["itemContainer",{"xmlns:xsi":"http://www.w3.org/2001/XMLSchema-instance","xsi:schemaLocation":"http://omeka.org/schemas/omeka-xml/v5 http://omeka.org/schemas/omeka-xml/v5/omeka-xml-5-0.xsd","uri":"https://johnntowse.com/LUSTRE/items/browse?collection=10&output=omeka-json&page=2","accessDate":"2026-05-22T23:10:36+00:00"},["miscellaneousContainer",["pagination",["pageNumber","2"],["perPage","10"],["totalResults","12"]]],["item",{"itemId":"94","public":"1","featured":"0"},["fileContainer",["file",{"fileId":"58"},["src","https://johnntowse.com/LUSTRE/files/original/fc9393b7c1a7e8247d30912b84f5e064.doc"],["authentication","eeb47f23b04a98a2d170905e85601a89"]]],["collection",{"collectionId":"10"},["elementSetContainer",["elementSet",{"elementSetId":"1"},["name","Dublin Core"],["description","The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/."],["elementContainer",["element",{"elementId":"50"},["name","Title"],["description","A name given to the resource"],["elementTextContainer",["elementText",{"elementTextId":"819"},["text","Interviews"]]]]]]]],["elementSetContainer",["elementSet",{"elementSetId":"1"},["name","Dublin Core"],["description","The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/."],["elementContainer",["element",{"elementId":"50"},["name","Title"],["description","A name given to the resource"],["elementTextContainer",["elementText",{"elementTextId":"2156"},["text","School Dropout in Developing Countries: The Case of Indigenous Communities in Guatemala"]]]],["element",{"elementId":"39"},["name","Creator"],["description","An entity primarily responsible for making the resource"],["elementTextContainer",["elementText",{"elementTextId":"2157"},["text","Patricia Gómez-Luengo"]]]],["element",{"elementId":"40"},["name","Date"],["description","A point or period of time associated with an event in the lifecycle of the resource"],["elementTextContainer",["elementText",{"elementTextId":"2158"},["text","2017"]]]],["element",{"elementId":"41"},["name","Description"],["description","An account of the resource"],["elementTextContainer",["elementText",{"elementTextId":"2159"},["text","Education is a basic right which any person should have the opportunity to access. However, around 120 million children worldwide are as yet uneducated. A qualitative study was conducted in an indigenous community in rural Guatemala in order to identify the key factors that discourage students to continue from primary to secondary school education. The participants of the study were divided into two groups depending on whether they were students, parents of students or teachers at the rural school. Results suggested that factors of different natures (structural, political and cultural) overlap each other. The factors related to school dropout were related to demography, health, lack of economic resources and Government support, lack of social support and lack of intrinsic motivation to graduate from formal education. In contrast, protection factors to remain at school were related to future aspirations and social mobility, parental support and economic support."]]]],["element",{"elementId":"49"},["name","Subject"],["description","The topic of the resource"],["elementTextContainer",["elementText",{"elementTextId":"2160"},["text","Qualitative\r\nSemi-structured interviews\r\nThematic Analyses"]]]],["element",{"elementId":"48"},["name","Source"],["description","A related resource from which the described resource is derived"],["elementTextContainer",["elementText",{"elementTextId":"2161"},["text","Data Collection\r\nData was collected using qualitative, semi-structured interviews to facilitate the discovery of new conceptual and theoretical knowledge (Power & Alison, 2017) about the factors that take place in the process regarding the decision of remaining or disengaging from education. This method has been used regularly by researchers who seek to generate an understanding of real-world psychology (Crandall, Klein, & Hoffman, 2006; Klein & Militello, 2004). The interview sought to achieve a better understanding of the reality that the people interviewed are living through analysing their personal experience.\r\nThe researcher who conducted these interviews spent 2 weeks prior to data collection immersing herself in the work environment of the school in the rural area of Guatemala, reading previous literature on the topic, attending school and classes with the children and interacting with teachers, students and families in order to establish a working relationship with each group.