How technology and innovations drive personalization in the fashion and textile industry: a systematic literature review and future research agenda

With changing times, customers of the “new economy” have begun to place significant social emphasis on being fashionable (Oran, 2019). 2020 marked the emergence of Generation MZ, comprising Millennials and Generation Z, as the focal point of consumption (Lee and Kim, 2021). The fashion industry has become synonymous with consumerism, and the total apparel market size is expected to reach US$2.6tn by 2025, with a 4% compound annual growth rate (CAGR) growth rate ^[1]. Across industries, we are witnessing an increasing trend of personalization, where customers increasingly seek customized products and services. Volatile market preferences for personalization are driving the prominence of mass customization in many industries, with the strategy being particularly significant in the fashion industry (Yeung and Choi, 2011; Ribeiro et al., 2017; Nobile and Cantoni, 2023). According to Silveira et al. (2001), mass customization refers to the production of customized products or services that satisfy individual needs while maintaining the cost-effectiveness of mass production. Businesses are witnessing rapid technological advancements, which enable them to achieve product customization and cater to diverse consumer preferences.

According to (Christopher et al., 2004), fashion, by definition, encompasses a product or market that has an element of style and is usually considered short-lived. Change is the essence of fashion, and clothing can be understood as a “new look” or “new use.” Fashion can thus be associated with newly designed products or innovative uses of either old or new products, combined (Oran, 2019). In fashion, novelty, though a vital dimension, can be driven by several factors beyond the product’s functional properties, such as design or the experience one gets when interacting with the product or service (Oran, 2019; Kautish et al., 2023). Therefore, for this study, innovation has been operationalized beyond product innovation to include process and service innovations.

There is an increasing body of research in this area, making it essential to take stock of the existing research and provide directions for future studies. Researchers have reviewed literature in the domains of technology adoption in the apparel industry, digitization efforts to promote sustainable practices, or social consumer behavior in the context of secondhand fashion shops, to name a few (Medina and Vázquez, 2025, Glogar et al., 2025). A literature review linking technology with mass customization was explicitly conducted in the context of using 3D and 4D printing for mass customization in the fashion industry (Yu et al., 2022). The current study approaches the domain with a broader lens of innovation, seeking to explore innovation initiatives, with a focus on personalization, across the stages of the apparel life cycle. The objective is also to highlight themes around the enablers for the above innovation initiatives. By following the TCCM approach, the review highlights leading theories used by researchers in the field. The author did not find any previous study attempting to do the same. Henceforward, the systematic literature review endeavors to address the following research questions:

To address the above research questions, the review maps innovations across the apparel life cycle by taking stock of the research in the domain. It also provides insights into the factors that enable achieving the desired outcomes from innovation initiatives for personalization. Theoretical lenses that can be used to explore research in the area have been identified.

The research was conducted following the generally accepted methodology in the peer-reviewed literature (Paul and Rosado-Serrano, 2019). This study used a systematic literature review to investigate the innovations driving personalization in the fashion and textile industries. Upon reviewing the literature, the author noted a scarcity of quantitative studies suitable for conducting a meta-analysis. The limitations of self-citation practices and collaborative research, which run the risk of overstated citation counts, thereby misreporting the apparent importance of individual research, discouraged the researcher from using bibliometric analysis (Aksnes et al., 2019). Moreover, as bibliometric analysis is based on citations, it may advocate well-established authors and journals and, in the process, overlook lesser-known contributors (Waltman, 2016). Finally, the author conducted a systematic literature review to assess the research in the area (Christofi et al., 2021). A comprehensive search string was developed, and the author loaded it into the SCOPUS and EBSCO databases. Because these databases offer extensive coverage of peer-reviewed journal articles from various domains and provide 70% greater access to articles than other online databases, they were used for the study (Brzezinski, 2015).

