Strategies for Emerging Retailers Modelled on Amazon (Designing a Model for Survival and Success) ()
1. Introduction
The United Arab Emirates (UAE) is known as an economic center with a very high potential and as an emerging and growing economy in the Middle East region (Preda, 2023). These outstanding and unique features, especially in the economic field, have attracted various investors who want to identify golden business opportunities in this country and start their activities (Alim et al., 2023). Many of these investors are looking to establish and launch new and innovative businesses in this country, especially in areas such as retail.
Since retail stores are always in the spotlight due to their direct relationship with meeting the daily needs of people and the role they play in social welfare (Surahman et al., 2024). But these businesses have faced several challenges in recent years that can have a severe negative impact on micro and macro business activities (Utari et al., 2024). These challenges for various reasons may lead to the inability of businesses and thus face them with serious problems. In this situation, many of these retail stores face a crisis and may even fail to survive in the market and go out of their commercial and economic activities. This trend not only affects the businesses themselves, but also the welfare of society. The withdrawal of these businesses from the business arena not only for them but also for most of the stakeholders and people, who are active in this commercial and economic ecosystem, brings negative consequences and unfortunate consequences. Hence, the precise identification of these challenges as well as finding suitable strategies to achieve survival in newly established retail stores that are Omni-channel or multi-channel, is extremely necessary and important.
There are several reasons why a newly established retail business cannot properly survive and succeed in the UAE. According to the evidence as well as the opinions of some experts in this field, the first challenge that such retailers face is the lack of sufficient experience against changing market trends (Manhiwa et al., 2016). Inability to adopt appropriate strategies in order to keep up with the conditions of the day and lack of knowledge of the infrastructure of a dynamic and systematic retail can seriously damage their survival. One of the most important changing trends in the market is the change of taste in consumers, which is happening rapidly in today’s world (Stewart et al., 2021). These sudden and unwanted changes make retail owners unable to provide the services and products needed by customers at the right time and in an effective manner. As a result, the lack of suitable products and services available to customers can lead to a decrease in the number of customers. This decrease in the number of customers, if not compensated quickly, can cause the failure of the business in a short period. In fact, not paying attention to these challenges and trends can lead to a vicious cycle that eventually leads the retailer to bankruptcy and exit from the market (Hayes et al., 2015). Therefore, a deep understanding of the market and consumer behavior is of particular importance as key factors for the success of newly established retailers.
Since the UAE is known as one of the leading countries in the field of tourism (Alrached, 2024), fluctuations in population growth are always observed. These demographic fluctuations caused by the frequent entry and exit of tourists to this country are significant and influential. Following these changes, the mismatch between supply and demand seems natural and inevitable (Li & Yang, 2017). This imbalance in supply and demand can have a significant impact on retailers, especially due to the cultural diversity in the UAE society, and make them face serious challenges in adopting appropriate strategies in order to provide services and products that meet the needs of consumers. In particular, this cultural diversity causes the tastes and needs of customers to be different and sometimes conflicting. As a result, emerging retailers, who may make spur-of-the-moment decisions due to lack of experience and knowledge, often face problems. These immature decisions without careful examination of the market conditions can seriously fail and cannot adapt to the existing challenges. Therefore, a correct understanding of the market and analysis of customer needs in these complex and changing conditions is extremely necessary for success and survival in this industry.
Another challenge for newly established retailers is the increasing and fierce competition in this field (Aljumah et al., 2022). Since larger and successful stores that have sufficient experience and resources and are recognized as market leaders (Madhavedi et al., 2024), they are easily able to eliminate newly established retail stores that lack effective survival strategies from the competition field. This exclusion can be due to the benefit of past experiences and deeper knowledge of the market, which can easily identify the weaknesses of these newly established stores and use it to strengthen their position. For this reason, retail stores that operate without knowing the correct patterns and strategy will face great challenges to survive in this competitive market (Faraj & Baazeem, 2024). This combination of challenges and problems continuously puts a lot of pressure on these stores, and unfortunately many of them will not be able to survive and the possibility of their collapse increases. Therefore, in a situation where there are many experienced competitors in the competitive market, identifying strategies that enable survival in retail sales is considered vital and necessary. Therefore, newly established retail owners should adopt strategies based on existing challenges. In addition, this is a serious and vital necessity for survival and success for newly established retail stores.
Based on the issues raised, the lack of survival and failure in the retail sector has many consequences that can have many negative effects on the economy and society (Gaskill et al., 1993). These consequences may lead to economic interference in the market, which in turn can affect unemployment and lead to its increase. The increase in unemployment not only worsens the individual situation of employees, but also causes a decrease in production in the country and a decrease in national income (Bilovodska & Ivanchenko, 2024). In addition, a decrease in income significantly reduces the possibility of achieving consumer satisfaction and can lead to general dissatisfaction in society. On the other hand, if the challenges of newly established retail can be overcome, this success can bring positive results, among which we can mention the reduction of unemployment rate (Barnard et al., 2011), increase of investments, creation of new business opportunities and also increase of production. Thus, it is clear that it is vital and necessary to carefully examine this issue and identify appropriate strategies for newly established retail outlets. On the other hand, many lessons can be learned from successful companies such as Amazon that can help prevent them from failing and exiting the market, both on the Internet and in the physical platform. In this way, using the experiences and effective strategies of these companies can help new retailers in facing the existing challenges and become the basis for their success in the competitive market.
