- Pre-registration nursing students
- No definition of master’s degree in nursing described in the publication
After the search, we collated and uploaded all the identified records into EndNote v.X8 (Clarivate Analytics, Philadelphia, Pennsylvania) and removed any duplicates. Two independent reviewers (MCS and SA) screened the titles and abstracts for assessment in line with the inclusion criteria. They retrieved and assessed the full texts of the selected studies while applying the inclusion criteria. Any disagreements about the eligibility of studies were resolved by discussion or, if no consensus could be reached, by involving experienced researchers (MZ-S and RP).
The first reviewer (MCS) extracted data from the selected publications. For this purpose, an extraction tool developed by the authors was used. This tool comprised the following criteria: author(s), year of publication, country, research question, design, case definition, data sources, and methodologic and data-analysis triangulation. First, we extracted and summarized information about the case study design. Second, we narratively summarized the way in which the data and methodological triangulation were described. Finally, we summarized the information on within-case or cross-case analysis. This process was performed using Microsoft Excel. One reviewer (MCS) extracted data, whereas another reviewer (SA) cross-checked the data extraction, making suggestions for additions or edits. Any disagreements between the reviewers were resolved through discussion.
A total of 149 records were identified in 2 databases. We removed 20 duplicates and screened 129 reports by title and abstract. A total of 46 reports were assessed for eligibility. Through hand searches, we identified 117 additional records. Of these, we excluded 98 reports after title and abstract screening. A total of 17 reports were assessed for eligibility. From the 2 databases and the hand search, 63 reports were assessed for eligibility. Ultimately, we included 8 articles for data extraction. No further articles were included after the reference list screening of the included studies. A PRISMA flow diagram of the study selection and inclusion process is presented in Figure 1 . As shown in Tables 2 and and3, 3 , the articles included in this scoping review were published between 2010 and 2022 in Canada (n = 3), the United States (n = 2), Australia (n = 2), and Scotland (n = 1).
PRISMA flow diagram.
Characteristics of Articles Included.
Author | Contandriopoulos et al | Flinter | Hogan et al | Hungerford et al | O’Rourke | Roots and MacDonald | Schadewaldt et al | Strachan et al |
---|---|---|---|---|---|---|---|---|
Country | Canada | The United States | The United States | Australia | Canada | Canada | Australia | Scotland |
How or why research question | No information on the research question | Several how or why research questions | What and how research question | No information on the research question | Several how or why research questions | No information on the research question | What research question | What and why research questions |
Design and referenced author of methodological guidance | Six qualitative case studies Robert K. Yin | Multiple-case studies design Robert K. Yin | Multiple-case studies design Robert E. Stake | Case study design Robert K. Yin | Qualitative single-case study Robert K. Yin Robert E. Stake Sharan Merriam | Single-case study design Robert K. Yin Sharan Merriam | Multiple-case studies design Robert K. Yin Robert E. Stake | Multiple-case studies design |
Case definition | Team of health professionals (Small group) | Nurse practitioners (Individuals) | Primary care practices (Organization) | Community-based NP model of practice (Organization) | NP-led practice (Organization) | Primary care practices (Organization) | No information on case definition | Health board (Organization) |
Overview of Within-Method, Between/Across-Method, and Data-Analysis Triangulation.