An advanced search option was used to generate a list of peer-reviewed journal articles published in English till March 2024 in the “Business, Management and Accounting,” “Social Science,” “Computer Science,” and “Decision Science” subject area categories (Klarin and Suseno, 2023). Various search strings were formed using the Boolean “AND” and “OR” operators to avoid missing relevant papers. The search string “technology” OR “technological innovation” OR “innovation” AND “personalization” OR “customization” AND “fashion” OR “fashion industry” OR “textile” OR “fashion” was used. Book chapters, books, short surveys and conference review papers were removed at the time of the search. A total of 517 journal articles were retrieved. After removing duplicate records, a total of 440 journal articles were left.

In the second phase, the author studied the titles, keywords and abstracts of 440 journal articles to assess their applicability for the review exercise. It was found that a couple of articles’ titles and abstracts contained the selection keywords, but the main research objectives were not in line with the domain of the review study. Such articles were highlighted for exclusion. After this exercise, 109 articles were left on the list.

In the third phase, which involved screening and selection, the full text of 109 articles was read. The following criterion was used to exclude irrelevant articles:

• The article should discuss an innovation or technology intervention for personalization or customization.

• The research should be positioned in the fashion or textile industry.

The author conducted this exercise, and articles that did not meet the above criteria were removed. For example, articles discussing innovation or technology intervention but not personalization or customization, and vice versa, were removed. Likewise, research not positioned in the fashion or textile industry was removed. After this exercise, 101 articles were left in the final list of sample studies. Following the suggestion of previous studies, a backward search of the reference list of the articles was carried out. This ensured no relevant article was accidentally missed (Van Giffen et al., 2022). One article was identified during this exercise and added to the main list. Further, two new studies were included in the review. There were 104 articles in the list at the end of this phase. Figure 1 illustrates the selection process followed, as outlined in the PRISMA 2020 guidelines (Page et al., 2021). A data extraction sheet, having information regarding authors, title, name of the journal, abstract, the country of research, context, methodology details, theoretical/conceptual framework, research objectives, significant findings, the innovation initiative taken, the different stages of an apparel life cycle and themes of the study, was prepared to extract data from the final set of articles. This approach ensured a comprehensive coverage of relevant information from each reviewed article. The apparel life cycle, as discussed by Gupta (2011), served as a reference point. This was further refined into eight stages of the apparel life cycle:

1. understanding user requirements;

2. designing and assembling garments;

3. testing and analysis (prototyping);

4. sourcing of fabric;

5. production;

6. marketing and distribution (retail experience);

7. use; and

8. end-of-life were identified for mapping the innovations.

Each innovation was mapped to one or more stages as deemed fit by the researcher. The sheet was then shared with a fashion expert to validate the mapping.