Modeling the strategies and strategic plans of Amazon in other retail stores becomes more important than in the past when it can argue and express why and how for each of the strategies (Ives et al., 2019). Different methods for modeling have been stated in the researches. Therefore, the authors of this article tried to choose a method that can fully cover a deep research gap. After reviewing the previous researches, the researchers concluded to use the Grounded Theory (GT) method in the qualitative section and to use the quantitative method to confirm the results obtained in this section that can finally be scientifically and academically suitable for the design of the success model of mixed and OC retailing. Therefore, based on the performance of the Amazon retail company since its establishment, they have presented their results in the form of this model. The result of this article is a clear path for the owners of mixed and OC retail sales that can have a significant impact on the survival and growth of the business and achieve significant success.
This study highlights the importance of the survival, success of mixed, and Omni channel retail stores in the UAE, particularly for new and non-branded retailers facing high bankruptcy rates. Given the lack of localized academic and specialized information, this research is especially significant in terms of its topic, methodology, and geographical focus. The findings will offer valuable insights for retailers and investors looking to optimize retail management, enhance consumer services, and improve beneficiary satisfaction.
The primary aim of this article is to presentation a comprehensive model that caters specifically to new and unbranded retail stores. These are businesses whose owners are actively seeking pathways to their Survival and Success in a competitive marketplace. Within the context of this discussion, the concepts of survival and success in the retail sector are singled out as the pivotal focus area for all retailers. Furthermore, it’s noteworthy that all stakeholders involved have expressed their approval of each component of the proposed model. This model stands out as a significant contribution to the retail industry, as it has the potential to facilitate sustainable success for retailers. However, it is important to acknowledge that this potential can be fully realized only in the absence of widespread crises that could jeopardize market stability and retailer operations worldwide.
2. Theoretical Foundations
2.1. Sustainable Development
Sustained success in business means achieving positive and consistent results over time that focus not only on immediate profitability but also on long-term value creation for all stakeholders, including customers, employees and society (Kurznack et al., 2021). In retail, this concept means the ability of stores to provide quality products and services, maintain positive relationships with customers, and create a unique shopping experience. Retailers must continuously innovate and improve processes, exploit new technologies, and respond to changing customer needs. On the other hand, survival in business, especially in the field of retail, refers to the ability of stores to continue operating and maintain their market share against competitive challenges and rapid changes in consumer behavior (Raji et al., 2024). Retail stores must continuously respond to customer needs and gain customer loyalty by providing quality services and a favorable shopping experience.
2.2. The Amazon Retail Success Model
As the largest online shopping retailer in the world (Čirjevskis, 2023; Henaway, 2023), Amazon is one of the largest e-commerce companies in the world (and one of the three prominent examples of hybrid retailing is Amazon.com) that most retail stores around the world (Pei & Li, 2023) are interested in implementing Amazon policies show to the extent that from 2012 to 2022, some of its strategies have been given different nicknames, from “metrics culture” to “digital factory” (Altenried, 2019). Amazon has moved towards sustainability with agility in its policies, and as a successful retailer, it has been at the top of hybrid and OC companies for years and has been able to use long-term and short-term strategic tactics and techniques to solve the concerns of buyers in a unique way. In addition, since the beginning of its activity, by understanding the ability to measure the market, it has had a significant effect on customer retention.
2.3. Retail in the UAE
In 2017, Amazon started its work in the UAE by purchasing SOUQ EMIRATES website and changing it to the Amazon e-commerce platform. From the very beginning of the activity of Amazon company, the experience of customers of this retail store was made by providing customer service in order to increase the pleasure of shopping, create confidence, and in addition, the product variety and the presence of harmony distinguished (Vakhariya, 2020). By providing developed logistics infrastructure, virtual reality and augmented reality technology, these countries have provided the conditions to improve the customer experience and witness the growth of the digital economy. For example, it has taken valuable steps to become a globally recognized business centre, and several large online markets in Dubai also indicate the growth of e-commerce and retail sales (Joghee et al., 2020). It also has a progressive government program to create a live test bed for new technology ideas such as HYPERLOO (Yadav & Szpytko, 2021).
2.4. Omni channel and Mix Channel
Omni channel is a complex concept with a broad scope, leading to various academic studies that examine it from different perspectives (Saghiri et al., 2017). (Pei & Li, 2023) identified three types of hybrid retailing currently in practice: 1) representative retailing, where manufacturers sell directly to customers and pay a fee to the platform owner; 2) reseller mode, where retailers purchase products for resale to end consumers; and 3) hybrid retailing, where many online platforms operate in both agent and seller modes for manufacturers. Kembro and Norman defined Omni channel retail as the simultaneous and interactive use of different channels.
3. Background of the Study and Question
In this research, we have organized the background of the research in such a way that each of the studied studies has a direct and significant relationship with the research topic and the potential applicability of the findings to the current research. Our goal with this classification is to clarify and better understand the needs and gaps in the field of research, so that we can achieve a deeper examination of the existing challenges and opportunities and identify the strengths and weaknesses of previous researches. Therefore, we used a comprehensive and systematic search method focusing on the research objective and based on keywords related to the field of survival and sustainable success in startup retail. These keywords included “launch retail”, “Amazon retail”, “online retail”, “hybrid”, “multi-channel retail”, “sustainable success” and “strategies for retail survival”. As indicated by these keywords, the focus and scope of our review are based on a targeted and relevant article search. Search articles published between 2020 and 2024 were reviewed. This time frame was chosen to ensure inclusion of the latest perspectives, methods, theories and research results.