\r\nProcedure\r\nEach interview lasted approximately half an hour, depending on the participants will, in order to prevent participant exhaustion (M = 35). The interview stopped if the participant was perceived to be uncomfortable. Participants were sat in a quiet location and interviewed on a mobile phone. Interviews were semi-structured and included topics such as the characteristic of the educational system, the practices and processes that appear as determinants of the decision for permanently remaining or disengaging from the formal educational process, and personal situations or of the people that the participants knew. Interviews were transferred to a computer and anonymously transcribed, with all identifiable details (e.g., names, locations) removed, for its analysis. Thematic analysis was conducted in order to understand the subjective experience of the participants as the paramount object of the study throughout the organization and reach description of the data set.\r\nData Analyses\r\nAnalyses conducted in the interpretation of the results were thematic analysis, as through its theoretical freedom, it provides a flexible and useful research tool, which can potentially provide a rich and detailed, yet complex, account of data (Braun & Clarke, 2006).\r\nPreliminary analyses were conducted of the notes that were taken during the interview immediately following each one of them. This was to develop an early understanding of the types of challenges identified by participants (Power & Alison, 2017). Analyses continued during interview transcription by keeping notes on the key themes that emerged during transcription.\r\nAfter the transcription of the interviews, thematic analyses were conducted on the data using NVIVO. Thematic analysis is a method for identifying, analysing and reporting patterns (themes) within data, minimally organising and describing the data set in (rich) detail (Braun & Clarke, 2006).\r\nAccording to Braun and Clarke (2006), thematic analyses involved six phases: (a) familiarization with the data through transcription and rereading of the text; (b) generation of initial codes across the data set; (c) collation of codes into themes that captured something important of the data in relation of the research question, and represents some level of patterned within the data set; (d) revision of themes and refinement of categories; (e) definition and naming of themes of the overall story that the data tells; and (f) production of a detailed scholarly report of analyses. Following the example of Power and Alison (2017), it is important to mention that the themes were not quantified. The reasons for this was that the interview style was semi-structured, meaning that not all participants were asked the same questions as prompts were used to probe discussion rather than lead it (i.e., just because the participant did not perceive lack of parental support as an issue in their description of the situations that may lead to students to disengage school, it does not mean that they do not perceive it as a helping factor to remain. Analyses were conducted by the primary researcher, who discussed coding with her supervisor to reach mutual agreement and consensus."]]]],["element",{"elementId":"45"},["name","Publisher"],["description","An entity responsible for making the resource available"],["elementTextContainer",["elementText",{"elementTextId":"2162"},["text","Lancaster University"]]]],["element",{"elementId":"42"},["name","Format"],["description","The file format, physical medium, or dimensions of the resource"],["elementTextContainer",["elementText",{"elementTextId":"2163"},["text","text/NVivo"]]]],["element",{"elementId":"43"},["name","Identifier"],["description","An unambiguous reference to the resource within a given context"],["elementTextContainer",["elementText",{"elementTextId":"2164"},["text","GómezLuengo2017"]]]],["element",{"elementId":"37"},["name","Contributor"],["description","An entity responsible for making contributions to the resource"],["elementTextContainer",["elementText",{"elementTextId":"2165"},["text","John Towse"]]]],["element",{"elementId":"47"},["name","Rights"],["description","Information about rights held in and over the resource"],["elementTextContainer",["elementText",{"elementTextId":"2166"},["text","Open"]]]],["element",{"elementId":"46"},["name","Relation"],["description","A related resource"],["elementTextContainer",["elementText",{"elementTextId":"2167"},["text","None"]]]],["element",{"elementId":"44"},["name","Language"],["description","A language of the resource"],["elementTextContainer",["elementText",{"elementTextId":"2168"},["text","English"]]]],["element",{"elementId":"51"},["name","Type"],["description","The nature or genre of the resource"],["elementTextContainer",["elementText",{"elementTextId":"2169"},["text","Text (speech)"]]]],["element",{"elementId":"38"},["name","Coverage"],["description","The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant"],["elementTextContainer",["elementText",{"elementTextId":"2170"},["text","LA1 4YF"]]]]]],["elementSet",{"elementSetId":"4"},["name","LUSTRE"],["description","Adds LUSTRE specific project