Customer Flow Research Articles

An Application of the Node–Place Model to Explore the Land Use–Transport Development Dynamics of the I-287 Corridor

A sustainable development plan should identify future urban development sites to maintain a balanced condition between transportation systems and land use. Most commonly used for Transit Oriented Development (TOD), the node–place model checks the balance between transportation systems and land use. While previous node–place research focused mostly on rail transportation, this research focuses on highway accessibility to assess future growth and urban development. To gain insight into the development dynamics, the node–place model is utilized with a focus on the I-287 Corridor located in New York, U.S. The node function describes the transport activity and connectedness of the area to other places of interest, which measures the accessibility of the locations, the type of connections, and the number of directions connected. In addition, population, number of workers in the labor force, and degree of a functional mix are also considered for place values. According to the results, four exits are in balanced areas with stable traffic and customer flow support and strong support from local government departments. This case study contributes to a deeper understanding and evaluation of highway accessibility and provides an exciting assessment tool for sustainable development planning. While node–place models cannot predict development, they can be used to understand development dynamics better.

Research on the Impact of Consumer Preference on the Profit of Catering Enterprises

In recent years, with the increasing improvement of the living standards, people's requirements for catering become higher and higher, which has also led to an increasing number of factors affecting the profit of catering enterprises. However, the choice of consumers plays a dominant role. Meanwhile, Sichuan came top of the revenue of the catering industry in China. Therefore, taking Sichuan cuisine as the research object, this article understands the customer’s consumption preferences by conducting the questionnaires, applies SPSS to the correlation analysis on the survey results, gets the hang of the impact of customer’s consumption preferences on the customer flow or profit of catering enterprises, and proposes the research prospects. In addition, it is hoped to provide some reference for other enterprises in the same industry.

Using smart meters data to model customer water consumption and evaluate customer flow patterns

ABSTRACT The correct determination of water consumption is key to increasing hydraulic modeling reliability. In this context, Smart Metering (SM) is an important concept. The SM systems can provide consumption at a customer level and in small intervals of time. In this scenario, the work focused on a residential condominium, in Brazil, where an SM system was implemented. The work uses data obtained to model consumers’ water consumption and proposes a system to detect metering failures and internal water loss. Three models were proposed: one uses Autoregressive Integrated Moving Average (ARIMA) theory, another uses the Seasonal Autoregressive Integrated Moving Average (SARIMA), and the last uses multivariate linear regression. The average root mean square error of the first model was 14,73, of the second was 13,50, and of the third was 20,99. A metering and consumption system, able to detect internal leaks and others, was also developed.

Data Analysis and Optimization of Thermal Environment in Underground Commercial Building in Zhengzhou, China

Underground commercial buildings have received increasing attention as an emerging place of consumption. However, previous studies on underground commercial buildings have mainly focused on the impact of a specific environment on comfort or energy consumption. Few studies have been conducted from the perspective of functional use. The purpose of this paper is to investigate, in terms of functional angles, the indoor thermal environment and air quality of an underground commercial building in Zhengzhou, China, and put forward an optimal control strategy of ventilation organization. The results showed that the relative humidity of the underground shopping mall was generally above 60%, and the average temperature of 29.1 °C led to a thermal comfort problem in the catering area in summer. Meanwhile, the concentration of CO2 exceeded the allowed figures during the peak of the customer flow rate, and PM2.5 concentration in the catering area also exceeded the standard, by 43.3% and 33.3%, respectively. Furthermore, to solve the indoor thermal environment and air quality problems found in the field measurements, this study assessed the air distribution by adopting three different air supply schemes for the catering area. Optimization results showed that compared with the ceiling supply, the side supply scheme kept the air temperature 0.4 °C cooler in summer and 0.5 °C warmer in winter. The temperature uniformity increased by 5.4% and 3.7%, and the velocity uniformity increased by 6.5% and 8.8%, respectively. This study can provide theoretical support for thermal environment construction and ventilation organization control of underground commercial buildings.

The Effect of Omnichannel Integration on Fresh Food Customer Engagement from the Viewpoint of Flow Experience

Customer engagement is a new value index affecting the omnichannel benefits of enterprises. It is beginning to attract the attention of both scholars and managers. However, there is still a lack of research on the mechanism influencing customer engagement in the fresh food omnichannel retail context. Drawing from the stimulus–organism–response (SOR) framework, this paper builds a relationship model between omnichannel integration and fresh food customer engagement. In addition, the paper empirically analyzes the mechanism of omnichannel integration’s impact on fresh food customer engagement. The findings show that omnichannel integration promotes flow experiences through three modes: information integration, business integration, and service and distribution integration. Flow experience is mediating the relationship between omnichannel integration and customer engagement. Strengthening the customer engagement knowledge system can help fresh food omnichannel retailers to create and manage the long-term cooperative relationship between customers and the company’s value creation. The higher the degree of omnichannel integration of fresh food retail firms, the more conducive it is to enhancing the customer flow experience, which will positively impact customers’ attitudinal and behavioral engagement.