3.1. Success Strategies and Retail Sales
This paper (Wang, 2024) examines the sustained success factors and competitive and collaborative strategies adopted by retailers in the omnichannel retail environment and states that flexible pricing, strong branding, and global supply chain collaboration are key elements of its success. Through comprehensive analysis, this paper provides strategic guidance for OC retailers, emphasizes the importance of innovation, technology application and partnership, and provides useful experiences and insights for the sustainable development of the retail industry in the future. Another research in the same direction states that (Masyhuri, 2022) concluded that to survive in the e-retail business, incumbent online retailers must compete with others to retain their customers. This means that retail businesses must understand what key factors determine customer satisfaction in order to survive on their own terms. (Kumar & Ayodeji, 2021) identified key determinants of online retail success for start-ups, concluding that factors such as environmental conditions, government policies, competition, marketing channels, and technological infrastructure significantly affect a new entrant’s success. Understanding these determinants is essential for online retailers to thrive in a rapidly changing market. Also while identifying the determinants of online retail success and start-up companies, concluded that for a new entrant to be successful in this industry, environmental factors, government policies, competitors, marketing channels, and infrastructure and technology development It affects it and the determining factors should be known for online retailers to be able to be in this rapidly changing market. In the same direction companies (Ismail Albalushi & Naqshbandi, 2022) investigated the factors affecting the success and survival of small and medium enterprises in the Middle East. These researchers believed that knowing these factors could be the leading solution to reduce unemployment and poverty and strengthen economic growth. Therefore, they examined the internal and external factors of the companies. By doing a quantitative approach, they concluded that the educational system, changing the business culture, focusing on management skills, and improving the methods needed to create a business have an effect on the success and survival of (Ivanova, 2021), examined the innovations and basic trends of the future of retail sales and considered the supply chain, technology development, global economy, customers, and e-commerce to be effective in the level of customer loyalty of retail sales. In this regard, (Rao et al., 2021) investigated the implications of the retail industry for the survival of GCC stores after the pandemic and the retention and loyalty of their customers. They also pointed out that a comprehensive review of the business strategy is very important for retailers in this region. Competitors considered technology infrastructure, customer interaction, use of social media, supply chain with OC capabilities, and finally sustainable social responsibility as its future consequences. (Yang et al., 2022) examined the types of customers in OC and hybrid retail and designed a model that had two parts: fixed channel customers and OC type customers. We considered the results of this research. (Liu et al., 2022) showed that OC retailing helps to understand customers and provide more reliable and integrated services to consumers by providing innovative methods. It also mentioned dynamic capabilities, expansion and modification of organizational resource base (Solem et al., 2023), contextual variables, and risks.
3.2. Survival and Retail
(Masyhuri, 2022) concluded that to survive in the e-retail business, incumbent online retailers must compete with others to retain their customers. This means that retail businesses must understand what key factors determine customer satisfaction in order to survive on their own terms. This study (Karcıoğlu & Öztürk, 2022) deals with the issue of sustainable cost in the retail sector, which is an important element of the supply chain and is of particular importance in economic crises. The authors found that the continuous development of sustainability awareness in the retail sector and in particular the adoption of sustainable cost management are very important factors for business success. Also this paper (Schmidtke & Siegfried, 2022) proposes a new business model based on modern technology for retail. This article is the basis of change, modern technology. Finding innovative ways to use technologies such as NFC, artificial intelligence and robotics is seen as a key factor for sustainable success. Also Mao (2022) concluded that retail quality has a positive relationship with the survival of retail businesses in the market environment, but there is no relationship between market competition and survival (Mao, 2022).
3.3. Dos and Don’ts in Retail and Its Relationship with Survival
Grewal et al. (2021) highlighted the retention and loyalty of customers and for their satisfaction according to the competitive ecosystem, the physical atmosphere of the store, the appearance of the website, the quality and price of the product, and the management of the supply chain as important components in obtaining the best position for survival. And the prosperity of retail sales in the era of new technology were emphasized (Grewal et al., 2021). Also (Roggeveen & Sethuraman, 2020) identified customer retention as the main concern of retailers to overcome the Corona pandemic crisis. These researchers believed that it is very important to predict the prospects of retail sales and understand the factors affecting it. After their investigations, they suggested that retail stores should move towards mixed retail stores for survival, and the most important solution is to identify the behavior and needs of consumers, increase the job security of employees, and improve the physical space of the store, proper supply chain, quality and price of goods. Brand building and online interaction with customers and gaining their experiences. In this regard, The path of growth of mixed and OC retail sales before the pandemic indicates that (Beck & Rygl, 2015) did multi-channel retail classification with a systematic approach. In this structure, the ability of customers to buy at any place and at any time through channels was considered an advantage in these retail stores, while in the opinion of (Chopra, 2016), the cost-effective supply chain in response to the needs of OC retail customers and the use of hybrid structures of Both physical and online channels were required, especially in emerging markets.
Hence the authors of this research have not only examined the background of sustainable survival and success in the retail industry, but also in other research studies focusing on Amazon’s strategies for sustainable survival and success in the retail sector. It represents a comprehensive research background on the dynamics of retail survival and success, with particular emphasis on the strategies employed by Amazon Retail. In addition, the table categorizes research background checks based on their relevance to the research topic and the potential applicability of the research findings to the current study (Table 1).