information"],["elementContainer",["element",{"elementId":"52"},["name","Supervisor"],["description","Name of the project supervisor"],["elementTextContainer",["elementText",{"elementTextId":"2171"},["text","Nicola Power"]]]],["element",{"elementId":"53"},["name","Project Level"],["description","Project levels should be entered as UG or MSC"],["elementTextContainer",["elementText",{"elementTextId":"2172"},["text","MSc"]]]],["element",{"elementId":"54"},["name","Topic"],["description","Should contain the sub-category of Psychology the project falls under"],["elementTextContainer",["elementText",{"elementTextId":"2173"},["text","Social Psychology"]]]],["element",{"elementId":"56"},["name","Sample Size"],["description"],["elementTextContainer",["elementText",{"elementTextId":"2174"},["text","A total of 15 people participated in this study. Data was collected from the students of a small school of about 120 in a small indigenous village near Antigua, Guatemala"]]]],["element",{"elementId":"55"},["name","Statistical Analysis Type"],["description","The type of statistical analysis used in the project"],["elementTextContainer",["elementText",{"elementTextId":"2175"},["text","Qualitative "]]]]]]]],["item",{"itemId":"34","public":"1","featured":"0"},["collection",{"collectionId":"10"},["elementSetContainer",["elementSet",{"elementSetId":"1"},["name","Dublin Core"],["description","The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/."],["elementContainer",["element",{"elementId":"50"},["name","Title"],["description","A name given to the resource"],["elementTextContainer",["elementText",{"elementTextId":"819"},["text","Interviews"]]]]]]]],["elementSetContainer",["elementSet",{"elementSetId":"1"},["name","Dublin Core"],["description","The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/."],["elementContainer",["element",{"elementId":"50"},["name","Title"],["description","A name given to the resource"],["elementTextContainer",["elementText",{"elementTextId":"1081"},["text","Effects of service brands’ current marketing strategies on customer attitude and behaviour"]]]],["element",{"elementId":"39"},["name","Creator"],["description","An entity primarily responsible for making the resource"],["elementTextContainer",["elementText",{"elementTextId":"1082"},["text","Laura Gould"]]]],["element",{"elementId":"40"},["name","Date"],["description","A point or period of time associated with an event in the lifecycle of the resource"],["elementTextContainer",["elementText",{"elementTextId":"1083"},["text","2013"]]]],["element",{"elementId":"41"},["name","Description"],["description","An account of the resource"],["elementTextContainer",["elementText",{"elementTextId":"1084"},["text","This project investigated current marketing strategies used by service brands, including car insurance and household energy companies, on customer’s attitudes and behaviours. The investigation involved a nationally representative sample of 1,977 participants completing an online survey, along with 30 participants taking part in a supporting qualitative online community panel exploring customers’ attitudes in more depth. Descriptive analysis showed that although participants’ loyalty reasons and bad provider experiences were predominantly determined by price, service quality was also an important factor. When choosing service providers, participants showed no preference between price and service, however slightly preferred price over reputation. Furthermore, significant correlations were found for the majority of provider switching attitudes and switching behaviours. Segmentation analysis identified four types of customers based on awareness of offers and convenience to switch: ‘Passive Loyals’, ‘Sceptical Loyals’, ‘Loyal Opportunity Switchers’ and ‘Conditioned Switchers’. A pattern was found amongst age groups – the older the participant, the more likely they preferred new customer deals over loyalty offers, were more interested in price over service quality and brand reputation, and had more expertise in the service industry. Due to the importance of customer retention (Berry, 1983), results implied brands should focus on loyalty rewards, along with gaining customers’ trust in their service quality and reliability.