Apparel Retail Industry Research Articles

Enabling self-service technology (SST) using an intelligent chatbot to advance the online apparel co-design process

ABSTRACT Apparel co-design in online has emerged as a promising approach to enhancing customer experience by offering customised apparel. However, effectively communicating accurate product requirements through product configurators for co-design is a challenge for non-specialists. This study proposes an approach to improve the online apparel co-design by developing a natural language-based, self-service technology-enabled chatbot to effectively elicit customer requirements. The study was based on sports bra as a case study, with a Wizard of Oz approach used to characterise the chatbot requirements through a newly developed website. Thematic analysis was conducted on chatlogs generated by thirty participants who co-designed a sports bra with the assistance of a product expert. Key findings were used to develop a reference model based on a morphological box, which helps design and develop a chatbot on product configurators in the online apparel retail industry to enhance experience through effective customer requirement elicitation.

The challenges to circular economy in the Indian apparel industry: a qualitative study

Purpose In recent years, researchers and practitioners have paid a great deal of attention to the circular economy (CE) due to its potential social and environmental benefits. However, limited attention has been devoted in the literature to studying the barriers to CE implementation in the apparel retail industry in emerging and developing nations besides China. Consequently, the purpose of this paper is to analyse the barriers to implementing CE in the Indian garment retail market. Design/methodology/approach This exploratory study uses a comprehensive literature analysis to identify the micro-level impediments to CE adoption in India's garment retail industry. The study aims to identify these barriers using a Delphi process, consisting of three stages. The first stage involves literature reviews and expert opinions, while the second and third stages involve survey methods with 14 industry professionals and academics. The use of the two primary data sources allows for triangulation of the data, which improves the validity of the findings and enables broader conclusions to be drawn from the results. Findings This study indicates that the top three challenges to implementing CE principles in the Indian apparel retail industry are “standards and regulation barriers” (84%), “strategic barriers” (82%) and “supply chain management and technology barriers” (79%). Strategies for overcoming these obstacles include gaining top management support, coordinating supply chain components, training and employee motivation. Research limitations/implications This study considers only Indian apparel retail industry, and the practical implications could potentially limit the study to emerging Asian economies. Originality/value To the best of the authors’ knowledge, this is the first research of its type to explore CE obstacles at the organizational level in the Indian garment retail business. Thus, it contributes to a greater understanding of the topic and enables practitioners to develop effective policies and business strategies for CE and sustainability.

The Effects of Social Media Advertising on Consumers’ Brand Loyalty: With Special Reference to the Apparel Retailers

The apparel industry faces increased competition from newcomers and imports, making it crucial for local companies to maintain brand loyalty. This research aimed to determine the most significant features of social media advertising (informativeness, entertainment, irritation, credibility) that influence apparel consumers' brand loyalty. The findings showed that social media advertising significantly impacts consumer brand loyalty in the Sri Lankan apparel retail industry. There was no significant relationship between informativeness and brand loyalty, but there were significant relationships between entertainment, irritation, and credibility. The data did not support a positive relationship between informativeness and brand loyalty, but entertainment had the greatest impact. The researcher has notified the Sri Lankan apparel retail industry to act on the findings and suggests future research in another industry to determine if these dimensions are correlated with the dependable variable.

Interactive Websites as Major Drivers of Growth and Innovation in Apparel Retail Industry

Interactive Websites as Major Drivers of Growth and Innovation in Apparel Retail Industry

Trend tracking tools for the fashion industry: the impact of social media

PurposeThis paper seeks to demonstrate the value of using social media to capture fashion trends, including the popularity of specific features of clothing, in order to improve the speed and accuracy of supply chain response in the era of fast fashion.Design/methodology/approachThis study examines the role that text mining can play to improve trend recognition in the fashion industry. Researchers used n-gram analysis to design a social media trend detection tool referred to here as the Twitter Trend Tool (3Ts). This tool was applied to a Twitter dataset to identify trends whose validity was then checked against Google Trends.FindingsThe results suggest that Twitter data are trend representative and can be used to identify the apparel features that are most in demand in near real time.Originality/valueThe 3Ts introduced in this research contributes to the field of fashion analytics by offering a novel method for employing big data from social media to identify consumer preferences in fashion elements and analyzes consumer preferences to improve demand planning.Practical implicationsThe 3Ts improves forecasting models and helps inform marketing campaigns in the apparel retail industry, especially in fast fashion.

