Search results

The rapid development of e-commerce has brought not only great convenience to people but a great challenge to online stores. Phenomenon such as out of stock and slow sales has been common in recent years. These issues can be managed only when the occurrence of the sales volume is predicted in advance, and sufficient warnings can be executed in time. Thus, keeping in mind the importance of the sales prediction system, the purpose of this paper is to propose an effective sales prediction model and make digital marketing strategies with the machine learning model.

Based on the consumer purchasing behavior decision theory, we discuss the factors affecting product sales, including external factors, consumer perception, consumer potential purchase behavior and consumer traffic. Then we propose a sales prediction model, M-GNA-XGBOOST, using the time-series prediction that ensures the effective prediction of sales about each product in a short time on online stores based on the sales data in the previous term or month or year. The proposed M-GNA-XGBOOST model serves as an adaptive prediction model, for which the instant factors and the sales data of the previous period are the input, and the optimal computation is based on the proposed methodology. The adaptive prediction using the proposed model is developed based on the LSTM (Long Short-Term Memory), GAN (Generative Adversarial Networks) and XGBOOST (eXtreme Gradient Boosting). The model inherits the advantages among the algorithms with better accuracy and forecasts the sales of each product in the store with instant data characteristics for the first time.

The analysis using Jingdong dataset proves the effectiveness of the proposed prediction method. The effectiveness of the proposed method is enhanced and the accuracy that instant data as input is found to be better compared with the model that lagged data as input. The root means squared error and mean absolute error of the proposed model are found to be around 11.9 and 8.23. According to the sales prediction of each product, the resource can be arranged in advance, and the marketing strategy of product positioning, product display optimization, inventory management and product promotion is designed for online stores.

The paper proposes and implements a new model, M-GNA-XGBOOST, to predict sales of each product for online stores. Our work provides reference and enlightenment for the establishment of accurate sales-based digital marketing strategies for online stores.

The purpose of this article is to explain how the management of the two areas business intelligence (BI) and customer insight (CI) needs to be brought together to support a company's interactive marketing.

The article is based on the author's work in consultancy and in assessing client company's customer management capabilities and performance, as well as a review of some of the literature on BI and CI.

The article suggests that companies need to pay close attention to the governance of BI, as a self-service approach to BI becomes increasingly used by CI teams.

The review of literature carried out by the authors suggests that the interface between BI and CI is poorly researched and would benefit from a significant research effort.

The focus on the interface between BI and CI is relatively new. The authors hope that it will trigger significant research.

This study aims to explore the opposite effects of single-category versus multi-category products information diversity on consumer decision making. Further, the authors investigate the moderating role of three categories of visitors – direct, hesitant and hedonic – in the relationship between product information diversity and consumer decision making.

The research utilizes a sample of 1,101,062 product click streams from 4,200 consumers. Visitors are clustered using the k-means algorithm. The diversity of information recommendations for single and multi-category products is characterized using granularity and dispersion, respectively. Empirical analysis is conducted to examine their influence on the two-stage decision-making process of heterogeneous online visitors.

The study reveals that the impact of recommended information diversity on consumer decision making differs significantly between single-category and multiple-category products. Specifically, information diversity in single-category products enhances consumers' click and purchase intention, while information diversity in multiple-category products reduces consumers' click and purchase intention. Moreover, based on the analysis of online visiting heterogeneity, hesitant, direct and hedonic features enhance the positive impact of granularity on consumer decision making; while direct features exacerbate the negative impact of dispersion on consumer decision making.

First, the article provides support for studies related to information cocoon. Second, the research contributes evidence to support the information overload theory. Third, the research enriches the field of precision marketing theory.