�"]]]],["element",{"elementId":"48"},["name","Source"],["description","A related resource from which the described resource is derived"],["elementTextContainer",["elementText",{"elementTextId":"1085"},["text","Materials and procedure\r\n\tThe online survey included both closed and open questions, thus a mixture of quantitative and qualitative methods. Participants were asked a few demographic questions: their gender, age, employment status, type of residence, location, and how long they had been a UK resident. Those who had not been screened were quizzed on their switching and service provider behaviours and attitudes. The questions were repeated for each service type, depending on whether participants had confirmed to paying for, or making decisions on electricity, gas or car insurance, and whether they’re household energy was on a dual fuel package or organised separately. The questions that were repeated were a select few, which required separate data for each service type. These included inquiries into people’s prior switching behaviour, reasons for maintaining a relationship with their current provider, and any ‘bad’ experiences they had encountered with a service provider. This was then followed by general attitudinal scales concerned with loyalty and switching amongst service providers. The data was collected in Confirmit, a software used to create, monitor and analyse online surveys (http://www.confirmit.com/). The quantitative information was analysed in Reportal, Confirmit’s analysis feature. The qualitative answers were transferred into Microsoft Excel, and examined through the CIT (Flanagan, 1954).\r\n\r\nStage 2:\r\nMethod\r\nThe second stage of the project involved segmenting participants into different groups, in order to identify different people’s needs and desires in regard to switching and loyalty, and how brands should consider different types of individuals in their marketing and advertising. First, segmentation dimensions were established, and every participants was assigned to a particular segmentation cluster. Secondly, the different segments were profiled to make each group as distinct as possible. \r\nSegmentation\r\nIdentifying the segments was explorative, thus there were no set guidelines as to which dimensions to use. However, segmentation was based around the idea of switching and loyalty, and whether participants were able to be classed as different types of customers according to this. Attitudinal scales used in the online survey in stage 1 were either negatively or positively related to switching. Using k cluster means analysis on SPSS, the following variables were used as dimensions for the segmentation (along with the statements used in the online survey):\r\nConvenience of switching: I find that switching service providers is inconvenient\r\nAwareness of other deals: I’m not aware of offers available from other providers other than my current one\r\nThe first variable determined how constrained individuals were from switching brands. This gave an idea of whether participants felt dedicated or constrained by service brands (constrained by brands: Stanley & Markman, 1992). If a person found switching convenient, they were not held by constraints to stay within the company, thus were more likely to remain with the provider due to their brand dedication.\r\nThe second variable determined participants’ characteristics, whether they actively sought to evaluate other providers’ offers (awareness of other offers: Zeithaml, 1981).  According to the research, if someone showed both awareness of other offers and convenience to switch, the individual felt they were more able to switch and were presumably more likely to switch. \r\nBoth statements were answered on a 10 point scale in the online survey, thus when segmentation divisions were made according to these dimensions, the following cluster centres for each segment were identified:\r\nsegments\r\nI find that switching service providers is inconvenient\r\n\r\nI’m not aware of offers available from other providers other than my current one\r\n\r\n1\r\n8\r\n8\r\n2\r\n8\r\n3\r\n3\r\n5\r\n6\r\n4\r\n3\r\n2\r\nTable 4. Typical scores observed for each statement across the 4 segments identified\r\nEach participant who took part in the survey was assigned to one of the four segments according to their scores on the two dimensions. Whichever segment cluster centres were closest to their scores determined their segment group.\r\nThe next phase of the segmentation process was profiling each group in order to make them as identifiable and different to the other groups as possible. This was carried out by comparing answers from the online survey by producing crosstabs across the four clusters. Once the variation of answers were cross tabbed, comparisons were able to be carried out. This gave an insight into how many people in a particular segment gave a certain answer to the questions. In order to find any significant differences from the overall mean, answers were indexed. \r\nIndexing\r\n\tIndexing was used to look at how over-represented or under-represented certain characteristics were for the four segments, relative to the base sample (1,977 participants). This was carried out by calculating an index score:\r\nPercentage incidence of the variable for the target group\tx 100\r\nPercentage incidence of the variable for the base group \r\nThe index score indicated whether the variables for the two groups were showing significant differences. Comparing them gave an idea of which variable was over indexing the most or least, giving a picture of what ‘ingredients’  may be making up the main differences between segments. Generally an index of less than 80 or greater than 120 was considered significantly statistically different.