Considering the conditions and challenges in the retail industry, many studies have shown that research on survival and sustainable success in start-up retails
Table 1. Literature review with considered Amazon’s strategy.
Keywords used to obtain research |
Study |
considered Amazon’s strategy |
Outcomes |
Keyword review and related to
research |
Key findings |
Retail business model innovation |
(Mostaghel
et al., 2022) |
Digitalization-driven
retail business model
innovation: Evaluation for future research trends |
Identifying areas for future research related to the
retail business model |
Retail Innovation → for survival |
The characteristics of digitization-enabled retail business model innovation. |
Multichannel
Retailing |
(Gauri et al., 2021) |
Evolution of retail formats: Past, present,
and Future |
A new customer-based framework for retailers to focus on continued
innovation and evolution |
Retailing → hybrid, multi-channel
retailing |
Different offline and online methods for
information search, purchase, and product return |
Amazon.in
Shopper Loyalty Determinants |
(Mathew & Athishu, 2021) |
Factors affecting customer loyalty among Amazon.in shoppers |
The factors that have the greatest impact on
electronic loyalty |
loyalty and
consumer →
Amazon retailing |
Understanding factors affecting online
customer loyalty in
retail |
Online Shopping Strategies for
Success |
(Warrier et al., 2021) |
Factors that Lead Amazon.com to a
Successful Online |
The factors that make
Amazon a successful online shopping platform |
Online shopping → sustainable
success |
Amazon success
factors |
Amazon retail consumers |
(West, 2019) |
Amazon Surveillance as a Service |
Economic, political, and social implications on
platforms |
Consumer retailing → amazon
company |
Monitoring is an
essential tool for
customer loyalty and sustainable survival |
Sustainability in Retailing |
(Vadakkepatt et al., 2021) |
Focus on supply chain
and contamination
reduction |
Focus on sustainability in retail, considering
economic, environmental and social resources for current and future
generations. |
Sustainability → amazon retailing |
Sustainability in
retailing requires a strategic approach |
Amazon Retail |
(Althafairi et al., 2019) |
Case study - AMAZON |
Review and analysis of
various aspects of the
company’s work process |
Customer loyalty and trust → Retail survival |
Prioritizing customer loyalty and trust for survival and
sustainable success |
has not provided a deep understanding to get rid of the problems and challenges of this sector. This study tries to fill a gap in this part of the literature. Therefore, providing the first comprehensive model for the path of survival and sustainable success in start-up retailers including hybrid and OC retailing can be useful. Therefore, according to the gap in the research literature, research questions are raised.
4. Methodology
This research employs a combined realization method. The qualitative aspect utilizes (GT) with Glaser’s approach, while the quantitative aspect tests the qualitative findings using PLS software. GT is particularly effective in marketing and management research, as these fields are consistently influenced by social, economic, and technological changes. Glaser’s approach is advantageous when information is scarce or when markets experience rapid changes and innovations, aiding in the identification of emerging trends. The integration of GT with quantitative methods and PLS software allows for the development of comprehensive theories and analysis of complex variable relationships. This method enhances the detection of direct and indirect effects and enables the testing and validation of theories through numerical data analysis. It also helps clarify relationships between variables and assess their interdependencies. Overall, this combined methodology strengthens qualitative results with quantitative evidence, improving the validity and reliability of the findings and allowing for broader generalization. Thus, utilizing this combined method is beneficial for achieving valid and reliable outcomes in this research.
The research method has been completely done in 8 steps. In the first step of the qualitative part, we first collected data and collected the required information based on the GT method with Glaser and based on the research topic. Accordingly, by identifying keywords related to the field of survival and sustainable success in retail, we conducted a specialized search. In the second step, after collecting the data and information related to the research field, we started coding the data into 3 stages. In the third step, we assessed their realization by experts and specialists in this field. In the fourth step, after evaluating the codes based on the research topic, we formulated hypotheses based on the obtained codes. In the fifth step, we designed a 77-question questionnaire using the obtained hypotheses and codes. In the sixth step, we evaluated the questionnaire based on validity and reliability. In the seventh step, we distributed the questionnaire and collected the answer sheets and analysed them using SPSS and PLS software. In the eighth step, according to the obtained results, we designed a model based on the conceptual model of GT. In Figure 1, we have shown the steps, and after that, we have explained all the steps in detail.
In management research, the “research onion” framework has been successfully used to depict the different methodological dimensions, in terms of the choice of data collection and data analysis techniques. The reason for using this tool is that: Research Onion, as a conceptual framework and a guiding tool in scientific research, makes it possible to examine the different stages of research design in a systematic and orderly manner. This structure becomes more important especially in qualitative and complex research, and in methods such as GT and in marketing research. Because it allows researchers to proceed accurately and regularly in a structured and orderly manner, examine and analyse data and extract theories from real data in this way to achieve a deeper understanding of the research topic.
The research process is as follows:
Figure 1. Research process.
Step 1-Data Identification:
The authors conducted a thorough search of English electronic databases, including Web of Science, Scopus, EBSCO, and Science Direct, as well as authoritative websites and other publishers relevant to researchers’ access to scientific resources. They initially used broad keywords such as “retailing,” “Amazon company,” “online retailing,” “hybrid and multi-channel,” “retailing during COVID-19,” and “retailing challenges,” which resulted in 3,847 articles. To refine their search, the authors employed more specific keywords pertinent to their research, including “how to succeed and survive Amazon,” “Amazon development,” “Amazon establishment,” “Amazon company strategies,” and “ways to attract and retain Amazon customers.” This focused search yielded approximately 754 relevant articles (Table 2).
