The Drivers of Sustainable Apparel and Sportswear Consumption : A Segmented Kano Perspective

The steady increase of sustainable consumer behavior leads companies to strengthen their efforts to become socially and ecologically more sustainable. Particularly in the clothing and footwear industry, more and more companies are aware of their need to fundamentally adapt the way they create value. Sustainability offerings are developed, e.g., usage of upcycled materials (e.g., ocean plastic), circular business models (e.g., decomposition of returned products into components for new ones), as well as adapted product ranges (e.g., smaller or with fewer fashion cycles). However, it is frequently unclear in advance, which offerings will increase (or decrease) satisfaction and, consequently, drive (or not drive) sustainable consumption. The application of a segmented Kano perspective in an apparel and sportswear context that helps to answer these questions is presented: 17 potential offerings were assessed by a sample of 490 consumers. Our analysis demonstrates the usefulness of this methodology and that returning used products (to recycle them), discounts for buying sustainable products, sustainability level indicators, and biobased materials are highly attractive. However, the responsiveness varies across the derived consumer segments, from being decisive or attractive to indifferent or reverse. As assumed, gender and attitude towards sustainability are good predictors for segment membership

The Importance of Sustainability Aspects When Purchasing Online : Comparing Generation X and Generation Z

As research on sustainability orientation across generations is still sparse, we contribute to literature by enriching this research field, focusing on Generation Z (‘Zers’) and X (‘Xers’). Moreover, no other study has analyzed cross-generational differences in the sustainability context by making use of choice experiments, which overcome issues related to (Likert) scale item investigations, and allow respondents to evaluate the trade-off between different purchase factors simultaneously. We thus applied one of the most recent advancements in choice experiments, named Adaptive Choice-Based Conjoint analysis, which appears to be more realistic than previous alternatives. The results indicate Zers consume more sustainably (inter alia higher importance of social labels; higher purchase likelihood) when shopping online; however, differences within each generation were uncovered, especially among Xers (e.g., gender differences regarding importance of price)

Impact of dietary sulfolipid-derived sulfoquinovose on gut microbiota composition and inflammatory status of colitis-prone interleukin-10-deficient mice

The interplay between diet, intestinal microbiota and host is a major factor impacting health. A diet rich in unsaturated fatty acids has been reported to stimulate the growth of Bilophila wadsworthia by increasing the proportion of the sulfonated bile acid taurocholate (TC). The taurine-induced overgrowth of B. wadsworthia promoted the development of colitis in interleukin-10-deficient (IL-10-/-) mice. This study aimed to investigate whether intake of the sulfonates sulfoquinovosyl diacylglycerols (SQDG) with a dietary supplement or their degradation product sulfoquinovose (SQ), stimulate the growth of B. wadsworthia in a similar manner and, thereby, cause intestinal inflammation. Conventional IL-10-/- mice were fed a diet supplemented with the SQDG-rich cyanobacterium Arthrospira platensis (Spirulina). SQ or TC were orally applied to conventional IL-10-/- mice and gnotobiotic IL-10-/- mice harboring a simplified human intestinal microbiota with or without B. wadsworthia. Analyses of inflammatory parameters revealed that none of the sulfonates induced severe colitis, but both, Spirulina and TC, induced expression of pro-inflammatory cytokines in cecal mucosa. Cell numbers of B. wadsworthia decreased almost two orders of magnitude by Spirulina feeding but slightly increased in gnotobiotic SQ and conventional TC mice. Changes in microbiota composition were observed in feces as a result of Spirulina or TC feeding in conventional mice. In conclusion, the dietary sulfonates SQDG and their metabolite SQ did not elicit bacteria-induced intestinal inflammation in IL-10-/- mice and, thus, do not promote colitis

Multiplexed Quantitative Assessment of the Fate of Taurine and Sulfoquinovose in the Intestinal Microbiome

