scholarly journals Customer segment transition through the customer loyalty program

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Makoto Kimura
Keyword(s):  
Customer Loyalty ◽  
Dynamic Models ◽  
Time Series Data ◽  
Series Data ◽  
Retail Industry ◽  
Loyalty Program ◽  
Content Type ◽  
Scenario Evaluation ◽  
Customer Segment ◽  
Digital Strategy

PurposeThis study presents the applicability of a model-based approach for loyalty program forecasting using smartphone app in the digital strategy of the retail industry.Design/methodology/approachThe authors develop a dynamic model with the cyclical structure of customer segments through customer experience. They use time-series data on the number of members of the loyalty program, “Seven Mile Program” and confirm the validity of the approximate calculation of customer segment share, customer segment sales share and aggregate sales performance. The authors present three medium-term forecast scenarios after the launch of a smartphone payment service linked with the loyalty program.FindingsThe sum of the two customer segment shares for forecasting (the sum of the quasi-excellent and excellent customer ratios) is about 30% in each scenario, consistent with an essential customer loyalty (true loyalty) share obtained in the existing empirical study.Research limitations/implicationsDigital strategy in the retail industry should focus more on estimating and forecasting average amounts of customer segments and the number of aggregated customers through the digitalization on the customer side than on individual customer journeys and responses.Practical implicationsMulti-scenario evaluation through simulation of dynamic models from a systemic view can be used for decision-making in retailing digital strategies.Originality/valueThis study builds a model that integrates the cyclicality of customer segment transition through customer experiences into a loyalty matrix framework, which is a method that has previously been used in the hospitality industry.

Exploring the dynamic contribution behavior of editors in wikis based on time series analysis

2016 ◽  
Vol 50 (1) ◽  
pp. 41-57 ◽  
Author(s):  
Linghe Huang ◽  
Qinghua Zhu ◽  
Jia Tina Du ◽  
Baozhen Lee
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Time Series Data ◽  
Time Of Day ◽  
Observation Time ◽  
Series Data ◽  
Clustering Methods ◽  
Content Type ◽  
Free Rider Problem ◽  
Series Analysis

Purpose – Wiki is a new form of information production and organization, which has become one of the most important knowledge resources. In recent years, with the increase of users in wikis, “free rider problem” has been serious. In order to motivate editors to contribute more to a wiki system, it is important to fully understand their contribution behavior. The purpose of this paper is to explore the law of dynamic contribution behavior of editors in wikis. Design/methodology/approach – After developing a dynamic model of contribution behavior, the authors employed both the metrological and clustering methods to process the time series data. The experimental data were collected from Baidu Baike, a renowned Chinese wiki system similar to Wikipedia. Findings – There are four categories of editors: “testers,” “dropouts,” “delayers” and “stickers.” Testers, who contribute the least content and stop contributing rapidly after editing a few articles. After editing a large amount of content, dropouts stop contributing completely. Delayers are the editors who do not stop contributing during the observation time, but they may stop contributing in the near future. Stickers, who keep contributing and edit the most content, are the core editors. In addition, there are significant time-of-day and holiday effects on the number of editors’ contributions. Originality/value – By using the method of time series analysis, some new characteristics of editors and editor types were found. Compared with the former studies, this research also had a larger sample. Therefore, the results are more scientific and representative and can help managers to better optimize the wiki systems and formulate incentive strategies for editors.

Dynamic prediction of Indian stock market: an artificial neural network approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Himanshu Goel ◽  
Narinder Pal Singh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stock Market ◽  
Institutional Investors ◽  
Time Series Data ◽  
Gradient Algorithm ◽  
Series Data ◽  
Content Type ◽  
Artificial Neural ◽  
Indian Stock Market

Purpose Artificial neural network (ANN) is a powerful technique to forecast the time series data such as the stock market. Therefore, this study aims to predict the Indian stock market closing price using ANNs. Design/methodology/approach The input variables identified from the literature are some macroeconomic variables and a global stock market factor. The study uses an ANN with Scaled Conjugate Gradient Algorithm (SCG) to forecast the Bombay Stock Exchange (BSE) Sensex. Findings The empirical findings reveal that the ANN model is able to achieve 93% accuracy in predicting the BSE Sensex closing prices. Moreover, the results indicate that the Morgan Stanley Capital International world index is the most important variable and the index of industrial production is the least important in predicting Sensex. Research limitations/implications The findings of the study have implications for the investors of all categories such as foreign institutional investors, domestic institutional investors and investment houses. Originality/value The novelty of this study lies in the fact that there are hardly any studies that use ANN to forecast the Indian stock market using macroeconomic indicators.