Authors have used the stimulus–organism–response framework, a theory in the field of environmental psychology, to investigate how the introduction of omnichannel shopping has impacted the perception and attitude of fast fashion consumers toward it (Jaengprajak and Chaipoopiratana, 2022). Chin-Min et al. (2013) applied the TRIZ systematic innovation theory to follow specific steps and procedures, suggesting an innovative design for customized handbags after exploring customers’ emotional needs in the Taiwanese market. The theory of reasoned action (Ajzen and Fishbein, 1980), which portrays the intentions of an individual toward a specific behavior, and the technology acceptance model (Davis, 1985), which portrays an individual’s intention to use a system, have been used to investigate the attitude of consumers of women’s ethnic wear toward hyper-personalization using digital clienteling and its impact on purchase intention. Wang and Cho (2012) used the model to examine consumers’ beliefs about attitudes and behavioral intentions toward customized online apparel, with fashion innovativeness serving as the moderator. Jain et al. (2021) used the extended technology-based reasoned action and technology-based services model to predict customers’ intentions to adopt hyper-personalization through digital clienteling. Xue et al. (2020) used consumer behavior theory to investigate consumers’ hedonic and utilitarian motivations for adopting virtual reality in online commerce. Kim (2021) has positioned the study within the technology adoption theory to explore the influence of smart retailing experience on customer satisfaction, examining the roles of perceived quality and perceived risk. The innovation diffusion theory (Rogers, 1995), which outlines the different characteristics of an innovation – relative advantage, compatibility, complexity, trialability and observability – that affect the innovation adoption decision of an individual was used by Tao and Xu (2018) and other researchers to study the perception of consumers and their intention to adopt an innovative retailing format introduced in the context of fashion products. Lawry (2023) has applied activity theory, which outlines how humans use technology to accomplish goal-oriented activities. It proposes a conceptual framework that explores how mobile-mediated services can create phygital luxury experiences, enabling luxury apparel shoppers to achieve their goals. Cho et al. (2023) have applied the uses and gratifications theory to explore the psychological needs and motivations of users engaging in contactless marketing. They have empirically demonstrated that entertainment and trendiness have a positive impact on satisfaction. The theory of facial expression (Hernández-Fernández et al., 2019), which proposes that it is possible to understand the emotions evoked by marketing advertisements by analyzing the consumers’ facial expressions, was used by Micu et al. (2022) to propose a prototype of an onsite customer profiling and hyper-personalization system based on an AI platform. Ten Bhömer et al. (2019) used the human–computer interaction theory to design interactions that use intelligent predictive algorithms to trigger creativity and personalized solutions in fashion. Gautam and Sharma (2017) used the relationship marketing theory to empirically illustrate the significant positive impact of social media marketing and customer relationships on consumers’ purchase intentions.

Table 1 presents prominent theories in the literature exploring innovation and personalization/mass customization in the fashion and textile industries.

In this section, the countries, participants and research setting are discussed.

The geographical spread of publications (Figure 2) shows that the maximum numbers of researchers in the area have taken a global or general perspective (36%). It is not surprising that the country with the highest focus on research in the domain is Europe (29%), followed by the USA (8%). Research contributions from emerging markets such as India, China, Africa and Southeast Asia have been scarce.

A total of 80% of research focuses on fashion apparel, comprising 7% knitwear and 1% hosiery. A total of 22% of research focuses on fabric innovations, and around 2% on dyes. Less than 2% of research has been in the area of fashion accessories.

As evident from Figure 3, the focus of the majority of researchers has been on exploring innovations for personalization in garment design and their assembly (32%), followed by innovations in enhancing the personalized retail experience of fashion consumers (20%). Innovations for personalization in the production stage (16%), in understanding user requirements (12%) and in the use phase (10%) have also caught the attention of the researchers. Research in the stages of testing/prototyping, fabric sourcing and the end-of-life phase has been limited.

The review indicates that fashion firms and consumers have been active research participants. The tilt, however, is toward industry, with most data (around 75%) collected from fashion and textile manufacturing firms, fashion designers, fashion brands, dyeing plants, e-commerce retailers and hosiery manufacturing firms. Among individual respondents, primarily consumers or fashion students, approximately 80% were in the 18–40 age group. Studies exploring customer behavior of participants over 40 are limited (Mckinnon and Istook, 2002; Jaengprajak and Chaipoopiratana, 2022). Only one study has collected data and inputs from participants with disabilities (Paganelli, 2021), and another study from participants with special necessities such as the elderly, the diabetic and the obese (Durá-Gil et al., 2017).

In this section, research articles are assessed based on their research approaches and analytical techniques. As shown in Table 2, approximately 39% of the articles are conceptual or descriptive, while the remainder are empirical. In empirical studies, 45% have used an exploratory or qualitative approach. Inductive case studies have been the most common qualitative method (23%), followed by experiments (17%). Researchers have used focus group interviews and content analysis to present their findings (Peng et al., 2012; Peng and Al-Sayegh, 2014). Simulation has also been used as a methodological tool (Ma et al., 2020). Surveys for data collection and structural equation modeling, as an analytical technique, have been the most common quantitative methods (59%). Other techniques, such as binary logistic regression, correlation analysis, ANOVA and factor analysis, have been used by five other studies.