Author | Contandriopoulos et al | Flinter | Hogan et al | Hungerford et al | O’Rourke | Roots and MacDonald | Schadewaldt et al | Strachan et al |
---|---|---|---|---|---|---|---|---|
Within-method triangulation (using within-method triangulation use at least 2 data-collection procedures from the same design approach) | ||||||||
: | ||||||||
Interviews | X | x | x | x | x | |||
Observations | x | x | ||||||
Public documents | x | x | x | |||||
Electronic health records | x | |||||||
Between/across-method (using both qualitative and quantitative data-collection procedures in the same study) | ||||||||
: | ||||||||
: | ||||||||
Interviews | x | x | x | |||||
Observations | x | x | ||||||
Public documents | x | x | ||||||
Electronic health records | x | |||||||
: | ||||||||
Self-assessment | x | |||||||
Service records | x | |||||||
Questionnaires | x | |||||||
Data-analysis triangulation (combination of 2 or more methods of analyzing data) | ||||||||
: | ||||||||
: | ||||||||
Deductive | x | x | x | |||||
Inductive | x | x | ||||||
Thematic | x | x | ||||||
Content | ||||||||
: | ||||||||
Descriptive analysis | x | x | x | |||||
: | ||||||||
: | ||||||||
Deductive | x | x | x | x | ||||
Inductive | x | x | ||||||
Thematic | x | |||||||
Content | x |
The following sections describe the research question, case definition, and case study design. Case studies are most appropriate when asking “how” or “why” questions. 1 According to Yin, 1 how and why questions are explanatory and lead to the use of case studies, histories, and experiments as the preferred research methods. In 1 study from Canada, eg, the following research question was presented: “How and why did stakeholders participate in the system change process that led to the introduction of the first nurse practitioner-led Clinic in Ontario?” (p7) 19 Once the research question has been formulated, the case should be defined and, subsequently, the case study design chosen. 1 In typical case studies with mixed methods, the 2 types of data are gathered concurrently in a convergent design and the results merged to examine a case and/or compare multiple cases. 10
“How” or “why” questions were found in 4 studies. 16 , 17 , 19 , 22 Two studies additionally asked “what” questions. Three studies described an exploratory approach, and 1 study presented an explanatory approach. Of these 4 studies, 3 studies chose a qualitative approach 17 , 19 , 22 and 1 opted for mixed methods with a convergent design. 16
In the remaining studies, either the research questions were not clearly stated or no “how” or “why” questions were formulated. For example, “what” questions were found in 1 study. 21 No information was provided on exploratory, descriptive, and explanatory approaches. Schadewaldt et al 21 chose mixed methods with a convergent design.
A total of 5 studies defined the case as an organizational unit. 17 , 18 - 20 , 22 Of the 8 articles, 4 reported multiple-case studies. 16 , 17 , 22 , 23 Another 2 publications involved single-case studies. 19 , 20 Moreover, 2 publications did not state the case study design explicitly.
This section describes within-method triangulation, which involves employing at least 2 data-collection procedures within the same design approach. 6 , 7 This can also be called data source triangulation. 8 Next, we present the single data-collection procedures in detail. In 5 studies, information on within-method triangulation was found. 15 , 17 - 19 , 22 Studies describing a quantitative approach and the triangulation of 2 or more quantitative data-collection procedures could not be included in this scoping review.
Five studies used qualitative data-collection procedures. Two studies combined face-to-face interviews and documents. 15 , 19 One study mixed in-depth interviews with observations, 18 and 1 study combined face-to-face interviews and documentation. 22 One study contained face-to-face interviews, observations, and documentation. 17 The combination of different qualitative data-collection procedures was used to present the case context in an authentic and complex way, to elicit the perspectives of the participants, and to obtain a holistic description and explanation of the cases under study.
All 5 studies used qualitative interviews as the primary data-collection procedure. 15 , 17 - 19 , 22 Face-to-face, in-depth, and semi-structured interviews were conducted. The topics covered in the interviews included processes in the introduction of new care services and experiences of barriers and facilitators to collaborative work in general practices. Two studies did not specify the type of interviews conducted and did not report sample questions. 15 , 18
In 2 studies, qualitative observations were carried out. 17 , 18 During the observations, the physical design of the clinical patients’ rooms and office spaces was examined. 17 Hungerford et al 18 did not explain what information was collected during the observations. In both studies, the type of observation was not specified. Observations were generally recorded as field notes.
In 3 studies, various qualitative public documents were studied. 15 , 19 , 22 These documents included role description, education curriculum, governance frameworks, websites, and newspapers with information about the implementation of the role and general practice. Only 1 study failed to specify the type of document and the collected data. 15
In 1 study, qualitative documentation was investigated. 17 This included a review of dashboards (eg, provider productivity reports or provider quality dashboards in the electronic health record) and quality performance reports (eg, practice-wide or co-management team-wide performance reports).
This section describes the between/across methods, which involve employing both qualitative and quantitative data-collection procedures in the same study. 6 , 7 This procedure can also be denoted “methodologic triangulation.” 8 Subsequently, we present the individual data-collection procedures. In 3 studies, information on between/across triangulation was found. 16 , 20 , 21
Three studies used qualitative and quantitative data-collection procedures. One study combined face-to-face interviews, documentation, and self-assessments. 16 One study employed semi-structured interviews, direct observation, documents, and service records, 20 and another study combined face-to-face interviews, non-participant observation, documents, and questionnaires. 23
All 3 studies used qualitative interviews as the primary data-collection procedure. 16 , 20 , 23 Face-to-face and semi-structured interviews were conducted. In the interviews, data were collected on the introduction of new care services and experiences of barriers to and facilitators of collaborative work in general practices.