Has the low level of economic growth spurred informal sector activities in Uganda? An empirical analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Stephen Esaku
Keyword(s):  
Economic Growth ◽  
Informal Sector ◽  
Shadow Economy ◽  
Time Series Data ◽  
Series Data ◽  
Content Type ◽  
Low Level ◽  
Revenue Collection ◽  
Long Run ◽  
Short Run

PurposeIn this paper, the authors examine how economic growth shapes the shadow economy in the long and short run.Design/methodology/approachUsing annual time series data from Uganda, drawn from various data sources, covering the period from 1991 to 2017, the authors apply the ARDL modeling approach to cointegration.FindingsThis paper finds that an increase in economic growth significantly reduces the size of the shadow economy, in both the long and short run, all else equal. However, the long-run relationship between the shadow economy and growth is non-linear. The results suggest that the rise of the shadow economy could partially be attributed to the slow and sluggish rate of economic growth.Practical implicationsThese findings imply that addressing informality requires addressing underlying factors of underdevelopment since improvements in economic growth also translate into a reduction in the size of the shadow economy in the short and long run.Originality/valueThese findings reveal that the low level of economic growth is an issue because it spurs informal sector activities in the short run. However, as the economy improves, it becomes an incentive for individuals to operate in the informal sector. Additionally, tackling shadow activities in the short run could help improve tax revenue collection.

An improved morphological weighted dynamic similarity measurement algorithm for time series data

2018 ◽  
Vol 11 (4) ◽  
pp. 486-495
Author(s):  
Ke Yi Zhou ◽  
Shaolin Hu
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Morphological Changes ◽  
Series Data ◽  
Similarity Measurement ◽  
Improved Method ◽  
Control Measurement ◽  
Content Type ◽  
Basic Work ◽  
Measurement Algorithm

Purpose The similarity measurement of time series is an important research in time series detection, which is a basic work of time series clustering, anomaly discovery, prediction and many other data mining problems. The purpose of this paper is to design a new similarity measurement algorithm to improve the performance of the original similarity measurement algorithm. The subsequence morphological information is taken into account by the proposed algorithm, and time series is represented by a pattern, so the similarity measurement algorithm is more accurate. Design/methodology/approach Following some previous researches on similarity measurement, an improved method is presented. This new method combines morphological representation and dynamic time warping (DTW) technique to measure the similarities of time series. After the segmentation of time series data into segments, three parameter values of median, point number and slope are introduced into the improved distance measurement formula. The effectiveness of the morphological weighted DTW algorithm (MW-DTW) is demonstrated by the example of momentum wheel data of an aircraft attitude control system. Findings The improved method is insensitive to the distortion and expansion of time axis and can be used to detect the morphological changes of time series data. Simulation results confirm that this method proposed in this paper has a high accuracy of similarity measurement. Practical implications This improved method has been used to solve the problem of similarity measurement in time series, which is widely emerged in different fields of science and engineering, such as the field of control, measurement, monitoring, process signal processing and economic analysis. Originality/value In the similarity measurement of time series, the distance between sequences is often used as the only detection index. The results of similarity measurement should not be affected by the longitudinal or transverse stretching and translation changes of the sequence, so it is necessary to incorporate the morphological changes of the sequence into similarity measurement. The MW-DTW is more suitable for the actual situation. At the same time, the MW-DTW algorithm reduces the computational complexity by transforming the computational object to subsequences.

A deep learning method for data recovery in sensor networks using effective spatio-temporal correlation data

Sensor Review ◽  
2019 ◽  
Vol 39 (2) ◽  
pp. 208-217 ◽  
Author(s):  
Jinghan Du ◽  
Haiyan Chen ◽  
Weining Zhang
Keyword(s):  
Deep Learning ◽  
Sensor Networks ◽  
Large Scale ◽  
Time Series Data ◽  
Temporal Correlation ◽  
Data Recovery ◽  
Series Data ◽  
Learning Method ◽  
Content Type ◽  
Spatio Temporal