This section provides a brief overview of how innovations supported by technology are driving personalization across the stages of the apparel life cycle.

Figure 4 illustrates that studies in the sample have explored innovation initiatives across stages of the apparel lifecycle, with a growing focus on designing and assembling garments. The next focus has been on innovation initiatives that give customers a personalized retail experience. A brief of innovations and supporting technologies in the stages of the apparel lifecycle is discussed below:

Artificial intelligence technologies and deep learning algorithms are being developed and used to create a personalized profile of each customer during their physical presence. The profile containing information on the age, gender, emotions, personality and products inspected or bought by the customer can further help businesses segment customers in strategic product campaigns, live product suggestions, evaluation of emotions toward a product, forecasts of sales, a personalized enhancement to store space based on augmented reality and patterns of customer purchasing (Jin and Shin, 2021; Micu et al., 2022). Researchers have examined how co-design software tools enable clients to modify a garment according to their personal style, color, pattern and size (Peterson et al., 2011; Li and Chen, 2018; Perna et al., 2018).

3D digital printing technology is helping businesses customize garments by printing graphics with complex color requirements, and its diffusion patterns were studied by Yu et al. (2022) in the US market. The effects of digital engineering, two-dimensional anthropometry and 3D virtual simulation systems on online shopping and customization have been studied by Li et al. (2023). Choi (2022) has explored the development of 3D dynamic fashion products that can change color, style and patterns using 3D virtual simulation systems. Modularization has enabled the attachment and detachment of different functional and design elements of a garment according to the customer’s tastes and needs (Lee and Kim, 2021), using miniature teeth interlocking panels of interconnected parts (Casas, 2017). Researchers have discussed how computer-aided design technologies can facilitate the automatic, rapid design of patterns in a made-to-measure system (Liu, 2023).

Paganelli (2021) has explored the use of virtual avatars that have the potential to realize better-fit standards, as a vast number of avatar sizes and shapes can be created within all virtual tools. This could benefit people with disabilities, as their needs could be represented via virtual fit models. Online 3D body scanners enable customers to match their body shapes with specific garments (Diaconu et al., 2008a, 2008b). As investigated by Perna et al. (2018), online 3D photorealistic configurators are user-friendly, enabling shoppers to design their products by selecting from existing product styles, customizing materials and adjusting other product details.

Innovations in fabrics in the form of high-performance, functional and intelligent fibers that are stain-resistant, wrinkle-free, waterproof, lightweight, flexible and anti-bacterial, that have been medically tested for toxins or combine electronics with fabric, give customers choices to select and personalize as per requirement (Lee and Kim, 2021; Rouf et al., 2022). Fabric electronics, such as fabric, knitwear and embroidery based on conductive yarn, can react to external stimuli to deliver, produce and save signals within the fabric and are adept at interactive interactions and networking (Lee and Kim, 2021). Ten Bhömer et al. (2019) have proposed intelligent predictive algorithms to trigger creativity and personalization during the fashion design process.

Innovations in the dyeing process enable the production of basic garments in large quantities at a low cost, allowing for personalized dyeing at a later stage (Sartal et al., 2017). Customers can opt for antimicrobial and bio-based dyes based on their preferences (Gulrajani, 2004; Gebhardt et al., 2016).

Additive manufacturing, facilitated by 3D modeling software and 3D scanning techniques, enables the collection of real-time data on garments and accessories. Modifications and alterations can be made to prototypes created using scan data without developing the base model from the beginning (Rouf et al., 2022). Virtual fitting rooms, created using virtual and augmented reality technologies, enable online shoppers to create their 3D shopping avatars by uploading photos and providing body measurements. Shoppers can try on outfits by positioning the garments over their avatars and rotating them 360 degrees (Jin and Shin, 2021).