Effect Of Gamification Principles And Perceived Ease Of Use Of Gamification In Mobile Devices On Customer Engagement And Flow State To Increase Mobile Shopping Loyalty Of Shopee Marketplace Users

The purpose of this research is to analyze the role of Gamification Principles and Perceived Ease of Use of Gamification in Mobile Devices on Customer Engagement and Flow State to Increase Mobile Shopping Loyalty of Shopee Marketplace Users in Aceh Province. The population used in this study was the entire population in this study were all Shopee users in Aceh Province. The number of samples in this study was 200 people. The sampling technique used is non-probability convenience sampling. Data processing was carried out using SPSS and Amos software using the structural equation model (SEM) technique. The results showed that of the 7 direct influence hypotheses tested, there was one that was not significant, namely the Effect of Gamification Principles on Shopping_Loyalty. The highest coefficient of influence that determines customers to become loyal online buyers is Customer Engagement. This means that the higher the level of customer engagement due to the gamification of this shopee brand, the higher the level of customer loyalty to the Shopee marketplace.

Effect of Gamification Principles and Perceived Ease of Use of Gamification in Mobile Devices on Customer Engagement and Flow State to Increase Mobile Shopping Loyalty of Shopee Marketplace Users

Effect of Gamification Principles and Perceived Ease of Use of Gamification in Mobile Devices on Customer Engagement and Flow State to Increase Mobile Shopping Loyalty of Shopee Marketplace Users

Model selection of Gaussian mixture process and its application

In this article, new penalized likelihood methods are proposed for model selection of Gaussian mixture process. Our methods integrate functional principal component analysis, kernel density regression, and penalized estimation, which can be carried out by EM algorithms. Component selection and parameter estimation are conducted simultaneously. Monte Carlo simulation shows that our methods require less computation and have accurate estimation even with not well separated data. The methods are further applied to a supermarket customer flow data to reveal some interesting patterns of shopping behavior.

Long-term forecasting of hourly retail customer flow on intermittent time series with multiple seasonality

In this study, we address a demanding time series forecasting problem that deals simultaneously with the following: (1) intermittent time series, (2) multi-step ahead forecasting, (3) time series with multiple seasonal periods, and (4) performance measures for model selection across multiple time series. Current literature deals with these types of problems separately, and no study has dealt with all these characteristics simultaneously. To fill this knowledge gap, we begin by reviewing all the necessary existing literature relevant to this case study with the goal of proposing a framework capable of achieving adequate forecast accuracy for such a complex problem. Several adaptions and innovations have been conducted, which are marked as contributions to the literature. Specifically, we proposed a weighted average forecast combination of many cutting-edge models based on their out-of-sample performance. To gather strong evidence that our ensemble model works in practice, we undertook a large-scale study across 98 time series, rigorously assessed with unbiased performance measures, where a week seasonal naïve was set as a benchmark. The results demonstrate that the proposed ensemble model achieves eye-catching forecasting accuracy.

Antecedents of Customer Brand Engagement Affecting Customer Satisfaction, Trust, and Commitments in Forming Loyalty and Word of Mouth in Indonesia

Manuscript type: Research Article Research Aims: The research objective was to determine customer brand engagement (CBE) drivers, which directly or indirectly generate word of mouth (WOM) on Instagram through trust, commitment, customer satisfaction, and loyalty. Design/methodology/approach: This research was conducted in Indonesia with 213 respondents who are active on Instagram accounts, have an age range between 20 and 60 years, and have an attachment to products from middle to upper-class brands in Indonesia. This quantitative research uses the Structural Equation Model (SEM) method. Research Findings: The results show that customer involvement and flow experience are the main drivers of CBE, which then directly generate WOM, customer trust, and customer commitment. CBE also indirectly forms WOM through trust and commitment. Theoretical Contribution/Originality: This study can explain the importance of customer involvement and experience in forming CBE to gain loyalty and word of mouth through Instagram in Indonesia. Practitioner/Policy Implication: The managerial implication of the research is about brand-building strategies, forming satisfaction, trust, and commitment to brand loyalty in the short and long term through disseminating information on social media such as Instagram. Research limitation/Implications: This study has limitations; first, the research data only relies on one social networking site (Instagram) in Indonesia to test hypotheses. Second, the survey was about middle-class and above-class product brands. Third, in this case, it has not discussed demographics about age.