Purpose In large-scale monitoring systems, sensors in different locations are deployed to collect massive useful time-series data, which can help in real-time data analytics and its related applications. However, affected by hardware device itself, sensor nodes often fail to work, resulting in a common phenomenon that the collected data are incomplete. The purpose of this study is to predict and recover the missing data in sensor networks. Design/methodology/approach Considering the spatio-temporal correlation of large-scale sensor data, this paper proposes a data recover model in sensor networks based on a deep learning method, i.e. deep belief network (DBN). Specifically, when one sensor fails, the historical time-series data of its own and the real-time data from surrounding sensor nodes, which have high similarity with a failure observed using the proposed similarity filter, are collected first. Then, the high-level feature representation of these spatio-temporal correlation data is extracted by DBN. Moreover, to determine the structure of a DBN model, a reconstruction error-based algorithm is proposed. Finally, the missing data are predicted based on these features by a single-layer neural network. Findings This paper collects a noise data set from an airport monitoring system for experiments. Various comparative experiments show that the proposed algorithms are effective. The proposed data recovery model is compared with several other classical models, and the experimental results prove that the deep learning-based model can not only get a better prediction accuracy but also get a better performance in training time and model robustness. Originality/value A deep learning method is investigated in data recovery task, and it proved to be effective compared with other previous methods. This might provide a practical experience in the application of a deep learning method.

Impact assessment of political administrations on the performance of the construction sector: a time series analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Najimu Saka ◽  
Abdullahi Babatunde Saka ◽  
Opeoluwa Akinradewo ◽  
Clinton O. Aigbavboa
Keyword(s):  
Time Series ◽  
Growth And Development ◽  
Time Series Data ◽  
Critical Factor ◽  
Sustainable Growth ◽  
Analysis Of Covariance ◽  
Series Data ◽  
Construction Sector ◽  
Content Type ◽  
Ancova Model

Purpose The complex interaction of politics and the economy is a critical factor for the sustainable growth and development of the construction sector (CNS). This study aims to investigate the effects of type of political administration including democracy and military on the performance of CNS using the Nigerian Construction Sector (NCS) as a case study. Design/methodology/approach A 48 year (1970–2017) time series data (TSD) on the NCS and the gross domestic product (GDP) based on 2010 constant USD were extracted from the United Nations Statistical Department database. Analysis of variance (ANOVA) and analysis of covariance (ANCOVA) models were used to analyze the TSD. The ANCOVA model includes the GDP as correlational variable or covariate. Findings The estimates of the ANOVA model indicate that democratic administration is significantly better than military administration in construction performance. However, the ANCOVA model indicates that the GDP is more important than political administration in the performance of the CNS. The study recommends for a new national construction policy, favourable fiscal and monetary policy, local content development policy and construction credit guaranty scheme for the rapid growth and development of the NCS. Originality/value Hitherto, little is known about the influence of political administration on the performance of the CNS. This study provides empirical evidence from a developing economy perspective. It presents the relationships and highlights recommendations for driving growth in the construction industry.

Corporation financial distress prediction with deep learning: analysis of public listed companies in Malaysia

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zulkifli Halim ◽  
Shuhaida Mohamed Shuhidan ◽  
Zuraidah Mohd Sanusi
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Financial Distress ◽  
Time Series Data ◽  
Series Data ◽  
Learning Models ◽  
Content Type ◽  
Financial Distress Prediction ◽  
Distress Prediction ◽  
Gated Recurrent Unit

PurposeIn the previous study of financial distress prediction, deep learning techniques performed better than traditional techniques over time-series data. This study investigates the performance of deep learning models: recurrent neural network, long short-term memory and gated recurrent unit for the financial distress prediction among the Malaysian public listed corporation over the time-series data. This study also compares the performance of logistic regression, support vector machine, neural network, decision tree and the deep learning models on single-year data.Design/methodology/approachThe data used are the financial data of public listed companies that been classified as PN17 status (distress) and non-PN17 (not distress) in Malaysia. This study was conducted using machine learning library of Python programming language.FindingsThe findings indicate that all deep learning models used for this study achieved 90% accuracy and above with long short-term memory (LSTM) and gated recurrent unit (GRU) getting 93% accuracy. In addition, deep learning models consistently have good performance compared to the other models over single-year data. The results show LSTM and GRU getting 90% and recurrent neural network (RNN) 88% accuracy. The results also show that LSTM and GRU get better precision and recall compared to RNN. The findings of this study show that the deep learning approach will lead to better performance in financial distress prediction studies. To be added, time-series data should be highlighted in any financial distress prediction studies since it has a big impact on credit risk assessment.Research limitations/implicationsThe first limitation of this study is the hyperparameter tuning only applied for deep learning models. Secondly, the time-series data are only used for deep learning models since the other models optimally fit on single-year data.Practical implicationsThis study proposes recommendations that deep learning is a new approach that will lead to better performance in financial distress prediction studies. Besides that, time-series data should be highlighted in any financial distress prediction studies since the data have a big impact on the assessment of credit risk.Originality/valueTo the best of authors' knowledge, this article is the first study that uses the gated recurrent unit in financial distress prediction studies based on time-series data for Malaysian public listed companies. The findings of this study can help financial institutions/investors to find a better and accurate approach for credit risk assessment.