The authors propose a central order processing system that uses discrete event simulation technology to facilitate demand-driven supply chain collaboration. This system enables all stakeholders to be responsive to the specific demands of the final customer, addressing diverse needs promptly and cost-effectively (Ma et al., 2020). Intelligent and smart logistics, along with virtual supply chains, use advanced technology and computerized data analysis methods to enhance the flow of goods and services from suppliers to consumers, thereby enabling businesses to respond more flexibly to changing environments (Chandrashekar and Schary, 1999; Wilson, 2009).

Like other industries, computer-aided manufacturing, which uses software and computer-controlled machinery, has helped automate garment manufacturing (Liu et al., 2020; Lee, 2021). Technological development that supports production in small batches, addressing the specific requirements of a particular community, also helps reduce waste (Palomo-Lovinski, 2020). Peterson and Ekwall (2007) have examined how technological development in the production of knitted garments, such as the 3D body-forming knitwear machinery, has enabled complete garments to be ready-made directly in the knitting machine, thereby eliminating the processes of cutting and sewing. The short lead time and quick response help to address customer demands. In combination with new materials, modern knitting technology enables the integration of localized functionalities within a garment on a “stitch-by-stitch level” (Ten Bhömer et al., 2019). Innovations in production processes enable the creation of nonwoven fabrics with the desired finish (Frederick, 2004). 3D printing technology has enabled the production of highly customized products using various fabrics, thereby reducing complexities and problems such as high lead times and considerable wastage (Rouf et al., 2022). Manufacturing in real-time fashion systems, secured by blockchain technology, is entirely digital and trackable and guarantees personalized service delivery (Lee, 2021). Durá-Gil et al. (2017) have proposed production equipment and technologies that can accommodate the needs of customers with special necessities, essentially the obese, diabetic and disabled.

Advancements in cloud-based fashion platforms that use augmented and virtual reality enable customers to try on garments and adjust the design and fit in virtual fitting rooms based on their preferences and body type before making a purchase decision. Amazon’s Echo Look service gives online recommendations to customers through photos and short videos, while the Drop service helps fashion influencers in designing garments for Prime members. These services also enable brands to send customized messages to customers and encourage them to share their experiences with others on social media platforms, such as Facebook, Instagram, Pinterest, Twitter, Tumblr and WeChat (Watanabe et al., 2021). Micu et al. (2022) have proposed an AI platform using a deep learning approach to complete customer profiles by gathering data on age, gender, personality, emotions, facial expressions and products purchased or interacted with during physical store visits. The different customer profiles created enable businesses to forecast sales, identify customer purchasing patterns, or offer personalized store enhancements using augmented reality. Community-based service activities, such as content sharing and multi-platform storytelling, and rules-based service activities, such as pseudo-webrooming and pseudo-showrooming, offer the advantage of an exclusive and personalized luxury experience (Lawry, 2023). A fashion subscription service, which includes personalized boxes containing products curated by personal stylists, is an approach in the fashion industry to acquire and retain customers (Tao and Xu, 2018). Big data collected through point-of-sale devices facilitates systematic observation of customer behavior and helps increase retail revenue through effective targeted advertising (Marín et al., 2020).

Innovations in the form of smartphone applications are proposed that interact with customers and offer the option to customize text, patterns, colors, images and animations according to their specific requirements. Fabric electronics, such as fabric, knitwear and embroidery based on conductive yarn, can react to external stimuli to deliver, produce and store signals within the fabric and are adept at interactive interactions and networking (Lee and Kim, 2021). Sensor modules incorporated into accessories and garments are designed to detect various parameters, including heart rate, fitness level, blood pressure, temperature, elevation and humidity (Goveia et al., 2019).

The need to adopt environmentally friendly and socially acceptable practices suggests a reconfiguration of the supply chain that engages customers beyond the point of retail. Designers, manufacturers and retailers strive for experiential retail to help customers obtain what they want and when they need it (Palomo-Lovinski, 2020). Kim and Yim (2022) identified that producing environmentally friendly and easily disposable fabric and promoting the concept of thrift through the sharing economy platform help promote a circular fashion system. Providing a virtual wearing experience to customers prevents rapid disposal and promotes sustainable consumption. Free digital sharing economy platforms enable individuals to buy and sell secondhand apparel (Palomo-Domínguez et al., 2023).

