Payment Behavior Research Articles

Beyond the Freebie Mentality: A News User Typology of Reasonings About Paying for Online Content

ABSTRACT Online news providers struggle to pull audiences behind their paywalls in order to complement (insufficient) advertising revenue with sales income. But what do consumers think about paying for—once freely available—online news content and how does it affect their payment behavior? The present study complements the existing research by developing a typology of attitudes and behavior toward paying for online news content based on qualitative interviews (n = 64) conducted in Germany. Five types of mindsets were identified: paying subscribers, free riders, promisers (users who do not pay but announce to do so in the future), occasional buyers, and convinced deniers. The typology organizes the diversity in positions toward paid news content, thus enabling a more powerful explanatory research on consumer valuation and monetization in journalism. In addition, it can assist online news providers in developing targeted strategies for converting online readers into purchasing customers.

Efficient forecasting and uncertainty quantification for large-scale account level Monte Carlo models of debt recovery

AbstractThe state-of-the-art in forecasting debt recovery from portfolios of non-performing unsecured consumer loans is to use stochastic models of payment behaviour of individual customers. Monte Carlo simulation of these models can enable forecasting of collections, where computational complexity arises from the very large number of heterogeneous accounts. We aim to solve 2 problems: efficient allocation of computational resources and quantification of uncertainty. We show that robust estimators of population-level variance can be constructed using unbiased estimators of the variance of individual accounts. The proposed methods are demonstrated through application to a model similar to those used in practice.

The applicability of machine learning algorithms in accounts receivables management

PurposeThe accurate prediction of incoming cash flows enables more effective cash management and allows firms to shape firms' planning based on forward-looking information. Although most firms are aware of the benefits of these forecasts, many still have difficulties identifying and implementing an appropriate prediction model. With the rise of machine learning algorithms, numerous new forecasting techniques have emerged. These new forecasting techniques are theoretically applicable for predicting customer payment behavior but have not yet been adequately investigated. This study aims to close this research gap by examining which machine learning algorithm is the most appropriate for predicting customer payment dates.Design/methodology/approachBy using various machine learning algorithms, the authors evaluate whether customer payment behavior patterns can be identified and predicted. The study is based on real-world transaction data from a DAX-40 firm with over 1,000,000 invoices in the dataset, with the data covering the period 2017–2019.FindingsThe authors' results show that neural networks in particular are suitable for predicting customers' payment dates. Furthermore, the authors demonstrate that contextual and logical prediction models can provide more accurate forecasts than conventional baseline models, such as linear and multivariate regression.Research limitations/implicationsFuture cash flow forecasting studies should incorporate naïve prediction models, as the authors demonstrate that these models can compete with conventional baseline models used in existing machine learning research. However, the authors expect that with more in-depth information about the customer (creditworthiness, accounting structure) the results can be even further improved.Practical implicationsThe knowledge of customers' future payment dates enables firms to change their perspective and move from reactive to proactive cash management. This shift leads to a more targeted dunning process.Originality/valueTo the best of the authors' knowledge, no study has yet been conducted that interprets the prediction of incoming payments as a daily rolling forecast by comparing naïve forecasts with forecasts based on machine learning and deep learning models.

Impact of AI in Consumer Finance – A Study with Special Reference to Chennai City

This study looks at how the introduction of innovative technologies, often marketed as "artificial intelligence", in the consumer finance sector can impact the customer service experience. The goal of this report is to explain how financial institutions use chatbot technology and the related challenges faced by their clients. Working with customers to solve problems or answer questions is a core function of financial institutions – and the foundation of relationship banking. Customers look to their financial institutions for help with financial products and services and expect quick and easy answers, regardless of the process or technology used. For decades, banks have used customer data such as income, credit scores and spending habits to promote, cross-sell and upsell their products to increase revenue. But today's technologies allow financial institutions to access more data and increase revenue in new ways. For example, a few years ago banks tracked consumers' credit behaviour through traditional databases and their monthly credit scores. But something has failed – the terabyte trail of behavioural and interest data it leaves behind through digital journeys and payment behaviour across products and services outside of relationships with customers. financial institutions. By using artificial intelligence and machine learning for faster, more granular analysis, financial institutions can proactively address the underlying needs of their customers by extracting compelling insights. from their digital footprint and payment behaviour. Many senior industry executives believe that AI is creating and will continue to generate the greatest business benefits by leveraging data and analytics and making better predictions.