Impact of digital consumer habits on innovation management in the hospitality and tourism industry

The development of high technologies is a process that can characterize the 21st century. Many processes in the life of society today are presented in an online format, the Internet space has become a platform for conducting many basic operations: conducting transactions, concluding purchase and sale transactions, etc. Of course, digital progress is also having a significant impact on the tourism industry, especially its recovery from the loss of customers due to restrictions due to the COVID-19 virus pandemic. The article identifies the main trends in consumer behavior after quarantine as vectors for the development of the hospitality and tourism industry to develop a marketing concept and build guest confidence in terms of security measures. The authors also consider the rate of recovery of the tourism and hospitality sector after the coronavirus epidemic in 2020, highlight domestic promising destinations for vacationers, and reveal a change in the attitude of citizens towards traveling around their native country. The article analyzes the results of a study by a large travel company Amadeus to study the opinion of citizens on the impact of technology on the organization of travel in Russia and foreign tours, on the basis of which the authors propose the most appropriate information tools that are not only popular among consumers and can help tourism enterprises attract more guests, but also capable of making it easier for tourists to travel, ensure safety on the road and gain consumer confidence, which will have a positive effect on increasing company revenue, customer flow, and gaining an advantage for Russian market players in the international arena.

Applied strategic management by managers in the hotel and tourism industry in Cyprus and Greece during the COVID-19 pandemic

This qualitative study was conducted to help combat the adverse effects of the COVID-19 pandemic. The purpose of the study was to offer practical and tactical strategies for navigating the uncertainty and risk prevalent in today's post-pandemic business environment in the hotel and tourism industry in Cyprus and Greece. Preliminary forecasts indicated that the tourism and hotel sector's revenues would drop by 20% to 25% compared to 2019, amounting to four or five billion euros in losses. Data were collected from 32 hotel managers in Cyprus and Greece using a four-item web-based interview tool. It was identified that most SMEs encountered a reduction in sales and customer flow and an increase in the unemployment rate due to the COVID-19 pandemic. The managers proposed adopting business strategies and diversifications to generate sales from different sectors of the hotel business to aid in keeping the business afloat in case of a future crisis.

Heterogeneous Attention Concentration Link Prediction Algorithm for Attracting Customer Flow in Online Brand Community

Attracting users from a mature large online product community to a new small one by friend recommendation is vital for new product marketing in social network. However, the traditional link prediction algorithms for friend recommendation cannot get high accuracy because of the network sparsity and scale-free problems when attracting customer flow between large and small circles. In order to better adapt to the link prediction of node pairs between circles of different sizes, we propose a collaborative combined link prediction algorithm (CCLPA), which can deeply extract user attention concentration (AC) features in sparse networks. CCLPA possesses three distinctive merits. Firstly, different edges in the network are assigned different attention, and heterogeneous attention concentration indexes (HACIs) within and beyond triadic closure structure are defined accordingly. Second, a random forest (RF) model is designed to adaptively select the appropriate HACIs for a given circle structure, so as to avoid the impact of scale-free problem on link prediction accuracy between different circles. Third, according to the collaboration of the selected indexes and their sensitivity to the circle structure, appropriate sensitive collaborative heterogeneous attention concentration index (SCHACI) is built to avoid the negative impact of blind combination of indexes on predicted performance. Experimental results on Twitter confirm the effectiveness of our proposed method in attracting customer flow in online brand community.

Some approaches to increasing the turnover of a small commercial enterprise

The article highlights the measures aimed at increasing the turnover of a commercial enterprise. The whole set of measures that contribute to the increase in the turnover of a trading enterprise can be combined into two groups: internal and external. Internal events are aimed at increasing the conversion of sales and the average check, while external ones are aimed at increasing customer flow. Tools for internal measures to increase turnover include, firstly, measures aimed at improving the efficiency of retail processes, improving the technology of commodity display and sales of goods, and, secondly, tools for rationalizing the use of the existing material and technical base and increasing inventory turnover. As part of external events, an increase in client flow can be ensured through active marketing and promotional activities of the organization. The purpose of these events is to activate the already existing customer coverage area, as well as its expansion by attracting potential buyers from more remote areas.

