Average Transaction Research Articles

The Influence of Public Facilities on the Transaction Price of Multi-Family Residential in Xitun District

The ratio of public area in Taiwan's multi-family residential is gradually increasing, and the items of public facilities are becoming more and more diversified. Most researches only classify them, however, it is rarely discussed how the connotation and diversity of public facilities affect residential transaction prices? This study intends to classify the public facilities in 22 multi-families residential near the Zhongke Shopping Plaza in Xitun District, Taichung City and give them diversity scores. The study uses hierarchical linear model for analysis, residential price as the dependent variable, diversity of public facilities scores as the independent variable of group-level, and public area, private area, age and floor as independent variables of individual-level. The empirical results found that floor, public area, and private area have positive and significant influence on residential transaction prices; when controlling the influence of individual-level explanatory variables on residential transaction prices, the diversity of public facilities at the group-level has a positive effect on the average residential transaction price of residential buildings.

기업의 평균 거래 거리가 운영성과에 미치는 영향 : 경기도 기업을 중심으로

기업의 평균 거래 거리가 운영성과에 미치는 영향 : 경기도 기업을 중심으로

Seeing Impacts of Park Design Strategies on Local Economy through Big Data: A Case Study of Gyeongui Line Forest Park in Seoul

Although big data has emerged as a crucial data source in urban studies, urban park-related studies still rarely use data such as that from card transactions. This study fills the gap between big data and park studies by using card transaction data within 400 m of the Gyeongui Line Forest Park in terms of economic benefits on local business. The authors divided the linear park into five sections according to each section’s design strategy to examine the relationship between the design features and card transaction behaviors. With the data, the authors analyzed the average ages of card users to understand average users’ age in each section. Results show the average ages increased from 2015 to 2017 in Sections 3–5 by years. Sections 1 and 2 describe decreasing of user ages by year, which means young generations visited Sections 1 and 2 For Section 1, amounts of average card transaction also increased from 2015 to 2017 continuously. Compared to other sections, only Section 1, as an open space within a commercialized area, contributed to local business positively. Other sections, such as 2–5, represented the negative impacts on local business from 2016 to 2017.

Efficient top-k high utility itemset mining on massive data

In practical applications, top-k high utility itemset mining (top-k HUIM) is an interesting operation to find the k itemsets with the highest utilities. It is analyzed that, the existing algorithms only can deal with the small and medium-sized data, and their performance degrades significantly on massive data. This paper presents a novel top-k HUIM algorithm PTM which can mine top-k high utility itemsets on massive data efficiently. PTM maintains the transaction database as a set of prefix-based partitions, each of which can fit in the memory and keeps the transactions with the same prefix item. The utility of an itemset can be computed by the transactions in one particular partition. PTM processes the prefix-based partitions in the order of average transaction utility to raise utility threshold faster. By the concise assistant data structures, PTM can skip majority of the partitions directly. For the partitions to be processed, PTM utilizes depth-first search on the set enumeration tree of the promising items to find the required results. The full-suffix-utility-based subtree pruning rule is devised to reduce the exploration space of set enumeration tree effectively. The extensive experimental results show that PTM can discover the top-k high utility itemsets on massive data efficiently.

Emergency Communication in IoT Scenarios by Means of a Transparent LoRaWAN Enhancement

This work deals with the management of sporadic and rare events linked with emergency situations in wireless Internet-of-Things (IoT) scenarios. The goal is to increase the performance of the emergency communication, when low-power wide area networks (LPWANs) are used as IoT backbone. In the proposed approach, a device usually operates as a normal node but, in case of emergency, can use the novel LoRa-REP access method. In this work, the LoRa-REP, based on message replication, is discussed focusing on its capability of reducing average transaction time and increasing success probability. Two operational paradigms have been considered and tested: public LoRaWAN infrastructure with cloud-based backend, and private LoRaWAN networks with local backend (edge/fog computing). Typical examples of public networks are smart cities, whereas local networks are often used in industry or building automation. Additionally, two real use cases (for public and local scenarios) are provided to show the effectiveness of the proposed approach. The experimental results (with a prototype device implementing LoRa-REP and named eNode) show that the success probability of the emergency communication can be increased up to 99.5%, and the average transaction time can be reduced up to 15% with respect to LoRaWAN without retries or up to 50% with respect to LoRaWAN with retries.