(1) Introduction: Sulfonates, which can be diet- or host-derived, are a class of compounds detected in the gut, are involved in host–microbiome interactions and have several health effects. Our aim was to develop a method to quantify five of the sulfonates in the intestine and apply it in a simplified human microbiome model. These were taurine, its metabolic precursor cysteate and one of its degradation products isethionate, as well as sulfoquinovose and one of its most relevant degradation products 2,3-dihydroxy-1-propanesulfonate. (2) Methods: An extraction and sample preparation method was developed, without the need for derivatization. To detect and quantify the extracted sulfonates, a multiplexed LC-MS/MS-MRM method was established. (3) Results: The accuracy and precision of the method were within GLP-accepted parameters. To apply this method in a pilot study, we spiked either taurine or sulfoquinovose into an in vitro simplified human microbiota model with and without Bilophila wadsworthia, a known sulfonate utilizer. The results revealed that only the culture with B. wadsworthia was able to degrade taurine, with isethionate as an intermediate. After spiking the communities with sulfoquinovose, the results revealed that the simplified human microbiome model was able to degrade sulfoquinovose to 2,3-dihydroxypropane-1-sulfonate, which was probably catalyzed by Escherichia coli. In the community with B. wadsworthia, the 2,3-dihydroxypropane-1-sulfonate produced was further degraded by B. wadsworthia to sulfide. (4) Conclusions: We successfully developed a method for sulfonate quantification and applied it in a first pilot study

Macrophage phenotype in response to ECM bioscaffolds

Macrophage presence and phenotype are critical determinants of the healing response following injury. Downregulation of the pro-inflammatory macrophage phenotype has been associated with the therapeutic use of bioscaffolds composed of extracellular matrix (ECM), but phenotypic characterization of macrophages has typically been limited to small number of non-specific cell surface markers or expressed proteins. The present study determined the response of both primary murine bone marrow derived macrophages (BMDM) and a transformed human mononuclear cell line (THP-1 cells) to degradation products of two different, commonly used ECM bioscaffolds; urinary bladder matrix (UBM-ECM) and small intestinal submucosa (SIS-ECM). Quantified cell responses included gene expression, protein expression, commonly used cell surface markers, and functional assays. Results showed that the phenotype elicited by ECM exposure (MECM) is distinct from both the classically activated IFNγ + LPS phenotype and the alternatively activated IL-4 phenotype. Furthermore, the BMDM and THP-1 macrophages responded differently to identical stimuli, and UBM-ECM and SIS-ECM bioscaffolds induced similar, yet distinct phenotypic profiles. The results of this study not only characterized an MECM phenotype that has anti-inflammatory traits but also showed the risks and challenges of making conclusions about the role of macrophage mediated events without consideration of the source of macrophages and the limitations of individual cell markers

The Impact of Lead Time and Model Selection on the Accuracy of Call Center Arrivals\u27 Forecasts

In call center management, high forecast accuracy of arrivals’ predictions yielded by business analytics techniques is not only essential to enhance perceived service quality by shortening customers’ waiting times, but further to realize cost savings for companies by avoiding overstaffing. Thus, in this regard, methodological improvements are drivers for a company’s success within its competitive environment. Therefore, this research investigates the predictive impact of both lead times and model selection on accuracy. We analyze 174.5 weeks of data from an online retailer’s call center comprising customer support’s call and e-mail arrivals. We compare traditional as well as novel time series and machine learning (ML) models for ten different lead times with cross-validation and a rolling forecast origin. Our findings indicate that accuracy decreases with increasing lead time. Specifically, we prove that the models’ forecast types are impacted differently by lead times: For all lead time extents, regression and ML approaches maintain their accuracy levels with ex-post forecasts. In contrast, time series models’ ex-ante forecasts worsen the longer the lead time, i.e., the further the time series’ values used for prediction lie in the past. Overall, random forest (RF) outperforms the remaining models for almost all considered lead time constellations