Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.

there are still knowledge gaps in how management should facilitate effective lean learning processes and behaviors in hospitals. Evidence suggests that healthcare administrators and managers may be unprepared to provide their employees with the learning and experience necessary to develop a lean culture and mindset.The intent of this research is to examine the learning and behavioral changes of frontline professionals during a collaborative implementation of the Toyota Production System, or lean methodology, at three hospitals. More specifically, our research question is, "What learning and behavioral changes occur in hospitals in the early stages of implementing lean?" In order to shed light on this question, both qualitative and quantitative research methods were employed at the end of years one and two of the multi-year implementation effort. The collected data came from a survey instrument, direct observation, unstructured and semi-structured interviews, and focus groups. By studying three different hospitals, our results provide insights and guidelines for facilitating lean thinking and behaviors during the early stages of lean implementation.The article is structured as follows. A review is presented of the concepts of single-loop and double-loop learning, which are the underpinning of this research and critical to understanding lean learning. Following this discussion, we describe the design of the study and the types of data collected at the hospitals. The next section presents the steps taken to analyze the data as well as an interpretation of the statistical results. We conclude the article with a detailed discussion that includes recommendations for practitioners, limitations of our research, and suggestions for future work. Background InformationIn the context of lean, learning can be thought of as "the detection and correction of error" (Argyris and Schon, 1978), where error is anything that inhibits healthcare professionals from taking effective action on the job. The successful implementation of lean requires employees to be effective problem solvers and learners, thereby eliminating errors and making operating improvements. In addition, effective problem solvers must be motivated to improve. This in turn requires self-reflective and self-reactive capabilities that empower them to develop personal standards that motivate their process improvement behaviors (Bandura, 1990;Bandura, 2002). To study learning and behavioral changes we used the single-loop and double-loop learning models proposed by Argyris and Schon (1978). Both models and their organizational consequences are explored in detail.

Purpose The purpose of this paper is to identify the realistic trade-offs young consumers make when purchasing organic T-shirts. Design/methodology/approach A full profile discrete choice design was used. The data were analysed using a multinomial logit model and desirability indices. Findings 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. Practical implications 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. Originality/value 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.

Purpose The purpose of this paper is to examine the relationship between contingency factors and reshoring drivers in the US textile and apparel industry. Design/methodology/approach Secondary data on the reshoring drivers and contingency factors for 140 US textile and apparel companies are analyzed using analysis of proportions. Findings 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. Research limitations/implications 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. Practical implications 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. Originality/value 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.

Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.