To assess the theoretical saturation of the 754 data points, the authors employed an expert reviewer in management as the primary judge for screening. The initial screening prioritized criteria such as thematic relevance to the research subject and goals, as well as the articles’ titles, abstracts, methodologies, data quality, publication dates, result quality, study types (experimental, theoretical, systematic review), full-text access, and reference counts. Articles that were thematically irrelevant or lacked sufficient empirical evidence were excluded, along with older
Table 2. Review procedure.
Database |
Query |
Refrained by |
Web of
Science |
All Fields: (Retail sales), (“Amazon strategies”), (“Online retail strategies”), (“Hybrid and multi-channel”), (“Retail in the time of Covid-19”) AND (“Retail in developing countries”) > 1487 |
Amazon’s establishment - customers & buying from Amazon, “Amazon’s” and ways to attract and retain Amazon’s customers > 185 |
Scopus |
All Fields: (Retail sales…), (“Amazon company …”),
(“Online retail…”), (“Hybrid and multi-channel …”), (“Retail in the time of Covid-19”) AND (“Retail in developing countries”)
> 782 |
“Amazon’s” (development, + during the
Covid-19 pandemic+ establishment,
+ company strategies) > 224 |
EBSCI |
All Fields: (Retail sales + developed countries, (“Amazon +
strategies”), (“Online retail strategies”), (“multi-channel retail
strategies”), (“Retail sales in the time of Covid-19”) AND
(“Retail & developing countries”) > 985 |
Amazon’s establishment & development,
Amazon company strategies, ways to attract
and retain Amazon’s customers > 173 |
Science
Direct |
All Fields: (sales in countries…), (“marketing & strategies”), (“Online retail”), (“Hybrid + channel+”), (“Retail + Covid-19”) AND (“Retail challenges”) > 593 |
Amazon’s development, Amazon during the Covid-19 pandemic, Amazon’s customers & buying from Amazon, and ways to attract and retain Amazon’s customers > 176 |
Basic and general search: 3847 |
Articles: 754 |
articles or those failing to meet ethical research standards. In total, any article that clearly did not meet the eligibility criteria was discarded. Although this screening was time-consuming, it ensured the selection of high quality and valid articles for analysis. Following the initial screening, 74 articles relevant to the research field were identified as meeting the necessary criteria. For validation and further investigation, a secondary screening was conducted, where the same reviewer thoroughly read each full article and documented the content and results in a matrix. No additional articles were excluded during this phase. Ultimately, through the process of theoretical saturation and exclusion criteria, 74 suitable articles were identified and analyzed, demonstrating our commitment to the quality and validity of the results.
Step 2-Data Coding:
After collecting relevant and targeted data with the research topic, they were identified and coded with an inductive perspective in 3 stages (open coding, central coding and selective coding) based on the factors affecting survival and sustainable success in Amazon retail. Based on Glaser’s approach and coding rules, we identified 114 open codes in the first stage of coding. These codes represent basic concepts and main topics. Then, in the second stage of coding, we identified 20 core codes. At this stage, we reached a deeper understanding of the data and how the concepts are related. Then, in the third stage of coding, six selected codes were identified. In the third stage, the authors of this research, according to the main topic of the research, looked for concepts related to the topic and problem of the research (Table 3).
Table 3. An example of the open coding identification stage.
Reference |
Text excerpt |
code Open |
(Rikap, 2022) |
… New technologies broadening Amazon’s intellectual monopoly concentrate around the industry 4.0, in particular terms related to machine learning |
C7: New technologies\ intellectual monopoly
concentrates around the industry |
(Alshmrani, 2021) |
… Augment proficiency to coordinate or surpass client needs… |
C77: proficiency to coordinate or surpass client needs |
(Alshmrani, 2021) |
… Emphasizes customer feedback …
regularly and always updates... |
C32: emphasizes customer feedback experience C37: regularly and always updates his customers |
(Zana et al., 2018) |
Amazon... went on to create an alliance with the rival Borders. |
C21: alliance with the rival |
(Jindal et al., 2021) |
…to these initiatives for faster delivery, Amazon is also increasing its brick-and-mortar presence… |
C65: faster delivery |
(Sadik, 2018) |
... massive amount of data and processing it in the form
of reports that help decision makers… |
C97: Providing a huge amount of information
for strategic decisions |
Step 3-Evaluation of the extracted codes: Ten university professors and ten managers with experience in the field of retail business in the UAE carefully reviewed and evaluated the obtained codes according to their views and attitudes towards survival strategies as well as sustainable success in retail stores. Using their knowledge and experience, these experts have confirmed that the classification of codes based on the GT method has high precision and accuracy and there is a good agreement between the codes. After this stage, by applying the comparative method to compare and adapt information and different categories, the necessary conclusions and confirmations were obtained, which shows the efficiency of these methods in the field under investigation.