Today's marketing is dominated by decision-making based on artificial intelligence and machine learning. This study focuses on one semi- and unsupervised machine learning technique, generative adversarial networks (GANs). GANs are a type of deep learning architecture capable of generating new data similar to the training data that were used to train it, and thus, it is designed to learn a generative model that can produce new samples. GANs have been used in multiple marketing areas, especially in creating images and video and providing customized consumer contents. Through providing a holistic picture of GANs, including its advantage, disadvantage, ethical considerations, and its current application, the study attempts to provide business some strategical orientations, including formulating strong marketing positioning, creating consumer lifetime values, and delivering desired marketing tactics in product, promotion, pricing, and distribution channel. Through using GANs, marketers will create unique experiences for consumers, build strategic focus, and gain competitive advantages. This study is an original endeavor in discussing GANs in marketing, offering fresh insights in this research topic.

Obesity is today’s most neglected, yet blatantly visible, public health problem. This study aims to examine the role of social media and goal-directed behavior in motivating healthy lifestyle intentions for customers experiencing obesity. It investigates the distinct roles of self-conscious emotions (shame and pride) and weight-transformational posts shared by others on social media as moderators of these relationships.

The conceptual model uses the goal-directed behavior theory and social comparison theory, tested using data collected from 804 obese customers in Fiji through an experimental design.

Weight-loss transformation posts by others on social media, elicit distinct emotions for obese customers. Obese customers who felt guilt and shame due to shared weight-loss transformation posts showed a stronger association between goal disclosure and healthy lifestyle intention. In addition, the association between goal disclosure and healthy lifestyle intention is conditionally mediated by goal commitment, specifically for those obese customers that elicited guilt over shame due to shared weight-loss transformation posts by others on social media.

Despite the adoption of an experimental design using a fictional stimulus being a commonly used method in marketing studies, external validity issues are likely. Also, this study examines obese customer behavior relating to Facebook. In addition, data collection for this study has been done from a single country perspective. Therefore, caution needs to be exercised when generalizing the findings of this study.

The findings assist businesses and marketers in the health and fitness industry to better leverage social media and goal-directed behavior and understand the emotions of obese customers to undertake data-driven precision marketing strategies.

The findings provide novel insights into goal disclosure and commitment, electronic word-of-mouth on social media platforms, self-conscious emotions and healthy lifestyle intentions for customers experiencing obesity.

This paper investigates how retailers can obtain value from their customer and prospect databases. In the current fickle economic climate it has never been so important for organisations to be able to provide added‐value insights into their immediate and longer‐term development. Total DM conducted a telephone and e‐mail survey amongst marketing professionals from or representing the UK’s top 1,000 companies to determine different sector’s ability to obtain short‐term customer acquisition costs and longer‐term ongoing customer profitability. Secondary research was then performed to measure data richness for each industry. This measure took different factors into account, including penetration of loyalty. Retailers were found to be the overall winners at extending commercial value from their customer and prospect databases. This was greatly influenced by the large volume of customer information potentially available to retailers. The survey concluded that it was individual retailers who were able to exploit their databases to the full who would be able to win investor confidence.

This paper presents a meta-analysis of the electronic banking (e-banking) customer loyalty literature in the last 10 years. The study investigated the moderating role of national culture in the relationship between e-banking customer loyalty and its antecedents.

Using a meta-analysis of customer loyalty in 19 countries, the authors incorporated national culture scores based on Hofstede's cultural dimensions to explore how the relative importance of e-banking customer loyalty antecedents varies across cultures.

The study revealed that national culture moderates the relationship between e-banking customer loyalty and its seven antecedents for four cultural dimensions, yet there was no significant moderation for satisfaction.

This study reviewed the relationships in the literature on customer loyalty in e-banking contexts, extending and enriching the current knowledge. However, some specific limitations, such as the non-use of qualitative studies and the clipping of adverse concepts, exist in the secondary data and should be considered.

The results show that the seven antecedents affect e-banking customer loyalty to different degrees. Managers should incorporate cultural factors in e-banking customer management.