Euro area consumers' payment behaviour and banking digitalisation

Euro area consumers' payment behaviour and banking digitalisation

Literature Review of Reward-based Content Payment

This article mainly reviews the literature of CSSCI publications in the marketing field and summarizes customer payment behavior in the context of Reward-based Content Payment, exploring the application of traditional marketing theories in the Content Payment context. Firstly, the connotation of Reward-based Content Payment in existing literature is sorted out, and the classification of Reward-based Content Payment is explored in depth. Secondly, the influencing factors of Reward-based Content Payment are summarized and sorted out. This article provides direction for future research, aiming to promote further development of the Reward-based Content Payment industry.

The Pandemic, Cash and Retail Payment Behaviour: Insights from the Future of Payments Database

The Pandemic, Cash and Retail Payment Behaviour: Insights from the Future of Payments Database

Study of Thai Youths in the Northeastern Region of Thailand on the Effectiveness of Digital Payment Behavior

Digital payment or e-payment is well known in the present digital age and cashless society because of its variety and benefit. With an increasing amount of digital payment technology, the unified theory of acceptance and use of technology (UTAUT) has been utilized for decades to investigate consumers’ acceptance. However, youths’ behavioral intention toward digital payment usage in northeastern Thailand is unknown. We thus collected the data from 384 participants of Thai youths in the northeastern region. We discovered that performance expectancy, effort expectancy, and social influence expectancy positively influence youth’s behavioral intention. In addition, facilitating conditions and behavioral intention have a positive effect on youth’s use behavior. Furthermore, we found behavioral intention to be the strongest determinant in use behavior and that behavioral intention is the most influenced by performance expectancy. This result implies that more functional digital payment solutions lead to a robust intention to use by consumers. We anticipate that digital payment service providers will develop functional services and a user-friendly interface for the market. In a similar vein, we also expect businesses and entrepreneurs, as well as virtual or physical operations, to adopt digital payments and offer them to both existing and potential consumers.

Credit Card Defaulter Prediction

Our aim is to develop a Machine learning model and testing the model by using the data in relating to previous 6 months payment behaviour which is behavioural data and personal information which is demographic data as input of a client is used for this study. The research study is conducted using Random Forest Algorithm. Our aim is to identify that credit card customer is likely to default in the coming month.

A privacy protection scheme based on linkable ring signature for user payment of Peer-to-Peer uniform-price double auction transaction in the microgrid day-ahead market

In the microgrid day-ahead market Peer-to-Peer (P2P) transaction, the uniform-price double auction mechanism has advantages in the microgrid day-ahead market because it can prevent power price fluctuations during the one hour trading period, and give priority to trading Distributed Energy Resources(DERs) based on trading volume, which is conducive to the consumption of new energy. In this kind of transaction, aiming at the problem that the attacker can obtain the privacy information such as the collection and payment address and transaction amount of the producer and consumer through the account book information on the blockchain, so that the relationship between the producer and consumer and the payment behavior is subject to inference attacks, and thus the identity of the participants is inferred, a privacy protection scheme based on linkable ring signature for user payment of P2P uniform-price double auction transaction in the microgrid day-ahead market is proposed. This scheme has the following novelty and contribution: Firstly, the linkable ring signature is used to sign the one-time account address and payment information of the producer and consumer, which ensures the untraceability of transaction payment in the case of anonymity of the producer and consumer, and it has low computational cost; Secondly, in order to hide its real output address, destroy the association between the input address and the real output address, and provides the unlinkability of transactions, the one-time address is proposed; Finally, a one-time address generation method based on elliptic curve is designed, and point multiplication is used to improve the efficiency of address generation. Theoretical analysis proves that the scheme is untraceable and unlinkable while ensuring privacy. Experiments show that this scheme has lower computational cost.

Revisiting SME default predictors: The Omega Score

ABSTRACT SME default prediction is a long-standing issue in the finance and management literature. Proper estimates of the SME risk of failure can support policymakers in implementing restructuring policies, rating agencies and credit analytics firms in assessing creditworthiness, public and private investors in allocating funds, entrepreneurs in accessing funds, and managers in developing effective strategies. Drawing on the extant management literature, we argue that introducing management- and employee-related variables into SME prediction models can improve their predictive power. To test our hypotheses, we use a unique sample of SMEs and propose a novel and more accurate predictor of SME default, the Omega Score, developed by the Least Absolute Shortage and Shrinkage Operator (LASSO). Results were further confirmed through other machine-learning techniques. Beyond traditional financial ratios and payment behavior variables, our findings show that the incorporation of change in management, employee turnover, and mean employee tenure significantly improve the model’s predictive accuracy.