Step 4-Compilation of study Hypotheses
The extraction of hypotheses in the GT research method is done directly and completely based on data and is considered as one of the basic and vital techniques in the formulation of hypotheses. This process clearly indicates not using preconceived ideas or theories. In fact, in this method, the researcher collects data and then codes these data in three steps to formulate hypotheses. These coding steps include in-depth analysis and careful examination of the data in order to extract different meanings and patterns. The hypotheses that are finally formed based on these data and coding are designed in a way that can be tested and measured accurately. This feature ensures that the hypotheses are not only valid and scientific, but also can be comprehensively and applied in future researches. These hypotheses are formulated based on coded data.
H1) Causal conditions are effective on the core categories.
H2) the Core category is effective on the strategies by considering the intervening factors and background factors.
H3) Strategies are effective on consequence.
Step 5-Questionnaire design
A questionnaire consisting of 77 questions from the extracted codes, was designed to answer the 1 question and 3 hypotheses of the previous stage and to design the desired model and was approved by the experts. After designing the questionnaire, in the first stage, the experts (in the statistical population of the quantitative sector) confirmed the validity and reliability of the model and the questionnaire. The questionnaire was distributed among 30 people from the statistical population and the reliability of the questionnaire was obtained through Cronbach’s alpha coefficient of 0.81.
Step 6-Statistical society for measuring the validity and reliability of the questionnaire and Distribution
The statistical population of this research is divided into 2 parts.
The first part: the questionnaire was created based on the extracted codes. The first part of the process involved ensuring the reliability and validity of the questionnaire. To achieve this, 30 university professors and marketing managers, who are considered experts in the field of retail, were involved in the process. They were chosen for their expertise and experience in the retail industry. Once the questionnaire was designed, it was distributed to the selected experts, and their responses were collected. This step aimed to gather feedback and insights from individuals with significant knowledge in the retail sector. Following the collection of responses, the reliability of the questionnaire was assessed using a statistical measure known as Cronbach’s alpha. The calculated value for Cronbach’s alpha was 0.81, indicating a high level of internal consistency within the questionnaire. This value suggests that the questions in the questionnaire were reliable and consistent in measuring the intended constructs.
The Second part: After validating the questionnaire, the authors considered the questionnaire in the statistical community that included a diverse range of individuals such as university professors, owners, managers, and employees of mixed B2C retail stores and Omni channel in the UAE. The statistical community was carefully selected with the aim of relating these people to the research topic. Since the subject of the research is in the field of retail sales, all the individuals who were selected are related and actively involved in the field of retail sales. This selection was made because these individuals have a deep understanding of how a retail store operates and are therefore able to provide valuable insights and perspectives related to the research topic.
Step 7-Analysis of Questionnaire Data
Since this is a specialized academic research, all questionnaire respondents are required to hold at least a bachelor’s degree. This ensures that they have a proper understanding of the research topic and questions. Additionally, we selected reputable retail stores with experience in retail sales, and required their employees to have at least a bachelor’s degree. As a result, the statistical population for this research consisted of individuals with at least a bachelor’s degree. Due to the unknown number of the population, an available sampling method was chosen. The number of available people was 421. According to Cochran’s formula, the sample size for the quantitative portion of the study was determined to be 385 individuals. Considering account for the questionnaire return rate, 393 questionnaires were distributed. The data was analysed using PLS software.
Step 8-Grounded model design
The research model was designed with the GT approach and showed the mutual relationship of meaning in the perception of subjects and their actions.
5. Results Report
The demographic questions that were taken into account in the questionnaire comprised five specific queries. These questions aimed to gather essential information about the respondents and included inquiries regarding their gender, age, level of education, job position, and work experience. Each of these aspects was carefully selected to provide a comprehensive overview of the demographic characteristics of the participants involved in the study. By including these five questions, the questionnaire sought to ensure that a broad range of demographic factors was considered, thereby allowing for a more nuanced understanding of the data collected (Table 4).
Table 4. Demographic information of participants.
|
Group |
Frequency |
Percentage Frequency |
Gender |
Female |
54 |
13.74 |
Male |
339 |
86.26 |
Age |
20 - 30 |
42 |
10.69 |
30 - 40 |
143 |
36.39 |
40 - 50 |
170 |
43.26 |
50 - 60 |
24 |
6.11 |
up to 60 |
14 |
3.56 |
Education |
Bachelor |
172 |
43.77 |
Master |
151 |
38.42 |
PHD |
70 |
17.81 |
Position |
Expert |
19 |
4.83 |
Owner |
20 |
5.09 |
Manager |
10 |
2.54 |
Head of Department |
163 |
41.48 |
Responsible |
88 |
22.39 |
Operator |
93 |
23.66 |
Work experience |
1 - 10 year |
104 |
26.46 |
11 - 20 year |
127 |
32.32 |
21 - 30 year |
98 |
24.94 |
31 - 40 year |
52 |
13.23 |
up to 41 year |
12 |
3.05 |
Total |
393 |
100 |
5.1. Word Abbreviations
To identify the codes in the statistical part, we put all the selective codes and axial codes in the table as abbreviated (Table 5).
Table 5. Table of word abbreviations.