Only a few studies have assessed cultural differences in relation to e-banking customer loyalty. The authors address this need by offering deeper insights into how cultural dimensions moderate the relationships between e-banking customer loyalty and its antecedents through a meta-analytical review. The study findings offer managers a new perspective of leveraging the benefits of cultural differences, enhancing their decision-making in international business.

Observing across four comparative case organizations, the purpose of this paper is to identify two sources of contingency (i.e. construct and contextual contingency) for the relationship between knowledge heterogeneity and innovation.

The contingencies was explored by conducting a comparative case analyses with rich qualitative data extracted and interpreted from four case companies.

First, the construct contingency is examined by refining knowledge heterogeneity into three dimensions: domain, process, and context heterogeneity. Specifically, the author proposed that knowledge heterogeneity in domain is associated with innovation in an inverted U-shape, while heterogeneity in process and context dimensions both negatively influence innovation. Second, contextual contingency is studied. The author proposed that: trust positively moderates the relationship between knowledge heterogeneity and innovation; depending on the knowledge owner attributes, centralization positively or negatively moderates the relationship between heterogeneous knowledge and innovation; shared knowledge vision positively moderates the relationship between knowledge heterogeneity and innovation.

The influences of knowledge heterogeneity on innovation have yet been inconsistent. The present study set to reconcile such inconsistency with a solution of contingencies that intervene the heterogeneity-innovation relationship. These results offer useful references for future large-scaled, quantitative studies.

With the continuous development of online shopping, analyzing the competitiveness of products in the fierce market competition is becoming increasingly crucial to position their own product development. However, the information overload brought by the network development makes it getting difficult to obtain the accurate competitiveness information. Therefore, competitiveness analysis research to combine with the perceived helpfulness study needs urgent solution. Furthermore, deviations exist in the three common methods of perceived helpfulness research. Finally, the traditional information fusion analysis only analyzes the advantages and disadvantages of products in competitiveness analysis without taking account of the competitive environment.

This study puts forward a novel prediction model of perceived helpfulness in conjunction of unsupervised learning and sentiment analysis techniques, to conduct the comparison with pros and cons of congeneric products.

This paper adopts Wilcoxon test to demonstrate the significant rectification of our competitiveness analysis to the traditional methods. It is noted that the positive reviews of the products in this study impact more on product word of mouth and competitiveness than negative ones.

To sum up, the results of this study benefit businesses in locating their dynamic market position with competitors in practice and exploring new method for long-term development strategic planning.

In today's fast developing era, the volume of data is increasing day by day. The traditional methods are lagging for efficiently managing the huge amount of data. The adoption of machine learning techniques helps in efficient management of data and draws relevant patterns from that data. The main aim of this research paper is to provide brief information about the proposed adoption of machine learning techniques in different sectors of manufacturing supply chain.

This research paper has done rigorous systematic literature review of adoption of machine learning techniques in manufacturing supply chain from year 2015 to 2023. Out of 511 papers, 74 papers are shortlisted for detailed analysis.

The papers are subcategorised into 8 sections which helps in scrutinizing the work done in manufacturing supply chain. This paper helps in finding out the contribution of application of machine learning techniques in manufacturing field mostly in automotive sector.

The research is limited to papers published from year 2015 to year 2023. The limitation of the current research that book chapters, unpublished work, white papers and conference papers are not considered for study. Only English language articles and review papers are studied in brief. This study helps in adoption of machine learning techniques in manufacturing supply chain.

This study is one of the few studies which investigate machine learning techniques in manufacturing sector and supply chain through systematic literature survey.

1. A comprehensive understanding of Machine Learning techniques is presented.

2. The state of art of adoption of Machine Learning techniques are investigated.

3. The methodology of (SLR) is proposed.

4. An innovative study of Machine Learning techniques in manufacturing supply chain.

A comprehensive understanding of Machine Learning techniques is presented.

The state of art of adoption of Machine Learning techniques are investigated.

The methodology of (SLR) is proposed.

An innovative study of Machine Learning techniques in manufacturing supply chain.