projects. The empirical evidence reveals that the use of s-SA Analogy leads to accuracy improvement compared to UJ. This improvement in accuracy is greater when introducing pooling of analogies through interaction in IG. A smaller scale experiment run to compare Delphi with IGs with inconclusive results

Forecasting and planning for special events in the pulp and paper supply chains

Due to global warming, flood is an increasing threat to companies operating in the pulp and paper industry. The impact of this threat needs to be managed. We deploy a qualitative investigation into how paper manufacturers can forecast and mitigate the impact of special events, most notably floods, across their supply chains. A grounded theory approach using semi-structured interviews held with supply chain consultants in three stages allowed for topic categories emerging during previous interviews to be explored. Analysis of these interviews uncovered tactics unique to the pulp and paper industry. The findings are three-fold. First, paper manufacturers should focus on basic forecasting methods which they are capable of, such as subscribing to flood warnings, rather than poorly executing advance machine learning forecasts. Second, planning is of equal importance to forecasting: integrated business planning should guide the process. Third, business execution should involve a proactive approach to decision-making which trusts data and has people that nurture and drive the process

Long-Term Economic Forecasting with Structured Analogies and Interaction Groups

In this study, we employ judgmental forecasting techniques, Structured Analogies and Interaction groups for long-term forecasting. The aim of the paper is not to evaluate forecasting accuracy per se but to highlight the potential of such techniques in this so complex and challenging task. The case study is about Saudi Arabia and its aim to adopt a diversification strategy to reduce its dependency on the oil sector, where oil revenue consists 90% of its budget currently. The study has four phases: Unaided Judgment, Structured Analogies, and Interaction Groups with Structured Analogies - all three using disguised data – before finally working on the undisguised case study under review over a significant amount of time. Adopting judgmental methods are attributed to three main reasons: in an attempt to derive long-term economic forecasts about Saudi Arabia’s ability to diversify its investments, to discover the impact of different factors on financial and economic outlooks, and to explore the main reasons for deviating the accuracy of financial and economic forecasts

An empirical investigation of water consumption forecasting methods

Many regions on earth face daily limitations in the quantity and quality of the water resources available. As a result, it is necessary to implement reliable methodologies for water consumption forecasting that will enable the better management and planning of water resources. This research analyses, for the first time, a large database containing data from 2 million water meters in 274 unique postal codes, in one of the most densely populated areas of Europe, which faces issues of droughts and overconsumption in the hot summer months. Using the R programming language, we built and tested three alternative forecasting methodologies, employing univariate forecasting technique