Empirical Analysis on Comparison between Self-checkout and Regular Staffed-checkout lanes in a Poland Retail Store

Customer satisfaction in retail stores are considerably affected by checkout services. Self-checkout counters have been installed in order to reduce waiting times at checkout in retail stores. However, it is uncertain whether the self-checkout lanes actually decrease the average waiting time of customers. Rather, there are some problems associated with self-checkout lanes such as theft or service failure due to technological problems. This study analyzes comparison between self-checkout and regular staffed-checkout lanes, based on the dataset collected from a retail store in Poland. As a result, we observe that the average transaction times were longer at the self-checkout lanes though fewer products were purchased than at the staffed-checkout lanes. In addition, the customers who buy more products tend to use self-checkouts less frequently. We also check that transaction times are proportional to the number of products customers purchase, and that both the time to scan one item and the fixed time related to checkout are significantly longer at the self-checkout counters. As there has been very few research on the effectiveness of self-checkouts, this study can be the first step to investigate managerial insights on checkout services in retail stores.

Input and Output Driven Sales Personnel Performance Measures: Insights from an Experiment

It is a globally accepted strategy that, retaining existing consumers is significantly cost effective than focussing on acquiring new consumers. In brick-and-mortar retailing, sales personnel play the most important and complex role whereby they are the ones who are connected to consumers directly on a real-time basis. It is observed that the majority of brick-and-mortar retailers in India use measures to measure sales personnel performance which is mathematically derived numbers viz. average transaction value (ATV), average basket size (ABS) and contribution margin percentage (GM%) and these are all output driven measures. Such output driven measures are making the sales personnel’s role furthermore complex and in turn, their focus is expected to have shifted from consumer needs to these output driven measures set by their management. In this research, we have carried out an experiment using input driven measures viz. category invoice penetration (CIP) and invoices/bills generated by the sales personnel per day and evaluated the change in: (a) sales personal attitude towards consumer orientation and (b) overall store profitability.

Community-Based Recommendations to Improve Customer Turnout at a Non-Profit Grocery Store

Abstract Objectives Aimed at improving healthy food access in a low-income neighborhood, the Salvation Army's first non-profit grocery store in Baltimore City has so far failed to attract a large customer base. The store averages just 140 customers daily and serves 500 unique families weekly, with an average transaction of $14. This study explored reasons for low usage from the perspective of community members and suggests solutions. Methods Mixed-methods formative research was conducted at DMG Foods. In-depth interviews were conducted with store staff (n = 5), wholesalers (n = 3), local store non-users (n = 4), and store users (n = 9).Interviews focused on purchasing habits, observations, prices, marketing efforts, quality of food, location, and convenience. Direct observations of store operations (n = 8) and a short demographic survey of store users with loyalty cards (n = 119) and without loyalty cards (n = 33) was also completed. All in-depth interviews were coded and analyzed using ATLAS.ti v8.4.18. Results The survey results indicated that the average amount spent at DMG Foods per loyalty customer within the last 30 days was $82. Only 11% survey respondents reported using DMG for all their grocery shopping. Reasons given for low store usage, included: (a) prices at DMG Foods are not low; (b) Non-users do not perceive DMG Foods as a food store; and (c) food quality concerns. In light of these responses, we made several recommendations, including: (a) make displays showing price differences between DMG Foods and other competitors; (b) place rebranded signs outside the store to indicate that DMG Foods is a grocery store; and (c) improve and maintain the quality of produce and meat in store. Additionally, store management should negotiate with wholesalers and distributors to obtain competitive wholesale prices. Conclusions The results of this study will be used to inform DMG Foods and to help Salvation Army refine their non-profit grocery model as they expand it to other locations in Baltimore and across the United States. Funding Sources Johns Hopkins University Bloomberg School of Public Health Department of International Health.