Influencer Marketing and its Role in Indian Retail Apparel Industry: A Quantitative Study

Influencer Marketing and its Role in Indian Retail Apparel Industry: A Quantitative Study

Research on Customer Relationship Segmentation of Apparel Retail Industry through Data Mining

Objectives: This paper aims to segment customers in the apparel retail industry using data mining techniques. Methods: First, a customer segmentation model was constructed, and then the K-means algorithm was used to classify customers based on indicators from the model. The classification effectiveness was enhanced by introducing indicator feature weights. A case study was also conducted. Findings: When the value of K was 4, the K-means algorithm achieved the best classification performance. The improved K-means algorithm outperformed the traditional K-means algorithm in terms of classification effectiveness. The improved K-means algorithm categorized customers into premium customers, important customers, regular customers, and churned customers. Different marketing suggestions were proposed to manufacturers. Novelty: The novelty of this article lies in the introduction of feature weights for indicators, which allows for a distinction between their importance and improves classification effectiveness. Doi: 10.28991/HIJ-2023-04-02-05 Full Text: PDF

Editorial Impacts of Technology Management on the Apparel Retailing and Manufacturing Industry

Editorial Impacts of Technology Management on the Apparel Retailing and Manufacturing Industry

Shopping Hard or Hardly Shopping: Revealing Consumer Segments Using Clickstream Data

The recent rise of big data analytics is transforming the apparel retailing industry. E-retailers, for example, effectively use large volumes of data generated as a result of their day-to-day business operations data to aid operations and supply chain management. Although logs of how consumers navigate through an e-commerce website are readily available in a form of clickstream data, clickstream analysis is rarely used to derive insights that can support marketing decisions, leaving it an under-researched area of study. Adding to this research stream by exploring the case of a U.K.-based fast-fashion retailer, this article reveals unique consumer segments and links them to the revenue they are capable of generating. Applying the partitioning around medoids algorithm to three random samples of 10 000 unique consumer visits to the e-commerce site of a fast-fashion retailer, six consumer segments are identified. This article shows that although the “mobile window shoppers” segment consists of the largest consumer segment, it attracts the lowest revenue. In contrast, “visitors with a purpose,” although one of the smallest segments, generates the highest revenue. The findings of this article contribute to marketing research and inform practice, which can use these insights to target customer segments in a more tailored fashion.

Comparative Analysis of Marketing Strategies of Fast Fashion Brands ZARA and H&M

With the development of the social economy, people's pace of life has accelerated, and the concept of consumption has also been affected. Citizens to a high quality of life, as well as people to clothing inside and outside the high aesthetic requirements. In this demanding environment, fast fashion has shifted to the market and thus formed an important consumer trend. ZARA and H&M have become the most iconic brands of fast fashion. They occupy a large market share in the apparel retail industry through their unique marketing strategies. Therefore, this paper explores the marketing strategies of ZARA and H&M from three aspects: product, price, and promotion. The research methods were literature review and comparative analysis. This paper also compares the marketing strategies of ZARA and H&M to find out the existing problems and put forward suggestions.

A linear programming-based QFD methodology under fuzzy environment to develop sustainable policies in apparel retailing industry