Effects of Covid-19 Pandemic on Informal Sector’s Women in Damak, Jhapa

Economic sectors are broadly categorized as formal and informal sectors. Informal sector is also known as informal economy and grey economy. Informal sectors are beyond the monitoring system and tax system of government. To explore social and economic effects ofcovid-19 on informal sector’s women, this study is conducted as the title of effects of covid-19 pandemic on informal sector’s women in damak, Jhapa. It is based on the theoretical assumption of feminism. It employs exploratory research design and is based on both primary and secondary sources of data. The study revealed that there are very bad effects ofcovid-19 pandemic on women’s livelihood, who are involved in informal economic sectors especially in street vending. The pandemic has indeed affected by decreasing the income of the women. The working condition in regards to hours of work per day and days of work per week has decreased significantly affecting the income. Additional problems such as less customer flow, low income, increased municipality eviction and lack of supplies of goods increased and led to indebtedness, and no saving at all during the pandemic. It suggests that concerned authorities should make effective institutional frameworks to empower women involved in informal sector of economy.

Guest Editorial: Special Section on Embracing Artificial Intelligence for Network and Service Management

Artificial Intelligence (AI) has the potential to leverage the immense amount of operational data of clouds, services, and social and communication networks. As a concrete example, AI techniques have been adopted by telcom operators to develop virtual assistants based on advances in natural language processing (NLP) for interaction with customers and machine learning (ML) to enhance the customer experience by improving customer flow. Machine learning has also been applied to finding fraud patterns which enables operators to focus on dealing with the activity as opposed to the previous focus on detecting fraud.

Evaluating the Dynamic Impact of Theater Performances and Sports Events on Parking Demand in Downtown Pittsburgh

The number of drivers using parking facilities (parking demand) in downtown Pittsburgh is highly variable throughout business operating hours, which makes an efficient operation of parking facilities challenging and results in congestion around the facilities. In this study, we applied an event-based ordinary least squares (OLS) regression model to the parking data set provided from one of the parking facilities, the Theater Square Garage in downtown Pittsburgh. We demonstrated that our model achieved a high R-squared value during time periods when parking demand is highly variable. Using the model, we revealed the dynamic (time-dependent) impact of theater performances and sports events on parking demand. This dynamic information can help facility managers appropriately adjust their operating settings (e.g., the number of staff and fee structure) during surge or vacant time periods accordingly. This model is applicable to various businesses in downtown areas that have increased customer flow from theater performances and sports events, not only parking garages.

Price Discovery Pada Pasar Obligasi Pemerintah Indonesia

The study investigate price discovery in Indonesia government bond market. Understanding price discovery is a must to identify relevant factor affecting price of the asset. This research will cover two tier market, first tier is interdealer market and second tier is customer market. We study the dealer and customer order flow to identify which side that have greater impact in price formation in government bond market. The results show that from the two layers of order flow, the short-term dealer order flow and short-term customer order flow have role in price discovery process. Customer flow order regression coefficient is larger than the dealer order flow. Customers trade not only trade based on liquidity but also have information in making decisions or making investment portfolios (informed customers). At the group dealer order flow, dealers have a heterogeneous role and have different segments.

Hierarchal Formation of Trust on Peer-to-Peer Lodging Platforms

Based on the concept of trust from a hierarchical approach and with the support of processing fluency theory, the current study investigates customer trust, and its relationships with flow experience, satisfaction, and repatronage intention on peer-to-peer (P2P) lodging platforms. Customer flow experience on the Airbnb platform is verified to be an antecedent of economy-based trust. The hierarchical organization of trust is also proved applicable in the context of sharing economy websites (economy-based trust → information-based trust → identification-based trust). The results provide marketers and hosts of P2P lodging platforms effective strategies to assist customers form trust in sequential steps and in multiple perspectives. The present study is a pioneer which in depth examined trust formation in the emerging sharing economy platforms.

A Traffic Interval Prediction Method Based on ARIMA

The purpose of this paper is to construct an autoregressive integrated moving average model (ARIMA) for forecasting the traffic interval, which is helpful for the mobile industry to forecast the change of requirements for the peak of customer traffic, adjust the bandwidth dynamically, and improve the ability of active service. In this paper, the lateral time series analysis method is applied to analyze the data of the peak traffic of the uplink and downlink network from August to October of 2017 and 2018 to establish ARIMA prediction model for determining the parameters of it. MAPE method is used for model assessment and model diagnosis. Then, the optimal forecasting model is selected and the forecast error rate is calculated as the adjustment parameter of the forecast range. The model is used to forecast the customer flow range in the first three days of October 2019. Finally, the MIA method is proposed to compare with the LSTM algorithm and linear regression method for interval cumulative error comparison of traffic interval prediction. The result shows that the ARIMA (0,1,0) model has the lowest mean interval error rate of 15.5%, which proves the reliability of the model in predicting peak traffic interval.