This section highlights five themes that can be identified as enablers for organizations’ efforts to provide personalized products and services to customers through innovation initiatives. The grounded theory approach (Glaser and Strauss, 1968) was used to extract themes from the open codes generated during the literature review. Table 3 maps the contributing articles under each core theme.

Jaengprajak and Chaipoopiratana (2022) found that a customer’s innovativeness, or their willingness to adopt new fashion products and ideas, as well as their perception of the utility and associated risks of a particular intervention, have a significant relationship with their intention to try out a product or service. A customer’s experience in interaction with the firm’s goods or services greatly influences their perception of the innovation initiatives taken by the firm to offer personalized services. Chin-Min et al. (2013) discovered that new-age customers buy not only for “function” but also want positive emotions such as self-confidence and joy from a particular product. Customer attitude and purchase intention toward customized fashion goods are influenced by their self-identity, perception of performance risk, their need for uniqueness and being different from others, and subjective norms (Jain et al., 2018). Xue et al. (2020) explored and proved that customers’ hedonic and utilitarian motives influence the adoption of virtual reality as a means of online shopping. In contrast, perceived ease of use and perceived usefulness/advantage of technology, perceived control and the perceived enjoyment from a particular technology were found to influence customer acceptance of body scanning technology (Peng et al., 2012) and the overall satisfaction of consumers when using innovative retail technologies (Tao and Xu, 2018; Kim, 2021). Subjective norms, or the extent to which a customer is influenced by social norms or what friends and colleagues say about a particular fashion trend, have been shown to influence the adoption intention of technology for hyper-personalization (Kim, 2021). Gen Z consumers are concerned about climate change and various social issues, and brands can accordingly strategize their brand perception (Palomo-Domínguez et al., 2023).

Oran (2019) proposed, on the contrary, that fashion firms have been influencing consumer preferences and manipulating customer choices for their interests.

Chandrashekar and Schary (1999) have proposed an organizational structure that promotes an efficient and effective flow of physical goods and information. Lica et al. (2021) suggested that because of the increased interaction required between the R&D and production units, the co-location of these units in firms may improve their performance. Sargiacomo (2018) and Liu et al. (2020) have proposed new performance management, an integrated data infrastructure and quality information systems to complement innovative accounting practices. Innovations in online IT solutions for offering customized products and services to customers require companies to make tradeoffs concerning advancements in offline procurement and production systems (Perna et al., 2018). This necessitates decisions to invest in technology and skill upgrades to support the modified processes (Ramathal and Sankar, 2005). Gulrajani (2004) has documented the need to use local natural resources effectively to customize and compete in international markets. Yeung and Choi (2011) observed that an organization’s return policy for mass-customized products influences customer behavior. The availability of enabling technologies, such as digital information technology for communication among internal and external stakeholders, as well as specialized technological applications, facilitates mass customization. A customer’s engagement with virtual reality for online shopping was found to be influenced by the convenience and accessibility of the platform, the authenticity and lifelike experience of the virtual commerce environment, the personalization of the interface and the virtual sales assistance provided (Xue et al., 2020; Kim, 2021). Lawry (2023) has proposed that customers of luxury products are driven by status and hedonic goals. Firms can use phygital luxury experiences generated by mobile-mediated services to build community-based content sharing, rules-based approaches such as pseudo webrooming and showrooming and labor-based activities, including the adoption of smart or intelligent display services. Tao and Xu (2018) investigated customers’ adoption of innovative retail offerings. They discovered that customers had varying degrees of knowledge, at times inaccurate, about the novel product, which impacted their decision to adopt it. Fashion firms can, therefore, educate customers about their innovative offerings and how they support personalization. Fashion firms must strike a balance between stylistic and technological innovations. Cappetta et al. (2006) discovered patterns of convergence and divergence in styles adopted by firms.