As the retailing industry becomes more customer oriented, it struggles with integrating the voice-of-customers into quality development policies, determining accurate customer expectations, and understanding how to incorporate the required store attributes in retailing activities. The aim of this study is to provide managers with a more decisive and sustainable framework to fulfill customer satisfaction by determining the most essential customer needs and gain a competitive advantage by applying a benchmarking process for the whole retailing activities. To essentially support managers in determining and implementing required store attributes, this study develops a sustainable linear programming (LP) based Quality Function Deployment (QFD) methodology under IVIF-environment. The proposed method determines more accurate customer expectations (CEs) and related service requirements (SRs). Accordingly, while “Clothing Quality”, Price Policy”, and “Staff Behavior” are determined as the most important CEs, “Design of Customer Persona”, “Production Cost”, and “Marketing Applications” are obtained as the most affecting SRs. Since no specific study in the literature addresses uncertainty in CEs and SRs in the apparel retailing industry, we developed an LP-based QFD under the IVIF-environment framework, which reflects the ambiguity and vagueness of the evaluations better. Thus, this study contributes to the literature by proposing a sustainable framework for managers to make decisions that are more effective and take sustainable actions. The companies who want to get the advantage in the apparel retailing industry should follow the methodology provided within this study by adding their business specific dimensions. Lastly, to represent the validity and feasibility of the proposed approach sensitivity and comparison analysis are conducted. The results of comparison show that the LP based QFD method is as consistent as other method but more effective in terms of handling ambiguity and fuzziness of expert evaluations, comprehensively.

Positioning through visual merchandising: Can multi-brand outlets do it?

With rising competition in the apparel retail industry in India, multi-brand outlets need to create a particular store image in consumers' minds to ensure consumer loyalty and sales. Past studies indicate the impact of visual merchandising (VM) on store image, but few studies discuss how VM can help in-store positioning. The current study explores how VM elements can be used for store positioning. The study employed a survey-based approach using a structured questionnaire to find the perception of 385 males and females of the 18–25 age groups belonging to one of India's largest cities, Ahmedabad, regarding VM The multi-branded retail outlets selected for the study were - Pantaloons, Westside, Ahmedabad Central, Brand Factory, and Shoppers Stop. The VM elements identified through a literature review were - atmospherics, window display, mannequin display, product display, signages, fixtures, and store props. In addition, discriminant analysis was used to generate a perceptual map, which revealed the unique position of each store in the consumer's mind based on different VM elements. Findings suggest that different combinations of VM elements can help multi-branded retail stores position themselves successfully in the shoppers' minds.

How chatbot e-services motivate communication credibility and lead to customer satisfaction: The perspective of Thai consumers in the apparel retailing context

A Correction to this article was published on 13 July 2023 Many apparel retailing brands use e-service marketing tools such as a chatbot (a system that is available 24 hours a day, 7 days a week) to increase their competitive advantage in today’s world of digitalization. During the COVID-19 pandemic, chatbots gained more power to serve as a communication tool that provides information and maintains customer experience. Therefore, this study is conducted to investigate the influence of chatbot e-service agents’ marketing efforts (involving interaction, entertainment, trendiness, and problem-solving) on Thai customers’ perceived communication credibility and satisfaction in apparel retailing, as research in this area is limited. In order to test the hypotheses, the paper employed structural equation modeling using Amos. In addition, an online survey of 400 Thai consumers who had previously used chatbots in the apparel retailing industry was conducted. The results showed that chatbot e-service marketing efforts, including interaction, trendiness, and problem-solving, affected customer satisfaction without entertainment elements. Beyond this, a chatbot, viewing interaction and entertainment, was found to have an insignificant effect on communication credibility. Thus, the coefficient value proved that information regarding communication credibility is more dominant in customer satisfaction. Therefore, the chatbot e-service marketing effort is essential in motivating communication credibility in customer satisfaction. These findings delivered managerial implications for understanding consumers in the field of digitalization.

Deep Learning for Demand Forecasting in the Fashion and Apparel Retail Industry

Compared to other industries, fashion apparel retail faces many challenges in predicting future demand for its products with a high degree of precision. Fashion products’ short life cycle, insufficient historical information, highly uncertain market demand, and periodic seasonal trends necessitate the use of models that can contribute to the efficient forecasting of products’ sales and demand. Many researchers have tried to address this problem using conventional forecasting models that predict future demands using historical sales information. While these models predict product demand with fair to moderate accuracy based on previously sold stock, they cannot fully be used for predicting future demands due to the transient behaviour of the fashion industry. This paper proposes an intelligent forecasting system that combines image feature attributes of clothes along with its sales data to predict future demands. The data used for this empirical study is from a European fashion retailer, and it mainly contains sales information on apparel items and their images. The proposed forecast model is built using machine learning and deep learning techniques, which extract essential features of the product images. The model predicts weekly sales of new fashion apparel by finding its best match in the clusters of products that we created using machine learning clustering based on products’ sales profiles and image similarity. The results demonstrated that the performance of our proposed forecast model on the tested or test items is promising, and this model could be effectively used to solve forecasting problems.