In 2 studies, direct and non-participant qualitative observations were conducted. 20 , 23 During the observations, the interaction between health professionals or the organization and the clinical context was observed. Observations were generally recorded as field notes.
In 2 studies, various qualitative public documents were examined. 20 , 23 These documents included role description, newspapers, websites, and practice documents (eg, flyers). In the documents, information on the role implementation and role description of NPs was collected.
In 1 study, qualitative individual journals were studied. 16 These included reflective journals from NPs, who performed the role in primary health care.
Only 1 study involved quantitative service records. 20 These service records were obtained from the primary care practices and the respective health authorities. They were collected before and after the implementation of an NP role to identify changes in patients’ access to health care, the volume of patients served, and patients’ use of acute care services.
In 2 studies, quantitative questionnaires were used to gather information about the teams’ satisfaction with collaboration. 16 , 21 In 1 study, 3 validated scales were used. The scales measured experience, satisfaction, and belief in the benefits of collaboration. 21 Psychometric performance indicators of these scales were provided. However, the time points of data collection were not specified; similarly, whether the questionnaires were completed online or by hand was not mentioned. A competency self-assessment tool was used in another study. 16 The assessment comprised 70 items and included topics such as health promotion, protection, disease prevention and treatment, the NP-patient relationship, the teaching-coaching function, the professional role, managing and negotiating health care delivery systems, monitoring and ensuring the quality of health care practice, and cultural competence. Psychometric performance indicators were provided. The assessment was completed online with 2 measurement time points (pre self-assessment and post self-assessment).
This section describes data-analysis triangulation, which involves the combination of 2 or more methods of analyzing data. 6 Subsequently, we present within-case analysis and cross-case analysis.
Three studies combined qualitative and quantitative methods of analysis. 16 , 20 , 21 Two studies involved deductive and inductive qualitative analysis, and qualitative data were analyzed thematically. 20 , 21 One used deductive qualitative analysis. 16 The method of analysis was not specified in the studies. Quantitative data were analyzed using descriptive statistics in 3 studies. 16 , 20 , 23 The descriptive statistics comprised the calculation of the mean, median, and frequencies.
Two studies combined deductive and inductive qualitative analysis, 19 , 22 and 2 studies only used deductive qualitative analysis. 15 , 18 Qualitative data were analyzed thematically in 1 study, 22 and data were treated with content analysis in the other. 19 The method of analysis was not specified in the 2 studies.
In 7 studies, a within-case analysis was performed. 15 - 20 , 22 Six studies used qualitative data for the within-case analysis, and 1 study employed qualitative and quantitative data. Data were analyzed separately, consecutively, or in parallel. The themes generated from qualitative data were compared and then summarized. The individual cases were presented mostly as a narrative description. Quantitative data were integrated into the qualitative description with tables and graphs. Qualitative and quantitative data were also presented as a narrative description.
Of the multiple-case studies, 5 carried out cross-case analyses. 15 - 17 , 20 , 22 Three studies described the cross-case analysis using qualitative data. Two studies reported a combination of qualitative and quantitative data for the cross-case analysis. In each multiple-case study, the individual cases were contrasted to identify the differences and similarities between the cases. One study did not specify whether a within-case or a cross-case analysis was conducted. 23
This section describes confirmation or contradiction through qualitative and quantitative data. 1 , 4 Qualitative and quantitative data were reported separately, with little connection between them. As a result, the conclusions on neither the comparisons nor the contradictions could be clearly determined.
In 3 studies, the consistency of the results of different types of qualitative data was highlighted. 16 , 19 , 21 In particular, documentation and interviews or interviews and observations were contrasted:
Both types of data showed that NPs and general practitioners wanted to have more time in common to discuss patient cases and engage in personal exchanges. 21 In addition, the qualitative and quantitative data confirmed the individual progression of NPs from less competent to more competent. 16 One study pointed out that qualitative and quantitative data obtained similar results for the cases. 20 For example, integrating NPs improved patient access by increasing appointment availability.