IDENTIFYING CONSUMER RESISTANCE OF MOBILE PAYMENT DURING COVID-19: AN INTERPRETIVE STRUCTURAL MODELING (ISM) APPROACH

Purpose – Due to country-wise lockdown and state-wise curfews in COVID-19, people were not able to make offline payments (i.e. cash payments) during purchases in India. So, people are switching their payment behavior from offline to online mode. But, as per the central bank report, the rate of adoption through mobile payments is still slow. The paper focuses on identifying critical barriers to mobile payment systems (MPSs) adoption in India. Innovation resistance theory (IRT) has been used as a base model for barriers, despite the wide range of choices of barriers available in the MPSs context. Additionally, three external variables which are out of the wider coverage of IRT constructs were incorporated in this paper. The study, on the other hand, adds to innovation resistance theory in the frame of reference of MPSs from a theoretical perspective. Interpretive structural modeling (ISM), together with MICMAC analysis is brought into play to analyse the direct and indirect relationship amongst the barriers. Research methodology – ISM approach has been used to establish the relationship among the eight (08) identified barriers, through literature and expert opinions. The key barriers to high driving power are then identified with the help of MICMAC analysis. Findings – The results reveal that value barrier (b2), image barrier (b5) and visibility barrier (b7) are the most significant variables. Interestingly, IRTs’ risk barrier (b3) and privacy barrier (b6) from the literature fall in the lowest level of the ISM model. The majority of the barriers fall under quadrant III of MICMAC analysis, indicating the high driving and dependence power. Research limitations – The developed ISM model is based on the sentiments of five (05) experts, which could be biased and influence the structural model’s final output. Due to COVID-19, data has been collected through online video conferencing mode, this may vary if data will be collected through an offline or face-to-face interview. The proposed model’s key findings aim to assist in explaining the barriers that exist during MPS adoption. Originality/Value – This study is the first attempt to use the ISM approach in conjunction with IRT to detect barriers within MPSs. The result of this paper will guide and motivate the researcher to analyse more critical barriers with IRT to contribute to the theoretical development.

Factors Influence Switching Intention to M-payment Using Push-Pull-Mooring Framework During the Coronavirus Pandemic in Malaysia

Cash and debit/credit card payments have been used for many years and have become customary for everyday payment transactions. However, the coronavirus pandemic has changed consumers’ buying behaviour as well as their payment behaviour. Besides, the governments, shops and restaurants are encouraging consumers to use cashless payment systems or m-payment to keep social distance and stop the spread of the virus. Owing to this, this study aimed to examine the factors influencing the Malaysians’ switching intention to mpayment during the coronavirus pandemic. Using the Push-Pull-Mooring framework, this study examined the effect of perceived lack of security, the relative advantage of substitution, ease of use, habit and self-efficacy as the factors influencing Malaysian consumers’ intention to switch to m-payment. The data were collected using the convenience and snowballing sampling technique. Respondents were invited through social network sites and email. Through the online survey, 226 usable data were successfully collected for analysis using the structural equation modelling (SEM) technique. The results show that perceived lack of security, ease of use, habit and self-efficacy have a significant impact on Malaysian m-payment switching intention. Notably, the relative advantage of substitution was found no significant impact attract them switching to m-payment. The results of this study contribute valuable insight to practitioners on the motivation and disincentives for Malaysians to switch intention to mpayment, as well as the impact of individual factors. From the perspective of theoretical implication, this study fills in the literature gap and enhances the understanding of m-payment switching intention in the context of Malaysia.

Information Adoption Patterns and Online Knowledge Payment Behavior: The Moderating Role of Product Type

The development of online knowledge payment platforms in recent years has increased their respective market value by nurturing content resources and improving content ecology. Yet, the underlying factors of knowledge seekers’ payment behaviors and their information adoption mechanisms are poorly understood. Based on the information adoption model, this study develops a research model to examine the relationship between information adoption patterns and knowledge seekers’ payment behavior, and explore the moderating effect of product type on this relationship. To test the research model and hypotheses, we used a multi-analytic approach combining text and regression analysis on a sample of 4366 social Q&A data collected from Quora+ between August 2021 and August 2022. We further classified the product types into utilitarian and hedonic, and compared the differences in influence paths between product types. The results show that the completeness, vividness, and relevance of the product description have a significant positive impact on knowledge payment behavior. The reputation, experience, and integrity of the knowledge provider have a positive impact on knowledge payment behavior. Compared to utilitarian knowledge products, the payment behavior for hedonic products is more related to the reputation and experience of the knowledge provider. This study provides insights into the factors that influence online knowledge payment behavior and practical guidance for the development of online knowledge payment services and platforms.