For interactivity, Choi (2022) found that 3D virtual simulation systems permit customers to engage online in the co-design process with their personalized avatars. As identifying customer needs can be challenging, with many customers unsure of their needs, researchers have proposed engaging customers in the mass customization process (Yeung and Choi, 2011). Liu et al. (2020) found that involving customers early in the design process enables them to make informed decisions about the design details of their customized products, thereby enhancing their overall purchase experience. Customer involvement in the design phase contributes to the intricacies of the product, with each batch of apparel varying by retailer and country (Sargiacomo, 2018). Digital consumers are not merely shoppers; they are digital content creators (Fiore, 2008). Benedetto (2014) has proposed that fashion firms can leverage the ideas generated by a few key users to identify the needs of emerging mainstream customers. Customer relationships were investigated to build trust and foster a sense of bondedness, revealing a strong relationship between the purchase intentions of fashion customers (Gautam and Sharma, 2017).

However, a customer’s willingness and interest in designing personalized solutions is important because an individual’s quality time and resources are required (Kim, 2021).

Researchers have proposed that developing a business relationship network is crucial for fashion companies implementing personalized customer solutions (Perna et al., 2018). A collaborative model integrating horizontal and vertical collaborations among competitor firms, customers and suppliers was proposed for efficient demand-driven textile supply chains (Ma et al., 2020). Ramathal and Sankar (2005) discovered that fashion and apparel firms in Malaysia were creating new industrial linkages to develop clusters. Tredwin (2001) discussed the formation of virtual communities of internet textile-related companies.

The increasing advent and popularity of digital and intelligent consumption patterns have encouraged fashion firms to explore various technologies to meet the customized needs of customers (Li et al., 2023). Accelerated competition from domestic and international players, unpredictable consumer demand, a desire for variety and shorter product life cycles have compelled fashion firms to focus on the personalized needs of customers (Bae and May-Plumlee, 2005).

As the review shows, fashion and textile firms have undertaken innovation initiatives in products, processes and services to drive personalization. Regarding product innovations, firms have explored the use of sustainable and non-woven fabrics, as well as bio-based dyes derived from natural products. (Wilson, 2009; Goveia et al., 2019, etc.). In process innovations, digital printing, computer-aided design, computer-aided manufacturing, component modularity, use of body scanners, etc. are exhibited (Sargiacomo, 2018; Yu et al., 2022; Li et al., 2023). In service innovations, use of immersive technologies, such as augmented reality and virtual reality, to improve the shopping experience is exhibited (Jin and Shin, 2021; Watanabe et al., 2021). These have helped fashion firms reduce the response time to frequently changing customer needs, give luxury experiences, reduce cost, reduce production time and address sustainability concerns, to name a few.

The review highlights the innovation initiatives and supporting technologies across the stages of the apparel life cycle in the fashion and textile industry, as well as how these initiatives have driven personalization and mass customization throughout the stages (Figure 4). As is evident, most research has focused on innovation initiatives and the technology used in the design stage to enhance the retail experience of personalized offerings. Research focusing on customer requirements at the end of the life of a particular apparel is scarce. Research has discussed the concept of thrift (Kim and Yim, 2022). The popularity of this amongst fashion consumers should be investigated further. Additionally, research exploring the need for personalization and customer behavior toward fashion accessories is scarce (Chin-Min et al., 2013; Goveia et al., 2019). The review found limited studies exploring balancing personalization and sustainability efforts (Gulrajani, 2004; Jain et al., 2018; Palomo-Lovinski, 2020). More efforts can be made in this direction in the future.