Although questionnaire results indicated that NPs and general practitioners experienced high levels of collaboration and satisfaction with the collaborative relationship, the qualitative results drew a more ambivalent picture of NPs’ and general practitioners’ experiences with collaboration. 21
The studies included in this scoping review evidenced various research questions. The recommended formats (ie, how or why questions) were not applied consistently. Therefore, no case study design should be applied because the research question is the major guide for determining the research design. 2 Furthermore, case definitions and designs were applied variably. The lack of standardization is reflected in differences in the reporting of these case studies. Generally, case study research is viewed as allowing much more freedom and flexibility. 5 , 24 However, this flexibility and the lack of uniform specifications lead to confusion.
Methodologic triangulation, as described in the literature, can be somewhat confusing as it can refer to either data-collection methods or research designs. 6 , 8 For example, methodologic triangulation can allude to qualitative and quantitative methods, indicating a paradigmatic connection. Methodologic triangulation can also point to qualitative and quantitative data-collection methods, analysis, and interpretation without specific philosophical stances. 6 , 8 Regarding “data-collection methods with no philosophical stances,” we would recommend using the wording “data source triangulation” instead. Thus, the demarcation between the method and the data-collection procedures will be clearer.
Yin 1 advocated the use of multiple sources of evidence so that a case or cases can be investigated more comprehensively and accurately. Most studies included multiple data-collection procedures. Five studies employed a variety of qualitative data-collection procedures, and 3 studies used qualitative and quantitative data-collection procedures (mixed methods). In contrast, no study contained 2 or more quantitative data-collection procedures. In particular, quantitative data-collection procedures—such as validated, reliable questionnaires, scales, or assessments—were not used exhaustively. The prerequisites for using multiple data-collection procedures are availability, the knowledge and skill of the researcher, and sufficient financial funds. 1 To meet these prerequisites, research teams consisting of members with different levels of training and experience are necessary. Multidisciplinary research teams need to be aware of the strengths and weaknesses of different data sources and collection procedures. 1
When using multiple data sources and analysis methods, it is necessary to present the results in a coherent manner. Although the importance of multiple data sources and analysis has been emphasized, 1 , 5 the description of triangulation has tended to be brief. Thus, traceability of the research process is not always ensured. The sparse description of the data-analysis triangulation procedure may be due to the limited number of words in publications or the complexity involved in merging the different data sources.
Only a few concrete recommendations regarding the operationalization of the data-analysis triangulation with the qualitative data process were found. 25 A total of 3 approaches have been proposed 25 : (1) the intuitive approach, in which researchers intuitively connect information from different data sources; (2) the procedural approach, in which each comparative or contrasting step in triangulation is documented to ensure transparency and replicability; and (3) the intersubjective approach, which necessitates a group of researchers agreeing on the steps in the triangulation process. For each case study, one of these 3 approaches needs to be selected, carefully carried out, and documented. Thus, in-depth examination of the data can take place. Farmer et al 25 concluded that most researchers take the intuitive approach; therefore, triangulation is not clearly articulated. This trend is also evident in our scoping review.
Few studies in this scoping review used a combination of qualitative and quantitative analysis. However, creating a comprehensive stand-alone picture of a case from both qualitative and quantitative methods is challenging. Findings derived from different data types may not automatically coalesce into a coherent whole. 4 O’Cathain et al 26 described 3 techniques for combining the results of qualitative and quantitative methods: (1) developing a triangulation protocol; (2) following a thread by selecting a theme from 1 component and following it across the other components; and (3) developing a mixed-methods matrix.
The most detailed description of the conducting of triangulation is the triangulation protocol. The triangulation protocol takes place at the interpretation stage of the research process. 26 This protocol was developed for multiple qualitative data but can also be applied to a combination of qualitative and quantitative data. 25 , 26 It is possible to determine agreement, partial agreement, “silence,” or dissonance between the results of qualitative and quantitative data. The protocol is intended to bring together the various themes from the qualitative and quantitative results and identify overarching meta-themes. 25 , 26
The “following a thread” technique is used in the analysis stage of the research process. To begin, each data source is analyzed to identify the most important themes that need further investigation. Subsequently, the research team selects 1 theme from 1 data source and follows it up in the other data source, thereby creating a thread. The individual steps of this technique are not specified. 26 , 27
A mixed-methods matrix is used at the end of the analysis. 26 All the data collected on a defined case are examined together in 1 large matrix, paying attention to cases rather than variables or themes. In a mixed-methods matrix (eg, a table), the rows represent the cases for which both qualitative and quantitative data exist. The columns show the findings for each case. This technique allows the research team to look for congruency, surprises, and paradoxes among the findings as well as patterns across multiple cases. In our review, we identified only one of these 3 approaches in the study by Roots and MacDonald. 20 These authors mentioned that a causal network analysis was performed using a matrix. However, no further details were given, and reference was made to a later publication. We could not find this publication.