Consumers’ perceived value and use intention of cashless payment in the physical distancing context: evidence from an Asian emerging market

PurposeCOVID-19 and its measures such as physical distancing have shifted consumer payment behaviors toward cashless payment. Physical distancing is likely to remain a norm for some time to come and will be relevant in any future pandemics. This study aims to examine the impact of consumers’ perceived value of cashless payment on their use intention in the physical distancing context, with the mediating role of psychological safety and the moderating role of trust propensity.Design/methodology/approachThis study used a survey method to obtain data from 690 consumers in an Asian emerging market, i.e. Vietnam. The data were analyzed using different statistical methods, including structural equation modeling.FindingsResults show that perceived value of cashless payment positively affects use intention, and this effect is mediated by psychological safety. Furthermore, trust propensity has a positive moderating effect on the link between perceived value and psychological safety.Practical implicationsThis study’s findings provide implications for retailers and other stakeholders in implementing and promoting cashless payment systems, especially in the physical distancing context.Originality/valueThis study is among the first attempt to explain the relationships between consumers’ perceptions, feelings of psychological safety and use intention toward cashless payment in the physical distancing context. The study’s findings may also be relevant to any future pandemics.

ANALISIS KLASIFIKASI MENGGUNAKAN METODE REGRESI LOGISTIK BINER DAN BOOTSTRAP AGGREGATING CLASSIFICATION AND REGRESSION TREES (BAGGING CART) (Studi Kasus: Nasabah Koperasi Simpan Pinjam Dan Pembiayaan Syariah (KSPPS))

The Save Loan and Sharia Financing Cooperatives (KSPPS) is a financial institution that offers deposits, loans, and financing to its members while adhering to Islamic sharia rules. Customers payment behaviour is influenced by their background differences, such as age, gender, occupation, and so on. The classification method is used to determine the characteristics of members who are currently in arears or are stuck in arears. Binary Logistic Regression and Bootstrap Aggregating Classification and Regression Trees were utilized as classification methods (BAGGING CART). A Logistic Regression with binary response variables is known as a Binary Logistic Regression. By resampling 50 times, the technique with the BAGGING process is used to improve the performance of the classification using CART. Customer data from one of the KSPPS in Central Java in 2021 was used in this investigation. Gender, age, marital status, employment, education level, time period, and income were the independent variables in this study, whereas payment status was the dependent variable (not stuck and stuck). The Binary Logistic Regression approach had an accuracy of 78.67 percent with an APER 21.33 percent, a Press's Q of 24.65, and a specificity of 98.30 percent, according to the classification accuracy statistics. The accuracy of the classification produced by CART with an accuracy value of 77.33 percent with an APER 22.67 percent, the value of Press's Q is 22,413, and specificity is 94.91 percent, then approached by BAGGING process the accuracy of the resulting classification by predicting data testing accuracy value of 78.67 percent with an APER 21.33 percent, press's Q value of 24.65, and specificity of 96.61 percent. Based on these findings, it can be inferred that using the BAGGING process can increase the CART method's performance to the point where it is nearly as good as Binary Logistic Regression, which has a slightly higher classification accuracy

On-Street Parking Occupancy Inference Based on Payment Transactions

A record of spatial and temporal parking occupancy is critical to optimize on-street parking resources and to develop effective parking policies. Such data are often obtained through advanced and costly occupancy monitoring technologies. Moreover, it is usually challenging to integrate bay-level occupancies and parking data from other systems. Accurate occupancy–payment data are required for a wide range of analytical and practical purposes, including but not limited to investigating payment behavior, estimating and forecasting occupancy, and evaluating the efficiency and effectiveness of enforcement policies. This study proposes a metaheuristic optimization algorithm to integrate snapshots of bay-level parking occupancy, captured using simple cameras, with transactions from a conventional parking payment management system. The resulting integrated data were used to develop, calibrate and validate a parking occupancy estimation method utilizing parking payment data only. Details of the design, implementation, and validation of the proposed algorithm and modelling technique are provided. Logistic regression analysis was used to tune parameters of the data integration algorithm. Deep learning, gradient boosting and random forests were used to develop a model of parking occupancy. Evaluation of the algorithm indicated an accuracy of 76% of correct data integration; that is, individual bay occupancies integrated with the correct corresponding payment transactions. The best occupancy estimation model also showed a very high accuracy, with an R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> above 94% and a root mean square error (RMSE) of 1.2 (occupied bays), when tested with a random sample from the integrated data.

