Search results

The purpose of this paper is to identify the realistic trade-offs young consumers make when purchasing organic T-shirts.

A full profile discrete choice design was used. The data were analysed using a multinomial logit model and desirability indices.

Price was the most important attribute to consumers followed by the place of production and then sustainability. Consumers were most willing to purchase T-shirts that are eco-friendly, Made In America, made from wrinkle-free technology and cotton jersey knit fabric, and have a price of $15. Although consumers were most willing to pay $15, some were still willing to pay $25 or even $35 for the same eco-friendly T-shirt.

Consumers in the current study were more willing to purchase eco-friendly as opposed to organic apparel. The findings suggest that retailers need to consider the language used when communicating with consumers. Also, consumers were more willing to purchase T-shirts Made In America. Retailers may want to promote their domestic manufacturing through in-depth branding and promotions.

In order to identify the attributes to be used in the current study, labels on T-shirts in stores were examined and then those attributes were verified in the literature. In addition, the inclusion of price as an attribute, rather than as a separate independent question, provides a more realistic view of young consumers’ decision making.

The purpose of this paper is to explore and examine the relative impact of product and channel attributes on luxury product shopping in the multi-channel environment.

A D-optimal discrete choice conjoint design was used. The data were analysed using a multinomial logit model and desirability indices.

Findings indicate that low price was the most important factor that influenced young consumers' preferences. After price, young luxury consumers placed a greater importance on channel attributes such as human-assisted service and virtual fitting rooms.

The sample consisted of young consumers in their 20s and 30s, who utilise both online and offline channels. Hence, the income level was relatively low. Also, the results cannot be generalised to all luxury consumers.

Providing preferable channel attributes is more crucial to young luxury fashion shoppers than focusing on improving product attributes, with the exception of price.

The paper proposes the optimal combination of key product and channel attributes that is most preferable to young luxury fashion consumers in the multi-channel environment.

The purpose of this study is to identify US textile and apparel (TAP) products and categories that demonstrate export comparative advantage and their respective country destinations.

Revealed comparative advantage (RCA) and normalized revealed comparative advantage (NRCA) in the long term (1996-2016) and short term (2010-2016) using two-digit Harmonized Commodity Description and Coding System codes (HS codes) provide an insight into export advantage of TAP products. Non-parametric rank correlation (i.e. Spearman rank correlation) tests consistency between RCA and NRCA. Subsequently, NRCA using four-digit HS codes identify textile sub-categories with export comparative advantage. Bilateral trade data identify export destinations for sub-categories that indicate US export comparative advantage in the short term.

Aggregated product-level comparison of RCA and NRCA ranking indicates a consistent positive correlation between the two indices. The disaggregated findings reveal cotton fiber HS5201, artificial filament tow HS5502, non-wovens HS5603, cotton yarn HS5205, carpet and other floor coverings HS5703 and worn clothing HS6309 as sources of US TAP export advantage.

This research provides a comprehensive, current analysis of the US competitive position within the TAP global environment. Incorporation of NRCA into the study’s design demonstrates the flexibility of this measure in a new industry context.

Implementing new quality initiatives in organizations is challenging, as it requires managers and employees to adjust to new processes, methodologies and even mindsets. The purpose of this study is to investigate the relationship between quality management (QM) practices and readiness for change due to implementing new quality initiatives such as lean, six sigma and to determine which dimensions of QM are more important to change efficacy and change commitment.

The issues are examined in this study through the analysis of survey data obtained from US textile and apparel industry managers by using factor analysis, stepwise regression to construct path model and structural equation modeling.

This study identifies change readiness measures which are specific for quality implementations and establishes two constructs, namely, change commitment and change efficacy. The results indicate that as good Employee Relations increase, the level of organizational change commitment increases. If employees are engaged and empowered to provide excellent quality, then it is more likely that they will be committed to change due to implementing future quality initiatives. Moreover, a direct relationship between Customer Relationship Management and change efficacy is found. Companies with a strong customer orientation are more able to implement the quality initiatives that matter to their markets.