“Must-have” skills and knowledge for apparel merchandising professionals in South Africa

ABSTRACT Employing competent merchandisers has become a global challenge for apparel companies. This study aimed to prioritize merchandising skills and knowledge required to function in merchandising positions. Adaptive conjoint analysis was employed to prioritize 29 identified skills and knowledge types and 116 individual dimensions describing these types. An online survey was developed and administered using Sawtooth Software Inc. A total of 172 merchandising professionals participated in the study. Sawtooth Software built-in regression analysis was used to analyze the survey responses. A priority order for the 29 skills and knowledge types was established, consisting of 13 “must-have”, 13 “fairly-important”, and three “nice-to-have” types. The 13 “must-have” skills and knowledge types included a mix of six soft skill types (communication, diplomacy, flexibility, administrative/managerial, positive attitude, teamwork), one hard skill type (technology), five explicit knowledge types (retail operations, manufacturing, marketplace awareness, assortment management, product development), and one tacit knowledge (professional experience). Based on the prioritized 29 skills and knowledge types, an apparel merchandising competency framework was adapted for the South African retail industry. Out of the 116 individual dimensions only half (58) were determined as essential and included in the final framework. The findings indicate that a competent merchandising professional should have a well-balanced skill and knowledge set to succeed in the apparel retail industry.

Leveraging personalization and customization affordances of virtual try-on apps for a new model in apparel m-shopping

PurposeVirtual try-on apps (VTOs) hold great potential to transform online apparel shopping, yet their acceptance by consumers has been lukewarm. By drawing on two theories – Sirgy’s theory of the self and Sundar’s theory of interactive media effects (TIME) – the study tests a PLS model with two new constructs: Photo Satisfaction and Consumer Inspiration, and their impact on Adoption Intention.Design/methodology/approachThis mixed methods study examines the effects from personalization and customization affordances in the latest generation of apparel VTOs. The proposed model was tested in a quasi-experimental within-subjects design, as 61 female participants completed a goal-directed and an exploratory apparel shopping task using two VTO apps available on the market.FindingsInspiration induced from the customization affordance in the exploratory task influenced Adoption Intention as strongly as Fit Confidence did. For both conditions, users’ satisfaction with their avatar picture had a stronger effect on Perceived Augmentation than the individual trait of Body Esteem. The study also demonstrates how TIME’s Sense of Agency component could help theorize positive consumer responses to mobile marketing technology, which in our study were induced via the app’s Mix and Match feature. Also demonstrated was that Perceived Augmentation could be influenced by body-image affective judgments made during the pre-task VTO avatar creation phase.Practical implicationsVTO’s customization feature “Mix and Match” appears vital for the adoption of such apps for exploratory m-shopping of apparel. Also important is the satisfaction with one's photograph during the avatar-creation phase. We further suggest that apparel retailers work with Augmented Reality developers to include dynamic avatar capability as well as social sharing affordances in VTOs.Social implications60 billion USD worth of returns occur annually in the online apparel retail industry, mostly related to fitting problems, not to mention the larger carbon footprint from the increased transportation. A more wide-spread adoption of VTOs can not only help with these problems but also lighten the traffic in brick and mortar fitting rooms, a potential hazard at this time of pandemic.Originality/valueThis is the first study to examine Consumer Inspiration in the context of online apparel retailing and to demonstrate the importance of Photo Satisfaction during the avatar-creation phase in influencing the subsequent user experience in apparel VTOs.