\r\nSupporting qualitative attitudes \r\nThis was supported by a of the project involved an in-depth qualitative investigation, exploring participants’ attitudes on brand loyalty and switching, past experiences with household energy and car insurance providers, and attitudes on current loyalty and switching strategies. Participants were identified into 1 of the four segments and analysed through noting important themes and patterns of people’s attitudes in the data. \r\n"]]]],["element",{"elementId":"45"},["name","Publisher"],["description","An entity responsible for making the resource available"],["elementTextContainer",["elementText",{"elementTextId":"1086"},["text","Lancaster University"]]]],["element",{"elementId":"42"},["name","Format"],["description","The file format, physical medium, or dimensions of the resource"],["elementTextContainer",["elementText",{"elementTextId":"1087"},["text","data/excel.xlsx"]]]],["element",{"elementId":"43"},["name","Identifier"],["description","An unambiguous reference to the resource within a given context"],["elementTextContainer",["elementText",{"elementTextId":"1088"},["text","Gould2013"]]]],["element",{"elementId":"37"},["name","Contributor"],["description","An entity responsible for making contributions to the resource"],["elementTextContainer",["elementText",{"elementTextId":"1089"},["text","John Towse"]]]],["element",{"elementId":"47"},["name","Rights"],["description","Information about rights held in and over the resource"],["elementTextContainer",["elementText",{"elementTextId":"1090"},["text","Open"]]]],["element",{"elementId":"44"},["name","Language"],["description","A language of the resource"],["elementTextContainer",["elementText",{"elementTextId":"1091"},["text","English"]]]],["element",{"elementId":"51"},["name","Type"],["description","The nature or genre of the resource"],["elementTextContainer",["elementText",{"elementTextId":"1092"},["text","Data"]]]],["element",{"elementId":"38"},["name","Coverage"],["description","The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant"],["elementTextContainer",["elementText",{"elementTextId":"1093"},["text","LA1 4YF"]]]]]],["elementSet",{"elementSetId":"4"},["name","LUSTRE"],["description","Adds LUSTRE specific project information"],["elementContainer",["element",{"elementId":"52"},["name","Supervisor"],["description","Name of the project supervisor"],["elementTextContainer",["elementText",{"elementTextId":"1094"},["text","Leslie Hallam"]]]],["element",{"elementId":"53"},["name","Project Level"],["description","Project levels should be entered as UG or MSC"],["elementTextContainer",["elementText",{"elementTextId":"1095"},["text","MSc"]]]],["element",{"elementId":"54"},["name","Topic"],["description","Should contain the sub-category of Psychology the project falls under"],["elementTextContainer",["elementText",{"elementTextId":"1096"},["text","Psychology of Advertising"]]]],["element",{"elementId":"56"},["name","Sample Size"],["description"],["elementTextContainer",["elementText",{"elementTextId":"1097"},["text","A nationally representative sample of 1977 participants were recruited, all over 18 years of age in order to comply with the MRS code of conduct (https://www.mrs.org.uk/standards/code_of_conduct/) and were picked according to the current UK’s demographic statistics (see appendix C). Furthermore, individuals were screened from the survey if they had lived in the UK for only a very short period of time as they would not have had enough experience with household energy and car insurance providers to reliably compare them on their previous switching behaviour. Individuals were also screened if they were not involved in the payment of, or in the decision making for household energy or car insurance. 2596 people took part in the survey, which included those who did not fully complete the questions (338), started the survey after the deadline (21) and those that did not comply with the projects’ requirements (236). This left a total of 2001 complete responses which were used in the project. \r\nThe qualitative data collection involved recruiting 30 participants of those who opted to take part in further research from the 1,977 original sample. A mixture of dual fuel, gas, electricity and car insurance customers were contacted via email (200 emails in total) (see appendix E.1). Of those that responded, 30 were chosen to take part in the qualitative research"]]]],["element",{"elementId":"55"},["name","Statistical Analysis Type"],["description","The type of statistical analysis used in the project"],["elementTextContainer",["elementText",{"elementTextId":"1098"},["text","qualitative"]]]]]]]]]