Because it focused on the implementation of NPs in primary health care, the setting of this scoping review was narrow. However, triangulation is essential for research in this area. This type of research was found to provide a good basis for understanding methodologic and data-analysis triangulation. Despite the lack of traceability in the description of the data and methodological triangulation, we believe that case studies are an appropriate design for exploring new nursing roles in existing health care systems. This is evidenced by the fact that case study research is widely used in many social science disciplines as well as in professional practice. 1 To strengthen this research method and increase the traceability in the research process, we recommend using the reporting guideline and reporting checklist by Rodgers et al. 9 This reporting checklist needs to be complemented with methodologic and data-analysis triangulation. A procedural approach needs to be followed in which each comparative step of the triangulation is documented. 25 A triangulation protocol or a mixed-methods matrix can be used for this purpose. 26 If there is a word limit in a publication, the triangulation protocol or mixed-methods matrix needs to be identified. A schematic representation of methodologic and data-analysis triangulation in case studies can be found in Figure 2 .
Schematic representation of methodologic and data-analysis triangulation in case studies (own work).
This study suffered from several limitations that must be acknowledged. Given the nature of scoping reviews, we did not analyze the evidence reported in the studies. However, 2 reviewers independently reviewed all the full-text reports with respect to the inclusion criteria. The focus on the primary care setting with NPs (master’s degree) was very narrow, and only a few studies qualified. Thus, possible important methodological aspects that would have contributed to answering the questions were omitted. Studies describing the triangulation of 2 or more quantitative data-collection procedures could not be included in this scoping review due to the inclusion and exclusion criteria.
Given the various processes described for methodologic and data-analysis triangulation, we can conclude that triangulation in case studies is poorly standardized. Consequently, the traceability of the research process is not always given. Triangulation is complicated by the confusion of terminology. To advance case study research in nursing, we encourage authors to reflect critically on methodologic and data-analysis triangulation and use existing tools, such as the triangulation protocol or mixed-methods matrix and the reporting guideline checklist by Rodgers et al, 9 to ensure more transparent reporting.
Acknowledgments.
The authors thank Simona Aeschlimann for her support during the screening process.
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.
Supplemental Material: Supplemental material for this article is available online.
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International home improvement retailer kingfisher group opens up about the evolution of its ai strategy, and the rewards it is reaping.
Several months into the start of the global Covid-19 coronavirus pandemic, international home improvement retail group Kingfisher debuted a revamped company strategy focused on repositioning the organisation as a digital and service-oriented entity.
Kingfisher, which owns the B&Q, Screwfix and DIY.com brands in the UK, had seen several of its brands suffer sales declines as a result of what it termed in its 2020 financial results as “the company’s operating model becoming overly complex”.
“While some of our banners [brands] have delivered growth over the past four years … our performance has been disappointing. Group sales and retail profit need to improve,” its financial report, published in June 2020, stated.
In the wake of this realisation, the Powered by Kingfisher strategy was created, with an emphasis on ensuring each of the company’s brands was meeting the diverse and distinct needs of their respective customer bases, while also drawing on the businesses “core strengths and commercial assets”.
“To serve customers effectively today, we also need to be digital and service-orientated, while leveraging our strong store assets,” the report added.
A month after going public with its plans for a strategic shift in how the company operates, Kingfisher announced the creation of a new role within its customer team with the appointment of Tom Betts as group data director.
Fast forward several years, and these two events have led to Kingfisher having its own in-house data and artificial intelligence (AI) team whose efforts have seen it centrally develop and roll out various digital tools that have boosted sales across its brands.