Evaluating retail efficiency using DEA and AHP: a case in the Turkish apparel retail industry

PurposeThe purpose of this study is to propose a comprehensive efficiency evaluation approach to measure the efficiency of the retailers and to determine the relative importance of a wide variety of apparel retail-specific factors that support efficiency.Design/methodology/approachThe first stage of the proposed approach is the measurement of the relative efficiency of the apparel retailers by using data envelopment analysis (DEA) for identifying the best practice frontier and determination of the improvement ratios. The second stage is the prioritization of the factors that are important in the apparel retail efficiency using analytical hierarchy process (AHP).FindingsThe findings of the study revealed that the quantitative criteria to be considered for efficiency through input minimization and output maximization were “number of employees” and “profit before tax,” respectively, however, focusing on output maximization was found to be more critical. In addition, it was seen that the most important qualitative components of efficiency were satisfied customers, qualified staff and branding.Practical implicationsThe managerial actors in the apparel retail industry can be alerted for new strategic actions to adopt best practices or take precautions for future inefficiencies with the help of the results obtained. The customized hierarchical structure allows determining the priority areas that need to be focused on for increasing the performance of apparel retailers.Originality/valueIn this study, DEA and AHP were combined to build a comprehensive efficiency evaluation approach in the apparel retail industry for the first time in the literature. Thus, both qualitative and quantitative variables that are important in apparel retail efficiency were put forward and evaluated. The originality of the hierarchical structure in AHP was its customization to the apparel retail industry.

An integrated framework for supplier selection and performance evaluation for apparel retail industry

Supplier evaluation, including supplier selection and performance evaluation, has become a critical issue for apparel retailers within the apparel supply chain dealing with severe competition in the global market. An effective evaluation of suppliers requires considering both the quantitative and qualitative performance aspects and integrating the efficiency measurement. In an attempt to address this issue, this study proposes an integrated framework that can handle supplier selection and overall performance evaluation in a comprehensive but feasible manner. The proposed integrated framework customized for apparel retailers combines the current techniques having specific capabilities: data envelopment analysis (DEA), analytical hierarchy process (AHP), and the technique for order preference by similarity to ideal solution (TOPSIS). DEA has been used to make the overall performance evaluation, whereas AHP and TOPSIS have been used in combination to provide the quantitative data required by DEA. The proposed integrated framework was implemented and verified using numerical data gathered from a global apparel retailer. The findings revealed that suppliers can be more successful when they put a balanced effort into improving their performance in regards to the important criteria instead of concentrating on only the most important criterion. Moreover, the overall performance evaluation can be much more convenient and rewarding for apparel retailers in terms of finding out the true potential of suppliers when quantitative and qualitative data and efficiency measurements are taken into account at the same time.

Clarifying the merchandising function: analysis of merchandising roles and responsibilities in the South African apparel retail industry

ABSTRACT The purpose of this study was to examine merchandising roles and responsibilities from the perspective of professionals employed in the South African apparel retail industry. Data were collected through semi-structured, individual in-depth interviews. A total of 16 merchandising professionals were recruited to participate in the study. A phenomenological research design was followed. Atlas.ti software was used to analyze the transcribed interviews. The findings showed that merchandising departments in the South African retail tend to employ professionals in four distinct positions of buyers, planners, product developers, and sourcing coordinators. These merchandising roles, that make up the merchandising function, are essential in delivery products which are profitable and will satisfy consumers needs. The four positions also have distinct responsibilities that are organised according to the product lifecycle. Merchandising professionals rely on frequent daily interactions with each other through shared responsibilities and decision-making for some of the stages of the product lifecycle.

Visual Merchandising Elements: Drivers Of Retail Strategies?

Visual merchandising has increasingly become important in retailing especially with more and more customers opting to go online. This study focused on the consumer in order to obtain a better understanding of the visual merchandising elements that impact on visual merchandising in the South African apparel retail industry. The primary research objective was to identify visual merchandising elements from a retailers and consumers perspective in order to drive more focused retail strategies in store design and planning in the apparel market. This research study followed a mixed-methods approach. The value of the research study lies in its contribution to the development of strategic visual merchandising elements that can be used in the retail strategies of apparel retailers. Incorporating visual merchandising elements can improve visual merchandising proficiency and thus have greater impact on store design and layout, as well as when designing and planning new stores.