Wat is de rol van prijs in duurzaam consumentengedrag? ‘Omdat het te duur is’ is een veelgehoord argument wanneer mensen wordt gevraagd waarom ze geen duurzame producten kopen. Maar is dat werkelijk zo, of is dit een makkelijk alibi van mensen om niet hun gedrag te hoeven veranderen? We zijn over het algemeen niet bepaald armlastig in de westerse wereld, dus is het werkelijk een gebrek aan geld, of is er een andere oorzaak van dit gedrag? Om daar een uitspraak over te kunnen doen, hebben we de literatuur onderzocht op de relatie tussen prijs en duurzaamheid. Overall conclusie: een bepaalde groep mensen geeft in onderzoeken aan best bereid te zijn om meer te betalen voor duurzamere oplossingen, tot wel 29%. Maar sociale wenselijkheid speelt daarbij waarschijnlijk een grote rol. Want gezien het nog geringe marktaandeel van duurzame producten is de realiteit weerbarstiger. De meerderheid van de mensen is kennelijk nog niet zodanig overtuigd van de meerwaarde dat ze er ook extra geld voor over hebben. Dit document is opgedeeld in twee secties: 1. sectie 1 beschrijft een analytische beschouwing van de literatuur. Dit onderzoek schetst de ontwikkeling van de artikelen die tot dusverre gepubliceerd zijn over bereidheid van consumenten om een meerprijs te betalen voor duurzame producten; 2. sectie 2 beschrijft een inhoudelijke beschouwing van een selectie van de literatuur: specifiek artikelen die (experimenteel) onderzoek beschrijven naar de willingness to pay voor duurzame producten en diensten.
Retail industry consists of the establishment of selling consumer goods (i.e. technology, pharmaceuticals, food and beverages, apparels and accessories, home improvement etc.) and services (i.e. specialty and movies) to customers through multiple channels of distribution including both the traditional brickand-mortar and online retailing. Managing corporate reputation of retail companies is crucial as it has many advantages, for instance, it has been proven to impact generated revenues (Wang et al., 2016). But, in order to be able to manage corporate reputation, one has to be able to measure it, or, nowadays even better, listen to relevant social signals that are out there on the public web. One of the most extensive and widely used frameworks for measuring corporate reputation is through conducting elaborated surveys with respective stakeholders (Fombrun et al., 2015). This approach is valuable but deemed to be laborious and resource-heavy and will not allow to generate automatic alerts and quick and live insights that are extremely needed in this era of internet. For these purposes a social listening approach is needed that can be tailored to online data such as consumer reviews as the main data source. Online review datasets are a form of electronic Word-of-Mouth (WOM) that, when a data source is picked that is relevant to retail, commonly contain relevant information about customers’ perceptions regarding products (Pookulangara, 2011) and that are massively available. The algorithm that we have built in our application provides retailers with reputation scores for all variables that are deemed to be relevant to retail in the model of Fombrun et al. (2015). Examples of such variables for products and services are high quality, good value, stands behind, and meets customer needs. We propose a new set of subvariables with which these variables can be operationalized for retail in particular. Scores are being calculated using proportions of positive opinion pairs such as <fast, delivery> or <rude, staff> that have been designed per variable. With these important insights extracted, companies can act accordingly and proceed to improve their corporate reputation. It is important to emphasize that, once the design is complete and implemented, all processing can be performed completely automatic and unsupervised. The application makes use of a state of the art aspect-based sentiment analysis (ABSA) framework because of ABSA’s ability to generate sentiment scores for all relevant variables and aspects. Since most online data is in open form and we deliberately want to avoid labelling any data by human experts, the unsupervised aspectator algorithm has been picked. It employs a lexicon to calculate sentiment scores and uses syntactic dependency paths to discover candidate aspects (Bancken et al., 2014). We have applied our approach to a large number of online review datasets that we sampled from a list of 50 top global retailers according to National Retail Federation (2020), including both offline and online operation, and that we scraped from trustpilot, a public website that is well-known to retailers. The algorithm has carefully been evaluated by manually annotating a randomly sampled subset of the datasets for validation purposes by two independent annotators. The Kappa’s score on this subset was 80%.
MULTIFILE
Background: Multimodal prehabilitation programs are effective at reducing complications after colorectal surgery in patients with a high risk of postoperative complications due to low aerobic capacity and/or malnutrition. However, high implementation fidelity is needed to achieve these effects in real-life practice. This study aimed to investigate the implementation fidelity of an evidence-based prehabilitation program in the real-life context of a Dutch regional hospital.Methods: In this observational cohort study with multiple case analyses, all patients who underwent colorectal surgery from January 2023 to June 2023 were enrolled. Patients meeting the criteria for low aerobic capacity or malnutrition were advised to participate in a prehabilitation program. According to recent scientific insights and the local care context, this program consisted of four exercise modalities and three nutrition modalities. Implementation fidelity was investigated by evaluating: (1) coverage (participation rate), (2) duration (number of days between the start of prehabilitation and surgery), (3) content (delivery of prescribed intervention modalities), and (4) frequency (attendance of sessions and compliance with prescribed parameters). An aggregated percentage of content and frequency was calculated to determine overall adherence.Results: Fifty-eight patients intended to follow the prehabilitation care pathway, of which 41 performed a preoperative risk assessment (coverage 80%). Ten patients (24%) were identified as high-risk and participated in the prehabilitation program (duration of 33-84 days). Adherence was high (84-100%) in five and moderate (72-73%) in two patients. Adherence was remarkably low (25%, 53%, 54%) in three patients who struggled to execute the prehabilitation program due to multiple physical and cognitive impairments.Conclusion: Implementation fidelity of an evidence-based multimodal prehabilitation program for high-risk patients preparing for colorectal surgery in real-life practice was moderate because adherence was high for most patients, but low for some patients. Patients with low adherence had multiple impairments, with consequences for their preparation for surgery. For healthcare professionals, it is recommended to pay attention to high-risk patients with multiple impairments and further personalize the prehabilitation program. More knowledge about identifying and treating high-risk patients is needed to provide evidence-based recommendations and to obtain higher effectiveness.
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