Class |
Selective code |
abbreviation |
Axial code |
abbreviation |
Causal
conditions |
Market sensing
capability |
MSC |
Market needs |
MN |
Funding |
F |
Determining the persona of the business |
DPB |
Warehouse of physical products |
WPP |
Main
category |
customer
loyalty |
CL |
Good quality and reasonable price |
GQRP |
Respecting cultural and environmental laws |
RCEL |
Trust, safety, and security of customers, employees and third parties |
TSSCE |
Stakeholder experience management |
SEM |
Strategy |
Market
development |
MD |
Expansion of innovation and technology |
EIT |
Variety of products/services |
VPS |
Commercial-economic influence at the global level |
CEI |
Effective participatory management with stakeholders |
EPMS |
Background
factors |
environmental
factors |
EF |
Social variables |
SV |
Environmental variables |
EV |
Economic-legal variables |
ELV |
Intervening
factors |
unsystematic
risks |
UR |
Advancement of technology |
AT |
Changing the conditions of the beneficiaries |
CCB |
Competitors’ policy |
CP |
Consequence |
collaborative
foresight |
CF |
Futurism |
F |
Completing the market with stakeholders |
CMS |
Entrepreneurship |
E |
5.2. Checking the Validity of the Research Conceptual Model
Considering that the variables of the conceptual model have components, such models are called higher-order models. In such models, the low-order model should be evaluated first, and then the second-order model should be implemented after confirming the indicators. A separate two-step method has been used to check higher-order models. In a separate two-stage approach, in the first stage; all the higher-order constructs are converted to the first order and the hypothetical relationships between the dimensions of the higher-order variables are drawn with each other. The implementation of the second stage and checking the validity and reliability of higher-order constructs as well as checking the relationships between endogenous and exogenous variables are discussed. Figure 2 shows the general model of the research in the first order and without the structures of higher levels.
Figure 2. Conceptual model in the first order state.
5.3. Evaluation of the Validity and Reliability of the Measurement
Indicators of the Model in the First Order
In this part, the validity and reliability of the measurement indicators of the structures are examined and tested in the first place. In evaluating reflective measurement models, indicators such as internal consistency, convergent validity, and divergent validity need to be examined.
5.4. Examining the Internal Stability and Convergent Validity of
First-Order Constructs
Internal stability was measured by Cronbach’s alpha and composite reliability indices, and the value of these two indices was higher than 0.7, which can be claimed to have the necessary internal stability.
Convergent validity is the degree of convergence of the construct to explain the variance of its indicators. The reliability criteria of the indicators and the extracted average variance are used to evaluate the convergent validity. If the factor load of the items is greater than 0.708, which indicates that the structure explains more than 50% of the variance of the item, it can be concluded that the index has the necessary reliability. On the other hand, in order to confirm the convergent validity of a structure, the value obtained for the extracted average variance index must be greater than 0.50 (Table 6).
Table 6. The results of examining the internal stability and convergent validity of first-order constructs.
Component |
Indicator |
Loading |
Cronbach’s Alpha |
Composite
Reliability |
Average Variance Extracted (AVE) |
MN |
MN1 |
0.849 |
0.841 |
0.893 |
0.677 |
MN2 |
0.843 |
MN3 |
0.782 |
MN4 |
0.816 |
F |
F1 |
0.814 |
0.830 |
0.887 |
0.662 |
F2 |
0.819 |
F3 |
0.823 |
F4 |
0.800 |
DPB |
DPB1 |
0.863 |
0.804 |
0.884 |
0.718 |
DPB2 |
0.842 |
DPB3 |
0.837 |
WPP |
WPP1 |
0.844 |
0.792 |
0.878 |
0.706 |
WPP2 |
0.848 |
WPP3 |
0.828 |
GQRP |
GQRP1 |
0.828 |
0.776 |
0.870 |
0.690 |
GQRP2 |
0.832 |
GQRP3 |
0.832 |
RCEL |
RCEL1 |
0.828 |
0.829 |
0.887 |
0.661 |
RCEL2 |
0.807 |
RCEL3 |
0.820 |
RCEL4 |
0.798 |
TSSCE |
TSSCE1 |
0.826 |
0.787 |
0.876 |
0.702 |
TSSCE2 |
0.858 |
TSSCE3 |
0.828 |
SEM |
SEM1 |
0.818 |
0.774 |
0.869 |
0.689 |
SEM2 |
0.817 |
SEM3 |
0.855 |
SV |
SV1 |
0.837 |
0.875 |
0.909 |
0.666 |
SV2 |
0.826 |
SV3 |
0.800 |
SV4 |
0.787 |
SV5 |
0.831 |
EV |
EV1 |
0.837 |
0.804 |
0.885 |
0.719 |
EV2 |
0.858 |
EV3 |
0.848 |
ELV |
ELV1 |
0.823 |
0.774 |
0.869 |
0.688 |
ELV2 |
0.834 |
ELV3 |
0.831 |
AT |
AT1 |
0.848 |
0.855 |
0.902 |
0.697 |
AT2 |
0.841 |
AT3 |
0.821 |
AT4 |
0.829 |
CCB |
CCB1 |
0.813 |
0.839 |
0.893 |
0.675 |
CCB2 |
0.818 |
CCB3 |
0.847 |
CCB4 |
0.808 |
CP |
CP1 |
0.816 |
0.848 |
0.898 |
0.687 |
CP2 |
0.846 |
CP3 |
0.829 |
CP4 |
0.825 |
EIT |
EIT1 |
0.819 |
0.874 |
0.908 |
0.664 |
EIT2 |
0.804 |
EIT3 |
0.818 |
EIT4 |
0.825 |
EIT5 |
0.809 |
VPS |
VPS1 |
0.840 |
0.851 |
0.899 |
0.690 |
VPS2 |
0.835 |
VPS3 |
0.828 |
VPS4 |
0.821 |
CEI |
CEI1 |
0.838 |
0.811 |
0.888 |
0.726 |
CEI2 |
0.869 |
CEI3 |
0.848 |
EPMS |
EPMS1 |
0.811 |
0.867 |
0.903 |
0.652 |
EPMS2 |
0.797 |
EPMS3 |
0.810 |
EPMS4 |
0.811 |
EPMS5 |
0.806 |
Fu |
Fu1 |
0.855 |
0.818 |
0.892 |
0.733 |
Fu2 |
0.866 |
Fu3 |
0.847 |
CMS |
CMS1 |
0.846 |
0.815 |
0.890 |
0.730 |
CMS2 |
0.847 |
CMS3 |
0.870 |
E |
E1 |
0.837 |
0.870 |
0.911 |
0.719 |
E2 |
0.853 |
E3 |
0.854 |
E4 |
0.847 |
5.5. Investigating the Divergent Validity of First-Order Constructs
Divergent validity expresses the fact that to what extent a construct is different from other constructs by empirical standards. Therefore, the existence of divergent validity means that it is a unique construct and includes phenomena that are not represented by other constructs in the model.