Optimal Category Mix in Multi-Category Retailing – Insights from an Experiment

In brick-and-mortar retailing format, retailers need to ensure minimum level of inventorydisplayed at each store for each category irrespective of the revenue or profit generated by aparticular category. It is observed that majority of bricks-and-mortar retailers in India assume;(a) existing category mix is ideal for their stores, (b) any modification in the existing categorymix could possibly lead to loss of sale of an existing category, (c) it is preferred to havecategories generating higher average transaction values and most importantly,(d) categorieswith lower average selling price products and generating lower average transaction valuesnegatively impact store’s revenue. Such assumptions and widely followed practice havecreated a predisposition and mindset in store managers and they believe that, their storedelivers revenue and profit to the best of its potential with the existing category mix. In thisresearch, we have analysed the existing category mix of a select retailer, attempted to alter theexisting category mix through an experiment and evaluated change in (a) category levelprofitability, and (b) overall store profitability.

Optimal Category Mix in Multi-Category Retailing – Insights from an Experiment

In brick-and-mortar retailing format, retailers need to ensure minimum level of inventory displayed at each store for each category irrespective of the revenue or profit generated by a particular category. It is observed that majority of bricks-and-mortar retailers in India assume; (a) existing category mix is ideal for their stores, (b) any modification in the existing category mix could possibly lead to loss of sale of an existing category, (c) it is preferred to have categories generating higher average transaction values and most importantly, (d) categories with lower average selling price products and generating lower average transaction values negatively impact store’s revenue. Such assumptions and widely followed practice have created a predisposition and mindset in store managers and they believe that, their store delivers revenue and profit to the best of its potential with the existing category mix. In this research, we have analysed the existing category mix of a select retailer, attempted to alter the existing category mix through an experiment and evaluated change in: (a) category level profitability, and (b) overall store profitability.

Seeing sweet and choosing sour: Compensatory effects of typeface on consumers’ choice behavior

The present research included two field studies investigating, firstly, the effects of round and angular typefaces on taste expectations and, secondly, how such expectations may have downstream effects on naturalistic consumer choice. A taste experiment conducted at a science festival asked participants (N = 125) to rate the expected and actual sweetness and sourness of beer served from plastic cups labelled with either angular or round typeface. Effects of typeface were found on both taste expectations and perceived taste, but in opposite directions; participants rated the expected sourness of a beer as higher in the round (vs. angular) typeface condition, whereas they rated perceived sourness as lower in the round compared to the angular typeface condition. A follow-up field study conducted at a beer bar tested whether different typefaces on a beer board would affect actual beer choices. Data consisted of beer transactions (N = 1,952) and included the monetary amount, and the specific style, and size of beer purchased. The results showed that average transaction amount was lower in the round (vs. angular) typeface condition, and that customers purchased more sour beer options in this condition. These findings support a crossmodal compensation account, and suggest that round typefaces could increase choices of contrasting sour products. Hence, marketers may want to consider how typefaces can either enhance or diminish certain tastes.

Input and Output Driven Sales Personnel Performance Measures: Insights from an Experiment

It is a globally accepted strategy that, retaining existing consumers is significantly cost effective than focussing on acquiring new consumers. In brick-and-mortar retailing, sales personnel play the most important and complex role whereby they are the ones who are connected to consumers directly on a real-time basis. It is observed that the majority of brick-and-mortar retailers in India use measures to measure sales personnel performance which is mathematically derived numbers viz. average transaction value (ATV), average basket size (ABS) and contribution margin percentage (GM%) and these are all output driven measures. Such output driven measures are making the sales personnel’s role further more complex and in turn, their focus is expected to have shifted from consumer needs to these output driven measures set by their management. In this research, we have carried out an experiment using input driven measures viz. category invoice penetration(CIP) and invoices/bills generated by the sales personnel per day and evaluated the change in(a) sales personal attitude towards consumer orientation and (b) overall store profitability.

Input and Output Driven Sales Personnel Performance Measures: Insights from an Experiment

It is a globally accepted strategy that, retaining existing consumers is significantly costeffective than focussing on acquiring new consumers. In brick-and-mortar retailing, salespersonnel play the most important and complex role whereby they are the ones who areconnected to consumers directly on a real-time basis. It is observed that the majority ofbrick-and-mortar retailers in India use measures to measure sales personnel performancewhich is mathematically derived numbers viz. average transaction value (ATV), averagebasket size (ABS) and contribution margin percentage (GM%) and these are all outputdriven measures. Such output driven measures are making the sales personnel’s rolefurthermore complex and in turn, their focus is expected to have shifted from consumerneeds to these output driven measures set by their management. In this research, we havecarried out an experiment using input driven measures viz. category invoice penetration(CIP) and invoices/bills generated by the sales personnel per day and evaluated the change in(a) sales personal attitude towards consumer orientation and (b) overall store profitability.