Innovations at all stages of the apparel life cycle are based on customer data. Consumer privacy concerns were found not to hinder adoption (Peng et al., 2012). This is a stream of research that merits further investigation. Researchers have proposed capturing the emotions of customers and other demographics during their on-site visits to retail stores (Micu et al., 2022). Although firms can effectively use the information, studies focusing on customers’ comfort levels can be conducted. The review demonstrates that changing customer behavior and Gen MZ’s passion for personalized products and services are driving fashion firms to work toward providing them with that experience. The fast progress of technology has enabled their efforts in this direction. Oran (2019) proposed, on the contrary, that fashion firms have been influencing consumer preferences and manipulating customer choices for their interests. Studies exploring fashion as a retailer/designer-driven or a customer-driven industry can be taken up for in-depth exploration.

Studies have highlighted customer attitudes toward the use of technological solutions for personalization offered by companies (Tao and Xu, 2018; Xue et al., 2020). Few studies have explored the importance of personalization for customers and the challenges. Likewise, differences in customer demographics and buying behavior can be studied. As the review illustrates, most research has been conducted from the perspective of fashion or design firms. As customers play a crucial role in the success or failure of various initiatives aimed at personalization or customization, future researchers can focus on the customer’s perspective. The review also demonstrates that most research has focused on consumers in the 18–45 age group. This is not surprising, as the belief is that consumers in this age group are the most receptive to interventions involving new-age technologies (Nielsen, 2016). Studies focusing on the attitude toward personalization efforts of older aged customers also deserve attention. As highlighted in the analysis, mainstream customers have been the primary focus of most researchers, with only two studies involving participants with disabilities or special needs. Studies focusing on non-mainstream customers, such as those belonging to the LGBTQ community or health-care professionals who desire a fashion element in their professional attire, such as scrubs, can be an interesting stream of research. Additionally, further research on fashion customers and firms from emerging markets is warranted.

The importance of collaborative relationships, both horizontal and vertical, in adhering to demand-driven value chains has been investigated by scholars, who have made suggestions for a seamless flow of resources, information and decisions (Walter, 2006; Ma et al., 2020). Collaborations come with their own set of challenges. Very few studies have explored these challenges in the context of innovation initiatives aimed at meeting the personalized needs of customers. Information technology is used to create virtual communities (Tredwin, 2001). Studies can explore the effectiveness of virtual communities in producing the desired personalization outcomes for fashion customers.

Most studies have focused on the customer mindset and the need to educate customers about the benefits of customized products or services. The review identified limited studies (Liu et al., 2020) that discuss the challenges faced by fashion firms in changing the mindset of internal employees to adopt a personalized or customized approach to operations. Engaging customers and involving them as co-designers has been a popular field of research (Liu et al., 2020; Choi, 2022, etc.). Research exploring customer expectations as co-designers is limited. Identifying lead users and building relationships with them to promote the personalization initiatives taken by companies is a field of study that researchers are exploring.

Table 4 presents research gaps in the existing literature and future research directions to address the gaps.

The review aimed to understand the innovation initiatives of fashion and textile firms across stages of the apparel life cycle to provide personalized products and services to customers. The innovations and technologies across the eight stages of the apparel life cycle are mapped. Five enabler-related themes that facilitate the success of these initiatives in delivering the target objective were also identified:

1. customer attitude, behavior and perceptions;

2. complementary organizational systems and processes;

3. customer engagement;

4. supporting ecosystem; and

5. external environment shifts.

In terms of contexts, although studies can be found across the stages of the apparel life cycle, most have focused on innovation and technology interventions in the design and retail experience stages. In retail experience, immersive technologies such as augmented and virtual reality, entertainment, storytelling, etc. received attention. The review also provides a glimpse of “garments of the future” that use smart and intelligent fibers and fabrics, which are adjustable during use.

The study has some limitations. First, limited search engines have been used to extract articles. There is a possibility that some relevant articles will be missed. Second, the review has considered only published research papers. Books, conference proceedings, etc. have not been considered. Third, the search was limited to a few keywords mentioned in the methodology section. More keywords might have yielded additional papers. Finally, the search was conducted in March 2024. Research published after that would thus have been missed out.

The author acknowledge the inputs given by the reviewers to improve the quality of the paper.