On this point, the company’s 2024 financial report stated: “Our [brands] are leveraging data and artificial intelligence to build customer-centric tools and solutions, support better commercial decision-making and higher productivity, thereby unlocking significant new sources of revenue, profit and cash.”
Speaking to Computer Weekly, Mohsen Ghasempour, group AI director at Kingfisher, said the appointment of Betts led to the creation of a team that has steadily grown in size and whose work has led to a notable uptick in sale across the group.
“We started with almost zero people on AI, and today we have around 28 – a mixture of machine learning engineers, data scientists, and engineers – so we [have the internal capabilities] to develop our own AI solutions,” he said,
“If you look at our portfolio of AI offerings today, we have 30-plus different initiatives on the go … and it might surprise people to know how much AI technology is impacting the way the DIY industry is operating.”
The company is using AI in its supply chain management and logistics function to deliver a demand forecasting model that can predict how demand for certain products will change over a 12-month period, as well as to pick up on patterns within the reviews customers leave about its products.
“We have services that sit on top of our customer reviews to extract actionable insights. Our AI algorithm can detect that 200 reviews are about product quality, and what specifically they are complaining about,” said Ghasempour.
The company is also working on some “very cool technology” that will help the group’s in-store customers find the products they are looking for more efficiently, he added. “There is a lot happening with AI here at the moment.”
However, when Ghasempour first joined the company three years ago, Kingfisher knew it wanted to use AI to help achieve its strategic goals, but was still figuring out what role the technology would play in its business.
“When we started, there was no plan in terms of ‘This is how we’re going to use AI’,” he said. “So, the question became ‘How are we going to use it?’”
The answer to that came through trying to address what Ghasempour describes as one of the businesses’ biggest problems: a customer wanting to buy a product online that is no longer in stock.
“It wasn’t an AI problem, it was a product availability issue [that needed solving] that was affecting customer experience,” he said. “At that time, the challenge was ‘How are we going to solve it?’, but we did not necessarily think the answer was in using AI.”
While addressing this challenge, the idea of creating an “alternative product” recommendation algorithm emerged, which Ghasempour said gave way to an exploration of what role AI could play in the process.
“We started investigating how we can use AI when customers are at the point of buying a product that is not available, and how you can recommend a product which is very similar to the product that they’re looking for as an alternative,” he said. “That was the first recommendation service we developed, it went live in early 2023 on [B&Q’s online site] diy.com.”
This service has now been rolled out, in one form or another, across all of Kingfisher’s brands, and since B&Q became the early adopter of the technology, the brand has seen more than 10% of its e-commerce sales originate from product recommendations, according to the company’s own stats.
“From the basic algorithm to solve one problem, today we have 10 different recommendation algorithms that try to help the customer journey in different ways by offering [serving customers information about] frequently bought together products and personalised recommendations,” said Ghasempour.
And the early success achieved from its first forays into building AI-powered recommendation engines allowed the company to take the concept of Powered by Kingfisher even further by providing it with the proof points needed to ditch some of its legacy tech providers, he added.
“We had some legacy recommendation providers on [our] e-commerce platform, and we started running tests A-B tests against those providers to demonstrate that we can achieve better performance, which justified building [out] this in-house [data and AI] capability even more,” he said.
“We completely replaced all the third-party providers we used for recommendation engines, so all of that, across all of our e-commerce platforms, is now powered by internal capabilities.”
These capabilities have also been created using Google Cloud’s portfolio of AI tools , with Ghasempour revealing that Kingfisher has partnerships in place with Microsoft and Amazon Web Services (AWS) too.
“Anybody wanting to build any kind of AI capability needs some infrastructure and at Kingfisher we have a partnership with all three cloud providers, but when it comes to AI and data science capability, Google has a bit more of a mature platform, from our point of view,” he said. “It was more intuitive and easier to use, so we started building that capability in Google’s infrastructure.”
Google Cloud’s fully managed development platform, Vertex AI, is playing a foundational role in the delivery of Kingfisher’s AI and data strategy, as it forms the basis of the company’s AI orchestration framework Athena.
Before the introduction of Athena, Kingfisher was effectively setting about addressing individual customer pain points, such as lack of product availability, by creating the AI microservices needed to address these problems from scratch each time.
In Kingfisher’s own words , this way of working resulted in lengthy development times for each microservice, which in turn slowed down the release time for them and caused scalability issues.