Blockchain-based event processing in supply chains—A case study at IKEA

Abstract One of the major areas of interest for deployment of blockchains is in the supply chain sector where decentralisation combined with immutability is expected to enhance the transparency and robustness of transaction processing drastically. In this work we contribute to a feasibility study for permissioned blockchains in the context of a major international retail company. First, we characterise the types, relationships, and volumes of events that are created in the life cycle of a product from creation to ownership transfer. We use the created event model and the data from the company operations to identify the load that such an event processing system would have to manage. Then we create a prototype based on Quorum that deals with a significant subset of the events and expose the blockchain-based prototype to variations in load to identify the maximum throughput and average transaction processing time. This system can then form the basis of understanding the bottlenecks, configuration settings and platform choices needed in future potential deployment.

The Functioning of the Real Estate Market: Dynamics of Price Formation and the Sale of Apartments

Purpose: The aim of this publication is to analyse how the dynamics of apartments’ price formation correlates with the development of the apartment market. Design/Methodology/Approach: We analysed data about real estate markets of Polish communes located in Central Pomerania. The values of the analysed indicators were calculated using Excel program and the results were presented in the form of Tables and Figures in Microsoft Excel and ArcMap. Findings: This publication focuses on issues related to price formation on the housing market with the objective to illustrate a correlation between the dynamics of price formation of apartments and the development of the apartment market. This study concentrates on the pricing behaviour of transactions in the primary and secondary real estate markets of apartments. Research results indicate that there is a relationship between the dynamics of price formation and the development of the apartment market in both the primary and secondary markets. The hypotheses have been positively verified. The analysis of the obtained data does not indicate the same tendencies in the transaction prices of the apartment properties in the researched seaside communes in the years 2014-2018. Practical Implications: The most important practical implication is that the analyzed seaside areas still have a large investment space for newer, more expensive and larger apartments. Originality/Value: Research analysis of the two markets shows that the primary market is characterised by a much greater volatility of the average transaction price per sqm of apartment’s usable floor area, while the secondary market is much more stable in terms of the average transaction price. There is also a very large variation in the average price per sqm between the surveyed municipalities.

A fast stream transaction system for real-time IoT applications

Due to the recent prevalence of IoT (Internet of Things) technologies, various IoT devices connect to the Internet and continuously send their generated data to remote processing computers such as video data or sensor data. The transaction rate is one of the main factors to improve the performance of some IoT applications. For instance, in surveillance systems, the probability to catch a thief increases as the processing computer analyzes the video with a higher transaction rate. To improve the transaction rate, some methods reduce the transaction time between a processing computer and stream data sources under a static transaction interval. However, the transaction rate can be further improved by changing the transaction interval dynamically depending on the transaction time. In this paper, we propose a method to improve the transaction rate by changing the transaction interval dynamically. In our proposed method, a processing computer sometimes changes the transaction interval to be the same length as the average transaction time. Moreover, our proposed method adopts a progressive quality improvement (PQI) approach to reduce the transaction time. We measured the transaction rate of our proposed method by both a simulator and an implemented system. We confirmed that our proposed method can improve the transaction rate by 4.4 times and the transaction time by 21% at least compared with the conventional method. Moreover, we confirmed that the average frame rate increases 22% compared with a simple method in a real situation.

Neighborhood Walkability and Housing Prices: A Correlation Study

This study aims to examine the relationship between the level of walkability and housing prices in Seoul, Korea. The average transaction price per square meter for each apartment complex was set as a dependent variable and the walkability score was used as an independent variable. This study divided a total of 5986 apartment complexes into areas with high and low housing prices for analysis. Based on the strong spatial autocorrelations of housing prices, this study employed spatial regression models in addition to the Ordinary Least Squares (OLS) model. Results showed that housing prices positively correlated with the walkability score in areas with low housing prices, whereas no significant association was observed in areas with high housing prices. Additional findings showed that housing prices were associated with building age (−), number of households in the complex (+), slope (−), and greenness (+) in both subsamples. Results also showed that high school quality had a different association with housing prices depending on the subsample (e.g., the sign was positive in areas with high housing prices and no significance in areas with low housing prices). The results herein support public policy proposals relevant to urban planning, environmental design, and housing policies.