This study is unique in investigating the empirical relationship between QM practices and the dimensions of readiness for change due to implementing new quality initiatives via data from various organizations. This study empirically contributes to the QM literature with change readiness antecedents in quality implementation setting.

The purpose of this paper is to identify the realistic trade-offs young consumers make when evaluating luxury co-branding combinations based on signalling theory.

Conjoint analysis was employed to evaluate the relative impact of four major attributes (i.e. brand combinations, retail channels, uniqueness and price) on consumer desirability for luxury co-branding combinations. The data were analysed using desirability indices.

Brand combinations, uniqueness and price significantly impact consumer desirability of luxury co-branding combinations. The luxury brand and sportswear combination results in the highest desirability when price is more similar to the sportswear constituent and participants perceive that the collaboration as exclusive.

The results suggest that luxury brands need to consider the partnering brand's retail format primarily for co-branding strategy. Luxury brand collaborations with sportswear and premium priced streetwear brands are more likely to result in higher desirability among consumers compared to collaborations with fast fashion and mass-market brands. Additionally, uniqueness may not be effective as a point of differentiation in cases where luxury brands cannot guarantee a single yearly collaboration.

The decision to use existing brands for the fictitious combinations developed more sensible scenarios for respondents. In addition, rather than discrete questions, attribute-based combinations provide a more realistic depiction of consumers' decision making on luxury co-branding. Finally, the results provide marketing practitioners with practical directions for future development of fashion luxury co-branding strategy.

This study explored the role that Instagram, a prominent social media platform, plays in the consumer decision-making process of young adults (age 18–27 years) in the United States as they shop online for apparel and interact with the digital profiles of apparel brands. Specifically, this study compared consumers' purchase and post-purchase evaluation behaviors on Instagram between Millennials and Generation Z, and investigated the role of consumers' trust of Instagram on these behaviors.

A survey instrument was developed to explore critical variables, including consumers' purchase and post-purchase evaluation behaviors on Instagram, consumers' trust of Instagram, and demographic factors, adapting previous works from Ko (2018), Djafarova and Rushworth (2017), and Herrando et al. (2019). Survey responses were collected via the Qualtrics participant panel service (N = 226), then coded and analyzed using JMP Pro (Version 14.2).

Results indicated that statistically significant mean differences were present among several key variables, including between Millennial and Generation Z post-purchase behaviors on Instagram, willingness to share feedback about a brand experience on Instagram, and trust of content on Instagram. Additionally, it was determined that purchase and post-purchase behaviors on Instagram are significantly different depending on whether or not a consumer trusts Instagram and the content therein.

While online consumer behaviors have been previously studied, the impact of young adults' Instagram usage on their purchase and post-purchase evaluation behaviors in the apparel sector had not been thoroughly examined. The findings from this study provide meaningful insights on the purchase and post-purchase behaviors on Instagram of young adult consumers from different generational cohorts. They demonstrate that developing trust with young adults is critical to the success of brands trying to reach this demographic on Instagram.

The purpose of this study is to explore Lean Six Sigma (LSS) project and program success in the textile and apparel industry. This paper presents depictions of LSS implementations and the resulting economic impact for a textile company.

A qualitative approach consisting of a single descriptive case study and project document reviews was used. This method provided an in-depth view into what LSS means for one medium-sized global textile company and how it has proven important to the company’s success.

Three successful projects, two on changeover time reduction and one on metal contamination, were completed. Additional findings from this study suggest that strategic partnerships with other high-performing companies and storytelling are two critical success factors. Also, it is critical for management to convey a clear vision for LSS that can be operationalized within a company for successful deployment of LSS textile projects.

The findings from this case study cannot be generalized.

The literature on LSS in small- and medium-sized businesses is limited. The literature on the use of LSS in the textile and apparel industry is even more limited. This paper shows various processes within the textile complex where LSS has been deployed successfully, yielding economic impacts. By using qualitative methods, the value of strategic partnerships, storytelling and a vision was seen.