The mediating effects of program loyalty in loyalty rewards programs: an experimental design in coffee shops

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pınar Başgöze ◽  
Yaprak Atay ◽  
Selin Metin Camgöz ◽  
Lydia Hanks
Keyword(s):  
Experimental Design ◽  
Customer Loyalty ◽  
Perceived Value ◽  
Deep Understanding ◽  
Loyalty Program ◽  
Content Type ◽  
Value Perception ◽  
Study Results ◽  
The Impact ◽  
The Relationship

PurposeThe purpose of this study is to evaluate the impact of reward structure on the customer's value perception of the program, loyalty to the program and loyalty to the firm.Design/methodology/approachA 2 (type of reward) x 2 (timing of redemption) between subjects experimental design was conducted. In addition, the indirect effect of the customer's value perception of the program on loyalty to the firm via loyalty to the program is tested with Hayes and Preacher's mediation procedure.FindingsStudy results indicated that type of reward has a positive impact on the perceived value of a loyalty program. Program loyalty mediates the relationship between the perceived value of the loyalty program and customer loyalty, as well as the relationship between type of reward and customer loyalty.Originality/valueThe findings of this study demonstrate the importance of the type and timing of loyalty program rewards on customer perceptions of the value of the loyalty program. In addition, this study is a step forward in providing a deep understanding of the impact of such perceptions on loyalty. These findings fill a number of research gaps and provide tangible guidance for practitioners.

A history of university income in the United Kingdom and Australia, 1922–2017

2020 ◽  
Vol 49 (2) ◽  
pp. 229-248
Author(s):  
Tamson Pietsch
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Content Type ◽  
The United Kingdom ◽  
History Of University ◽  
History Of ◽  
Strategic Influence ◽  
The Uk ◽  
Comparable Time

PurposeThe purpose of this paper is to create comparable time series data on university income in Australia and the UK that might be used as a resource for those seeking to understand the changing funding profile of universities in the two countries and for those seeking to investigate how such data were produced and utilised.Design/methodology/approachA statistical analysis of university income from all sources in the UK and Australia.FindingsThe article produces a new time series for Australia and a comparable time series for the UK. It suggests some of the ways these data related to broader patterns of economic change, sketches the possibility of strategic influence, and outlines some of their limitations.Originality/valueThis is the first study to systematically create a time series on Australian university income across the twentieth century and present it alongside a comparable dataset for the UK.

Assessing the role of product category involvement and relationship proneness in the satisfaction–loyalty link in retailing

2019 ◽  
Vol 48 (2) ◽  
pp. 207-226 ◽  
Author(s):  
Choukri Menidjel ◽  
Abderrezzak Benhabib ◽  
Anil Bilgihan ◽  
Melih Madanoglu
Keyword(s):  
Relationship Marketing ◽  
Customer Loyalty ◽  
Purchase Intention ◽  
Critical Role ◽  
Product Category ◽  
Retail Industry ◽  
Mediating Effects ◽  
Content Type ◽  
The Relationship

Purpose Product category involvement and relationship proneness are crucial in explaining relationship outcomes. Nevertheless, the authors know little about their roles in the formation of loyalty, especially in the retail industry. Individual consumer traits and preferences are likely to play a critical role in the success of relationship marketing. Yet, relationship marketing studies have fallen short of considering such individual differences. The purpose of this paper is to examine the mediating effects of product category involvement and relationship proneness on the relationship between satisfaction and loyalty in retail clothing stores. Design/methodology/approach Data were obtained using a survey of 220 consumers. Partial least squares structural equation modeling was employed to test the proposed theoretical model. Findings The results show that satisfaction significantly affects product category involvement and relationship proneness, which, in turn, significantly affect purchase intention and word-of-mouth (WOM). The results also show that product category involvement and relationship proneness partially mediate the impact of satisfaction on purchase intention and WOM. Research limitations/implications Product category involvement and relationship proneness play a critical role in explaining the satisfaction–loyalty link. Future research could consider the role of potential moderating variables. Practical implications Retail managers should not only focus on improving customer satisfaction to achieve customer loyalty, but should also consider the importance of product category involvement and relationship proneness, and their role in the formation of customer loyalty both in traditional and online environments. Originality/value To the best of the authors’ knowledge, this study is the first to explore the mediating effects of product category involvement and relationship proneness on the relationship between satisfaction, purchase intention and WOM in the retail industry.

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