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The purpose of this research is to determine if a consumer’s demographics, perceived consumer effectiveness, and awareness of fair trade practices affect their level of hedonic and utilitarian motivations that ultimately influence their purchase intentions. Two separate studies were conducted: a focus group and a survey. Middle-aged and older respondents were more motivated to buy fair trade clothing by both hedonic and utilitarian motivations than younger respondents. Respondents with only some college or an Associate’s degree are more hedonically motivated than other education levels. Respondents with a high household income are more motivated by their hedonic and utilitarian motivations than respondents with a low income. Respondents with high PCE responded more to their hedonic and utilitarian motivations to buy fair trade clothing than consumers with low PCE. Respondents that see promotional campaigns for fair trade clothing are overall less motivated to buy fair trade clothing than respondents that do not. Respondents that feel that people could make fairer choices if they were aware of which companies had high ethical principles are overall more motivated than respondents that feel people could not. As a respondent’s buying intention grew, the respondent grew more motivated in both hedonic and utilitarian categories.

Inom området hållbarhetsledning så finns det många verktyg som har utvecklats för att hjälpa företag att adressera och hantera olika delar av hållbarhet. Mängden verktyg som finns tillgängliga innebär att det är en utmaning i sig att välja det bästa möjliga verktyget för ett givet företag. För små och medelstora företag finns ytterligare svårigheter inom de flesta delar av hållbarhetsledning, framför allt på grund av begränsade resurser. En av dessa utmaningar är att hitta och implementera lämpliga och användbara hållbarhetsledningsverktyg. Många hållbarhetsledningsverktyg fokuserar på olika områden av hållbarhet, indelade i indikatorer. Olika industrier har olika verktyg som är utvecklade för och passar dem, och lägger olika vikt vid indikatorer baserat på vilken påverkan industrin har inom hållbarhetsområdet. Utöver detta har företag olika krav på hållbarhetsledningsverktyg, baserat på deras unika omständigheter, påverkan, resurser och existerade hållbarhetsarbete. En metod för att hantera valet av verktyg är därför att matcha verktyg och företag baserat på indikatorer och faktorerna beskrivna ovan. Detta projekt försöker förenkla processen att välja verktyg genom att skapa en strukturerad modell för urvalsprocessen, baserad på hållbarhetsindikatorer, och att testa den genom att skapa en verktygslåda med kombinationer av verktyg för två fallstudieföretag. Verktygslådan framtagen i projektet innehåller verktyg företaget använder i nuläget, verktyg som föreslås som komplement, och verktyg som är onödiga att fokusera på då använda verktyg täcker ekvivalenta indikatorer. I det här projektet utfördes två jämförande kartläggningar av verktyg och indikatorer för att testa modellen. Detta baserades på information om verktyg och vilka indikatorer de täcker, tillsammans med information om nuläge och hållbarhetsarbete hos två medelstora svenska företag. De båda företagen är Houdini, som arbetar med produktion och försäljning av kläder, och Jämtkraft, från energisektorn. Den utvecklade modellen kunde användas för att generera förslag på verktygslådor för de båda fallen. Dock visade dessa verktygslådor även modellens begränsningar och brister, såsom exkluderingen av verktyg som inte använder indikatorer, och antagandena att verktygen passar medelstora företag lika bra och producerar lika bra resultat om de täcker samma indikatorer. Projektets slutsats är därför att det är möjligt att använda den här modellen för att välja verktyg baserat på indikatorer, men dess användning begränsas av vad som exkluderas. Dock bör fortsatt vidareutveckling av modellen kunna lösa dessa problem. / Within sustainability management, there are several sustainability management tools which have been developed to help companies address disparate aspects of sustainability. This multitude of options means that selecting the best possible tool for companies is a challenge in and of itself. In the case of small- and medium sized companies, there are additional difficulties in regard to most aspects of sustainability management, primarily due to a lack of resources. Among these challenges are finding and implementing suitable and useful sustainability management tools. This project attempts to simplify the tool selection process, by creating a structured tool selection model based on sustainability key performance indicators. A large number of sustainability management tools are focused on different areas of sustainability, often divided into some form of indicators. Different industries have different tools available and suitable for them, and different priorities among sustainability performance indicators, based on which impacts and effects the industry has on sustainability. Individual companies have differing requirements on sustainability management tools as well, based on unique circumstances, impacts, available resources, and existing sustainability measures. One way to handle tool selection is thus to match tools to companies, based on indicators and the factors described above. The intention for this project is to create a model for simplifying tool selection, and to test it by creating suggested combination of tools, a “toolbox”, to use for two case companies. The toolbox includes which tools are currently used, suggestions for which additional tools should be implemented to cover indicator gaps, and which tools are unnecessary to focus on, since used tools cover equivalent indicators. In this project, two mappings of tools and indicators were performed to test the model. This was based on information gathered about the tools and which indicators they cover, combined with information about the conditions and sustainability work from two Swedish medium sized companies. The selected companies were Houdini, from the Swedish clothing retail industry, and Jämtkraft, from the Swedish energy sector. The model can be used to generate toolbox suggestion for these cases. However, there are several limitations to this model, such as the exclusion of non-indicator focused tools, and the assumptions that these sustainability tools produce equal results and are useful for medium sized companies. The conclusion is reached that a decision-making process based on indicators is possible, but there are several important considerations not included within this specific model which limits its potential use. Further development of this model can be done to remedy these shortcomings.

The clothing retail industry has developed globally and is today one of the world’s largest industries. In recent years, Sweden has developed a competitive fashion industry, where the new trend in e-commerce is clear. Since e-commerce is a suitable strategy for rapid growth, it is especially important for managers to understand the impact e-commerce has on their businesses. To be able to have the ability to survive in the long-term industrial competitiveness, firms have to continuously improve their knowledge base about the factors that must be considered when choosing sales channels in the clothing retail industry. In this master thesis we have studied and analysed factors that must be considered when choosing sales channels. A literature review was done, where eleven initial factors that affect the choice of sales channels were identified. Thereafter, to conduct a comprehensive empirical study, we conducted eight case studies with semi-structured interviews with companies in the Swedish clothing retail industry, founded no longer than approximately a decade ago to cover the recent developments in the industry. The empirical study resulted in that six factors from the literature were reinforced and that four new factors were identified. These ten final factors were thereafter analysed from two main perspectives: company focus and consumer focus. Finally, the impact of the final factors on the three different sales channels; online, brick-and-mortar and retailing, was discussed. The ten final factors are: Marketing and Customer Loyalty, High Costs, Customer Behaviour, Outsourcing or Insourcing, Managerial Productivity, Intangibility and Tangibility, Fill a Gap, Customer Experience Control, Spread of Distribution and Type of Assortment. Our contribution to previous research is that this study has a more distinct company focus, and due to the development of e-commerce this study has captured the new characteristics of the industry.

This paper serves to address the effect of time on the sales of clothing retail, from 2010 to May 2019. The data was retrieved from the US Census, where N=113 observations were used, which were plotted to observe their trends. Once outliers and transformations were performed, the best model was fit, and diagnostic review occurred. Inspections for seasonality and forecasting was also conducted. The final model came out to be an ARIMA (2,0,1). Slight seasonality was present, but not enough to drastically influence the trends. Our results serve to highlight the economic growth of clothing retail sales for the past 8 years, cementing the significance of the production economy's stability. The quarterly GDP data was collected in order to find out the relationship with the differenced clothing data. Some observations of GDP data were affected by the clothing data before removing the seasonality. After removing the seasonality, the clothing expense is white noise and not predictable from the historical GDP.