The purpose of this paper is to examine the relationship between contingency factors and reshoring drivers in the US textile and apparel industry.

Secondary data on the reshoring drivers and contingency factors for 140 US textile and apparel companies are analyzed using analysis of proportions.

The findings show that total annual revenue is significantly related to the reshoring driver of skilled workforce. No significant relationships are present between reshoring drivers and the region of the world reshored from not the region of the USA from which a company operates. There is a significant relationship between market segment and the reshoring driver of manufacturing process. The US production category (reshored, FDI, or kept from offshoring) exhibits a significant relationship with sustainability-related and cost-related reshoring drivers. Quality is a significant driver for reshoring from 2010 to 2016, although decreasing as a reported reason over that time period.

Limitations include a focus on one industry, the lack of information to investigate the differences between companies making captive or outsourced reshoring decisions, and the use of companies who publicly announced reshoring.

This study outlines the relationships between contingency factors and reshoring drivers. The results provide companies with information about resources that will be demand (e.g. skilled workers) as well as policies and regulations that may be developed to address concerns such as sustainability.

This study adds to the limited number of studies on the relationships between contingency factors and reshoring drivers and contributes to the quantitative research on reshoring drivers.

The purpose of this study is to examine why Sub-Saharan Africa (SSA) is not currently a major player in producing apparel for the US market and determine if SSA is likely to become one because of several opportunities that the region offers, including relatively low labor wages, an ample labor force and duty-free access to the USA through the 10-year renewal of AGOA.

In-depth interviews were conducted with eight high-level executives in apparel sourcing and trade agencies to obtain their views on the opportunities and challenges of sourcing in SSA in relation to other major apparel sourcing regions. A descriptive analysis of the qualitative data was used to answer three research questions.

The findings reveal that SSA is a competitive region to source from, because of low labor wages and the duty-free benefits through AGOA. However, several challenges hinder a significant increase in sourcing from SSA. The executives recommended that for SSA to be a significant force in the global apparel market, vertical supply chains should be developed, where raw materials like fabric are sourced from within the country/region and SSA governments should become more involved in business environment improvements.

At present, there is limited academic literature on sourcing and supplier selection in Africa, particularly in textile and apparel sourcing in SSA. Based on interviews from high-level executives engaged in the sourcing decision-making process, this study reveals the benefits, challenges and opportunities for sourcing apparel from SSA countries.

With the growth of unstructured data, opportunities to generate insights into supply chain risks in low cost countries (LCCs) are emerging. Sourcing risk has primarily focused on short-term mitigation. This paper aims to offer an approach that uses newsfeed data to assess regional supply base risk in LCC’s for the apparel sector, which managers can use to plan for future risk on a long-term planning horizon.

This paper demonstrates that the bulk of supplier risk assessments focus on short-term responses to disruptions in developed countries, revealing a gap in assessments of long-term risks for supply base expansion in LCCs. This paper develops an approach for predicting and planning for long-term supply base risk in LCC’s to address this shortfall. A machine-based learning algorithm is developed that uses the analysis of competing hypotheses heuristic to convert data from multiple news feeds into numerical risk scores and visual maps of supply chain risk. This paper demonstrates the approach by converting large amounts of unstructured data into two measures, risk impact and risk probability, leading to visualization of country-level supply base risks for a global apparel company.

This paper produced probability and impact scores for 23 distinct supply base risks across 10 countries in the apparel sector. The results suggest that the most significant long-term risks of supply disruption for apparel in LCC’s are human resource regulatory risks, workplace issues, inflation costs, safety violations and social welfare violations. The results suggest that apparel brands seeking suppliers in the regions of Cambodia, India, Bangladesh, Brazil and Vietnam should be aware of the significant risks in these regions that may require mitigative action.

This approach establishes a novel approach for objectively projecting future global sourcing risk, and yields visually mapped outcomes that can be applied in forecasting and planning for future risks when considering sourcing locations in LCC’s.