Determinants of card payments

Goczek, Łukasz; Witkowski, Bartosz

doi:10.1080/00036846.2015.1102846

Cited by 29 publications

(22 citation statements)

References 12 publications

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“…These variables, expressing the characteristics of the place of residence, were used also when modelling the propensity for holding a payment or credit card (e.g. Goczek and Witkowski, 2016). The data on earnings and population are available with quarterly and bi-annual frequency, respectively, o Movie parameters -running time in minutes (MovieLength), genre (9 dummy variables, representing 10 genres present in the dataset), country of the producer (2 dummy variables, representing Poland and the United States), dummy for 3D sound (Sound), dummy for sequels (Sequel), number of stars from top the 20 stars according to the IMDb (Stars20), dummy for movies targeted at small children (Childr), age classification, taking values 0, 6, 7, 10, 12, 13, 15, 18 (AgeClass), age of the movie in years (year it was shown minus the year of release; MovieAge).…”

Section: Methodsmentioning

confidence: 99%

“…These studies mostly use the characteristics of the individual or the household, which are not accessible in our study (typically the viewer is not identified by the cinema). However, we proxy these factors by the average salary in the region where the cinema is located and typically the customer has the place of residence (Goczek and Witkowski, 2016).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The literature on forecasting consumer demand or behavioural patterns indicates not only to identify new determinants but also to combine those related to the product's features and its' reputation, location of the business or macroeconomic situation. Applications in the field of economics and business include, among others, predicting the credit risk (Sztaudynger, 2018), modelling credit card usage (Goczek and Witkowski, 2016) and travel behaviour (Klinger and Lanzendorf, 2016). However, in accordance with our best knowledge, the issue of modelling and forecasting cinema attendance has not been fully researched in the field yet.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting Cinema Attendance at the Movie Show Level: Evidence from Poland

Baranowski

Korczak

Zając

2020

Business Systems Research Journal

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Background: Cinema programmes are set in advance (usually with a weekly frequency), which motivates us to investigate the short-term forecasting of attendance. In the literature on the cinema industry, the issue of attendance forecasting has gained less research attention compared to modelling the aggregate performance of movies. Furthermore, unlike most existing studies, we use data on attendance at the individual show level (179,103 shows) rather than aggregate box office sales.Objectives: In the paper, we evaluate short-term forecasting models of cinema attendance. The main purpose of the study is to find the factors that are useful in forecasting cinema attendance at the individual show level (i.e., the number of tickets sold for a particular movie, time and cinema).Methods/Approach: We apply several linear regression models, estimated for each recursive sample, to produce one-week ahead forecasts of the attendance. We then rank the models based on the out-of-sample fit.Results: The results show that the best performing models are those that include cinema- and region-specific variables, in addition to movie parameters (e.g., genre, age classification) or title popularity.Conclusions: Regression models using a wide set of variables (cinema- and region-specific variables, movie features, title popularity) may be successfully applied for predicting individual cinema shows attendance in Poland.

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Section: Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Forecasting Cinema Attendance at the Movie Show Level: Evidence from Poland

Baranowski

Korczak

Zając

2020

Business Systems Research Journal

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“…np. [Polasik i Maciejewski 2009, Fiszeder i Polasik 2009, Polasik 2015, Polasik, Wisniewski i Lightfoot 2012, Górka 2013, Goczek i Witkowski 2015, 2016). Z punktu widzenia banku centralnego interesującą kwestią jest określenie czynników motywujących do korzystania z kart płatniczych i identyfikacja tych barier utrudniających działalność przedsiębiorstw handlowych, które są zwią-zane z dodatkowymi opłatami interchange za transakcje dokonane przy użyciu kart.…”

Section: Wprowadzenieunclassified

Dwuwymiarowe zmienne licznikowe – bayesowskie modelowanie selekcji próby

Osiewalski

Marzec

2017

KREM

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Dwuwymiarowe zmienne licznikowe -bayesowskie modelowanie selekcji próby * Streszczenie W artykule przedstawiono propozycję łącznego modelu statystycznego dwóch zmiennych licznikowych, z których jedna może być zdegenerowana w zerze. Rozważane jest modelowanie oparte na przełączaniu między dwu-i jednowymiarowym modelem regresji poissonowskiej, przy czym przełączanie zależy od zaobserwowanej wartości trzeciej, dychotomicznej zmiennej. Zalecana jest analiza bayesowska; w dwóch szczególnych przypadkach proponowanego modelu bayesowskiego sformułowano konsekwencje ważne dla wnioskowania. W części empirycznej rozważane jest łączne modelowanie liczby płatności gotówką i kartą w Polsce, z wykorzystaniem danych zarówno dla posiadaczy kart, jak i osób ich nieposiadających.Słowa kluczowe: dwuwymiarowe modele regresji Poissona, przełączanie między rozkładem niezdegenerowanym i zdegenerowanym, faktoryzacja funkcji wiarygodności, płatności kartą płatniczą i gotówką. Klasyfikacja JEL: C25, C24, C51. Jacek Osiewalski, Uniwersytet Ekonomiczny w Krakowie, Wydział Zarządzania, Katedra Ekonometrii i Badań Operacyjnych, ul. Rakowicka 27, 31-510 Kraków, e-mail: eeosiewa@cyf-kr.edu.pl Jerzy Marzec, Uniwersytet Ekonomiczny w Krakowie, Wydział Zarządzania, Katedra Ekonometrii i Badań Operacyjnych, ul. Rakowicka 27, 31-510 Kraków, e-mail: marzecj@uek.krakow.pl * Artykuł stanowi wynik realizacji projektu sfinansowanego ze środków przyznanych Wydziałowi Zarządzania Uniwersytetu Ekonomicznego w Krakowie w ramach dotacji na utrzymanie potencjału badawczego.

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“…The dynamics of the number of transactions per type of payment service in Lithuanian payment market over the same period has been depicted in Figure 3. Summarising, it can be stated that the main determinants of payment card usage are availability, open-mindedness and reliance (Goczek, Witkowski 2016). Considering the findings of previous studies, which propose that prevalence of a particular type of payment is determined by customers' habits and culture in the finance markets, it can be stated that transfer from cash to non-cash payments can be accelerated by developing the measures that would enable to change entities' (in particular, households' and individual payers') attitudes towards the safety and convenience of noncash payments.…”

Section: The Comparative Analysis Of the Trends Of Payment Types In Tmentioning

confidence: 99%

The analysis of Lithuanian payment market: are we stuck to cash?

Remeikienė

Gasparėnienė

Grigaliune

2016

9th International Scientific Conference “Business and Management 2016”

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Abstract. Rapid technologic advancement alongside with increasing individual needs determine popularity of information technologies. As a result, a number of newly-developed instruments that enable to make payments in the methods different from cash are introduced to consumers in the payment market. The aim of this article is to establish how widely cash is used for making payments in Lithuanian payment market. The results of the research have revealed that the number of cash settlements in Lithuanian payment market is decreasing. Although the number of newly issued debit and credit cards is increasing, cash remains the prevalent method of payment, which corresponds to the trends of payment in the EU payment market.

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Determinants of card payments

Cited by 29 publications

References 12 publications

Forecasting Cinema Attendance at the Movie Show Level: Evidence from Poland

Forecasting Cinema Attendance at the Movie Show Level: Evidence from Poland

Dwuwymiarowe zmienne licznikowe – bayesowskie modelowanie selekcji próby

The analysis of Lithuanian payment market: are we stuck to cash?

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