Knowledge graph of mobile payment platforms based on deep learning: Risk analysis and policy implications

The Fintech mobile payment platform is expanding rapidly; this expansion, in turn, creates numerous risks. There is an urgent need to better understand these risks and to spur more secure payment behavior. This research aims to develop knowledge graphs of the mobile payment platform based on deep learning for risk analysis and policy inferences. We identify entities from collected policy documents, extract the relationships among the entities, and draw a risk knowledge graph on mobile payments. The use of unsupervised semi-automatic knowledge acquisition, we argue, can reduce the risk of mobile payment caused by a lack of knowledge. A significant benefit of this method is that risk knowledge can be acquired without supervision. Unlike other models, the absence of manual labeling allows for the relation extraction of triples to be unsupervised, while the previous triplet extraction was supervised. Compared with other unsupervised models, the precision of our model is improved, and the recall is the same as that of previous unsupervised shutdown extraction.Unsupervised relationship extraction can extract text relationships quickly and on a large scale, saving human resources for labeling.This method offers a potential solution to a fundamental problem; the content and quantity of policy documents exceed organizations’ and individuals’ ability to understand them. Our approach suggests the viability of developing a national policy risk knowledge graph to help mobile payment platforms understand national policies and reduce platforms’ operational risks while allowing users to quickly learn the risks of mobile payments and minimize the impact of those risks.

Consumers’ Switching from Cash to Mobile Payment under the Fear of COVID-19 in Taiwan

The COVID-19 pandemic impacts our lives significantly; people have changed their daily lives in response to the unprecedented epidemic which not only awakened the arrival of a new normal in business, but also new lifestyles. For example, the adaptation of contactless mobile payment has grown in the past two years to avoid unnecessary contact and possible infections. In this study, we intend to examine behavioral intentions that made consumers shift from traditional cash payment to mobile payment during the COVID-19 period. Our research framework and hypotheses were developed and examined through the push (dissatisfaction)–pull (alternative attractiveness)–mooring (perceived fear) model. We used structural equation modeling (SEM) to validate our model and corresponding hypotheses. The results of this study showed that dissatisfaction with tradition-al payments and customer’s perceived fears positively and significantly affected switching intentions. However, alternative attractiveness had no significant impact on consumers’ switching intentions from cash to mobile payments during the pandemic. Moreover, this study shows how perceived fear has a mediating effect that motivates people to change their payment behaviors. Implications and future research directions about consumers changing in such a dynamic time are also discussed.

International Technology Transfer and Economic Development

This paper intends to critically examine the concept of International Technology Transfer, considering Japan whilst they provide necessary tech advancements ideas for innovation to another transferee country i.e., India. This country realized the high significance of the technology transformation for its fast economic and commercial growth. We will also be analyzing Japan’s position as a tech superpower and an economic superpower. To analyze the economic development, a comparative study will be made with India. Here, Japan is the transferor, and India is selected due to its high intentions for development, and hence it needs support from developed nations with better technology. In this study, we take the balance of payment as the dependent variable and GDP, inflation rate, Exchange rate, and FDI as independent variables. Data is analyzed and results are obtained using regression which helps us analyze quantitatively. The data from the World Bank database about India and Japan has been obtained for the periods 2000-2019. This study helps us to interpret and find the key impacting factors of the balance of payment behavior in these nations. The empirical results show that India's GDP, as well as the inflation difference between Japan and India, are found to be very critical. The coefficient of India's GDP and inflation difference was positive while India's exchange rate and India's FDI were found to be negative. As the impact of India's exchange rate and India's FDI was negative, it is proof that an increase in India's exchange rate and India's FDI results in a decrease in the balance of payment, thereby the trade is ultimately affected. These results contain useful information that can be used by policymakers.