What Athena does is allow the Kingfisher team to automatically select the correct, ready-made Microsoft needed to answer a specific user issue or query, which it claims has cut the development time for new AI services from months to weeks.
“This is a fairly new technology for us, and is probably about a year old,” said Ghasempour. “And the idea behind Athena was, ‘How can we actually build a framework that means we can start to utilise the services in a in a safe and secure way, but also move fast because whoever is using this technology fastest is going to get the competitive advantage?’”
Athena acts as a “wrapper” around existing large language models, such as Google Gemini and Chat GPT, that allows Kingfisher to tap into the respective capabilities of these competing tools at once.
“Athena can wrap around all of those large language models, and provide a stronger and more powerful service because it can utilise all of those language models at the same time, plus build the security model around them. So, we can we can track all the conversation and we can make sure there is nothing inappropriate happening,” said Ghasempour.
This means Kingfisher can essentially take a “build once, apply everywhere” approach to rolling out AI services across its retail brands.
“You can just do the development once but you can scale it up to more banners [brands] while you’re still secure in the safe environment,” said Ghasempour.
Presently, Kingfisher is using Athena to create services that will make it even easier for the company’s customers to find products using AI-based conversational, image and text searches.
For instance, if a customer does not know the name of the piece of equipment they need to replace on a household item or what the name of a certain tool is, Athena makes it possible for the customer to search the product catalogue for what they need using an image and get a result in seconds.
“All they have to do is upload a photo of the part and we’ll show them exactly what they need,” said Ghasempour.
It is also experimenting with using Athena to moderate the content of the listings published on the marketplace section of diy.com, which allows third-party sellers to sell their home improvement wares online through its website.
“Athena assesses the description of the product to check for any racism or sexism, for example, and offers visual moderation of all the product images,” said Ghasempour.
Furthermore, the technology is being put to use internally at Kingfisher, to assist its 82,000-strong workforce with finding information about the group’s employment policies and guidelines that are contained within hundreds of internal staff documents.
“In any organisation you have a lot of documentation, from the legal team or HR, that tell staff what the rules of working there are, but people don’t go read the documents. So, at the moment, we’re putting [Athena] on top of those documents, so staff can ask an [internal chatbot] about the maternity leave policy, for example, and get the information they need,” said Ghasempour.
“Over the next couple of months, we’ve got a few more services going live internally to empower our colleagues using this technology to do their day-to-day jobs more efficiently.”
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IMAGES
VIDEO
COMMENTS
Case study design is an appropriate research design to consider when conceptualizing and conducting a dissertation research study that is based on an applied problem of practice with inherent real-life educational implications. Case study researchers study current, real-life cases that are in progress so that they can gather accurate ...
First, a case study provides a platform that allows you to study a situation in depth and produce the level of academic inquiry that is expected in a master's degree. In the context of any master's programme the dissertation operates as something of a showcase for a student's abilities. It can easily make the difference between getting a ...
The purpose of case study research is twofold: (1) to provide descriptive information and (2) to suggest theoretical relevance. Rich description enables an in-depth or sharpened understanding of the case. It is unique given one characteristic: case studies draw from more than one data source. Case studies are inherently multimodal or mixed ...
Revised on November 20, 2023. A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research. A case study research design usually involves qualitative methods, but quantitative methods are ...
A case study can provide appropriate research design in a qualitative or quantitative study to to gain concrete, contextual, in-depth knowledge and multi-faceted understanding of a complex issue in its real-life context. The case study can be a great tool for providing insight and developing theories in the avenue of present research. What is a
Although case studies have been discussed extensively in the literature, little has been written about the specific steps one may use to conduct case study research effectively (Gagnon, 2010; Hancock & Algozzine, 2016).Baskarada (2014) also emphasized the need to have a succinct guideline that can be practically followed as it is actually tough to execute a case study well in practice.
Case studies tend to focus on qualitative data using methods such as interviews, observations, and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data. Example: Mixed methods case study. For a case study of a wind farm development in a ...
In your dissertation template, write your introduction section, addressing each of the following points: • Restate the purpose statement. ... Yin (2009, p. 2) posited three conditions for use of a case study: the purpose must be to answer "how" or "why" questions, investigator must have little control over events, and the focus of the ...