Usage of Deep Learning and Blockchain in Compilation and Copyright Protection of Digital Music

In order to explore the application of deep learning algorithms in arrangement and composition, and the role of blockchain in the protection of digital music copyright, a monophonic melody composition model based on the deep generative adversarial networks (DCGANs) is constructed firstly, and the composition performance of the model is analyzed using hymn as input sample in this study. Later, the multi-instrument co-arrangement (MICA) model based on the multi-task learning is proposed, and the composition performance is analyzed by taking the actual music as an input sample. Finally, the improved practical byzantine fault tolerance (IPBFT) algorithm is proposed, and a digital music copyright protection system is designed based on the blockchain in this study. The results indicate that the accuracies constructed DCGANs model in predicting the Soprano and Alto voice melody are higher than those of the DeepBatch model by 2.29% and 3.32%, respectively. The performance on the harmony score, note accuracy, Levenshtein similarity (LS), notes distribution mean square error, and empty as well as the convergence speed of the constructed MICA model are better than those of other models. The average transaction per second (TPS) value of the proposed IPBFT algorithm in the real digital music copyright protection system is 3469, which is superior to other blockchain technologies. Finally, the digital music copyright protection system is achieved, the error rate of completing the request is 0% in the state of many users operating concurrently, and a high TPS value can be guaranteed. In short, the DCGANs and MICA models pointed out in this study can be used in the composition of monophonic melodies and complex melodies, and the digital music copyright protection system based on the blockchain has excellent performance in practical applications.

Impact of Macroeconomic Condition on Credit Card Default in Emerging Economy: Empirical Evidence from Indonesia

Indonesia is a developing country with the fourth largest population in the world. Household consumption is still the main pillar of national economic growth in Indonesia. One sector that has an important role in national economic growth is banking. Bank carries out an intermediary function that directly or indirectly can encourage the real sector. A credit card is one of the banking products that can encourage growth in household consumption to support the growth of the real sector. However, on the other hand, the credit card is an unsecured consumer loan. This indicates the bank will have a greater percentage of losses than other types of credit if the borrower default. Therefore, the growth of credit card business must be balanced with good credit quality for the safety and soundness of the banking sector. Credit quality can be measured using a Non-Performing Loan (NPL) that reflects credit default risk. This study aimed to analyze the impact of the macroeconomic condition on credit card default which is proxied by credit card NPL ratio. NPL data obtained from Indonesia's biggest private bank with cardholders that are widespread on every island and have average card growth, transaction value, and outstanding credit card were above the national average. ARDL Cointegration model is used to determine macroeconomic variables that significantly affect credit card NPL. This study was found that exchange rate and interest rate variables partially have a significant influence on the credit card NPL in the long-term. ARDL model can be used as an early warning indicator of the condition of Bank credit card NPL if there is a shock to macroeconomic variables and the model can be used to improve the feasibility analysis tool for new cardholders (credit scoring system) and an indicator of behavior scoring system for existing cardholders.

Study of Merchant Adoption in Mobile Payment System Based on Ensemble Learning

To explore the influencing factors of the adoption of mobile payment systems from the perspective of merchants, this study builds a data analysis model based on three different ensemble learning algorithms, Adaboost model, random forest and XGBoost model, where static social-economic attributes, dynamic trading behavior and clustering effect variables of merchants are used as independent variables. Moreover, this paper establishes the prediction models and analyzes the prediction accuracy of different models. The results of the study indicate that: 1) Merchants in the housing industry, health hospitals and retail industries are more willing to adopt mobile payment systems; 2) The average daily transaction volume and the average amount of each consumer significantly affect the merchant mobile payment adoption behavior; 3) The adoption of mobile payment systems by neighboring merchants significantly positively affected the adoption behavior of merchants; 4) On the basis of the social-economic attributes of merchants, the hit rate and accuracy of the prediction model were greatly improved after adding transaction data.