The purpose of a paper in the social sciences designed around a case study is to thoroughly investigate a subject of analysis in order to reveal a new understanding about the research problem and, in so doing, contributing new knowledge to what is already known from previous studies. In applied social sciences disciplines [e.g., education, social work, public administration, etc.], case ...
A Case study is: An in-depth research design that primarily uses a qualitative methodology but sometimes includes quantitative methodology. Used to examine an identifiable problem confirmed through research. Used to investigate an individual, group of people, organization, or event. Used to mostly answer "how" and "why" questions.
To conclude, there are two main objectives of this study. First is to provide a step-by-step guideline to research students for conducting case study. Second, an analysis of authors' multiple case studies is presented in order to provide an application of step-by-step guideline. This article has been divided into two sections.
Case study . Case studies are often a convenient way to narrow the focus of your research by studying how a theory or literature fares with regard to a specific person, group, organisation, event or other type of entity or phenomenon you identify. Case studies can be researched using other methods, including those described in this section.
1. Select a case. Once you identify the problem at hand and come up with questions, identify the case you will focus on. The study can provide insights into the subject at hand, challenge existing assumptions, propose a course of action, and/or open up new areas for further research. 2.
Select the case most suited for the study. Select the case (s) that correspond to your research questions. Explain the reasoning for choosing these cases and why they are appropriate for your ...
If you opt to use your dissertation as a case study, ensure that you do not focus on providing solution to the problem. If it is an already written dissertation, it requires a lot of editing. In a dissertation, you provide the solution to a problem, but in case studies, only analysis of events is enough to complete the project.
A case study is one of the most commonly used methodologies of social research. This article attempts to look into the various dimensions of a case study research strategy, the different epistemological strands which determine the particular case study type and approach adopted in the field, discusses the factors which can enhance the effectiveness of a case study research, and the debate ...
McMaster University, West Hamilton, Ontario, Canada. Qualitative case study methodology prov ides tools for researchers to study. complex phenomena within their contexts. When the approach is ...
OverviewYour dissertation is a multifaceted product that should, in the end, demonstrate that you were capable of conceptualizing, managing, conducting, and presenting a rigorous and comprehensive qualitative resear. h study. Of importance, your final product should also indicate and provide clear evidence that you have addressed alignment at ...
Tips on how to find and write up case studies for your Master's Dissertation.Tips on what to include int he chapter.Tips on finding two case studies to allow...
expertise and insight you provided in support of this study. A special thanks to the chair and faculty member who allowed me to use the students in my study. Thank you for your support of my study. To Dr. DiMartino, I appreciate your guidance as an advanced qualitative research professor. I was positive that I would design a quantitative research
Case studies are often used in the exploratory phase of research to gather qualitative data. They can also be used to create, support, or refute a hypothesis and guide future research. For instance, a marketing professional might conduct a case study to discover why a viral ad campaign was so successful.
Benefits. Their flexibility: case studies are popular for a number of reasons, one being that they can be conducted at various points in the research process. Researchers are known to favour them as a way to develop ideas for more extensive research in the future - pilot studies often take the form of case studies.
Case studies are recommended to evaluate the implementation of new roles in (primary) health care, such as that of NPs. 1,5 Case studies on new role implementation can generate a unique and in-depth understanding of specific roles (individual), teams (smaller groups), family practices or similar institutions (organization), and social and ...
Hi Everyone! Case study research is a qualitative research methodology that involves in-depth investigation and analysis of a particular phenomenon, organiza...
time. Emerging themes were identified using a multiple case study methodology. All instructors said their use of CRS evolved and changed from initial adoption to their current use of the technology today. Student engagement was the single ubiquitous reason provided for choosing to employ CRS. Other potential reason for using CRS include: peer
Abstract: This article presents a controlled case study focused on implementing and using Generative Artificial Intelligence (AI), specifically Large Language Models (LLM) in physiotherapy education to assist instructors with formulating effective technology-mediated feedback for students. It outlines how these advanced technologies have been integrated into an existing feedback-oriented ...
1. Gen AI solutions are based on probabilities, while great case discussions thrive on unpredictability. Consider how the large language models (LLMs) underlying gen AI work. These models are probabilistic; they use many parameters aggregated across vast amounts of data to "predict" the most likely answer to a question, one word at a time.
The company is using AI in its supply chain management and logistics function to deliver a demand forecasting model that can predict how demand for certain products will change over a 12-month ...