In order to measure divergent validity, Fornell-Larker criteria, cross factor loadings, and HTMT index ratio are used. The Fornell-Larker criterion states that it has a divergent validity structure when the second root of the extracted average variance of each of the latent variables is greater than its correlation value with other latent variables. Based on the matrix of cross factor loadings. A construct has the necessary divergent validity when the value of the factor loadings of a latent variable is greater than all of its reciprocal factor loadings. The HTMT index is defined as the average value of the correlations of the indicators among the structures compared to the (geometric) average correlation for the measurement indicators of the same structure. The value of the HTMT index should be less than 0.9 in order to claim that a construct has the required divergent validity.
According to the Fornell-Larker criterion, the elements marked in a different color in the table represent the mean square of the extracted variance, all of which exceed the correlation values of the target variable with other variables. Each index has the highest factor loading for the variable it measures, as indicated by the cross-factor loadings. Additionally, the HTMT ratio shows that the correlation of each variable with others is below the threshold of 0.90. Thus, since all three indicators of divergent validity remain within the established limits, the first-order constructs demonstrate adequate divergent validity.
5.6. Evaluation of Validity and Reliability of Measurement Models
in the Second Order
Considering that the validity and reliability of the measurement models of the conceptual model of the research were confirmed in the first order, therefore, in this part, considering that the variables of the research are the second order type, the validity, and reliability of the measurement models of the order Second, be examined and analyzed. As mentioned before, in the separate two-step approach method, the scores obtained for the variables in the second order are used for the higher-order models. Therefore, the structural model according to these grades is in the form of Figure 3. According to this diagram, the validity and reliability of the measurement models are evaluated in the second order, and if the structural part of the conceptual model of the research is confirmed, it is analyzed.
According to Figure 3, the validity and reliability of the measurement models were evaluated in the second order. Considering that the above items have been confirmed, the structural part of the conceptual model of the research is analyzed.
Figure 3. Evaluated in the second order.
5.7. Evaluation of the Measurement Model of Structures in the
Second Order
In this part, internal consistency, convergent and divergent validity of the second order are examined.
5.8. Examining the Internal Stability and Convergent Validity of
Second-Order Constructs
According to the results of Table 7, the second-order constructs have the necessary internal stability and convergent validity.
5.9. Investigating the Divergent Validity of Second-Order
Constructs
Similar to the steps carried out in the divergent validity investigation for the structures in the first order, in the second order validity investigation it is also necessary to consider the Fornell-Larker indices, cross-factor loadings, and the HTMT correlation ratio of the divergent validity of the second order constructs (Table 8).
Table 7. The results of the test of internal consistency and convergent validity of the second-order constructs.
Variable |
Component |
Loading |
Cronbach’s
Alpha |
Composite
Reliability |
Average Variance Extracted (AVE) |
Market Sensing
Capability |
MN |
0.779 |
0.817 |
0.880 |
0.646 |
F |
0.804 |
DPB |
0.836 |
WPP |
0.796 |
Customer Loyalty |
GQRP |
0.801 |
0.806 |
0.873 |
0.633 |
RCEL |
0.792 |
TSSCE |
0.822 |
SEM |
0.766 |
Environmental Factors |
SV |
0.841 |
0.796 |
0.880 |
0.710 |
EV |
0.828 |
ELV |
0.858 |
Unsystematic Risks |
AT |
0.842 |
0.830 |
0.898 |
0.746 |
CCB |
0.869 |
CP |
0.879 |
Market Development |
EIT |
0.866 |
0.882 |
0.919 |
0.739 |
VPS |
0.861 |
CEI |
0.837 |
EPMS |
0.874 |
Collaborative Foresight |
Fu |
0.884 |
0.853 |
0.911 |
0.773 |
CMS |
0.858 |
E |
0.896 |
Table 8. The results of examining the divergent validity of the second-order constructs.
Fornell-Larcker Criterion |
|
Market Sensing Capability |
Customer
Loyalty |
Environmental Factors |
Unsystematic Risks |
Market
Development |
Collaborative Foresight |
Market Sensing
Capability |
0.804 |
|
|
|
|
|
Customer
Loyalty |
0.681 |
0.795 |
|
|
|
|
Environmental
Factors |
0.487 |
0.593 |
0.843 |
|
|
|
Unsystematic
Risks |
0.502 |
0.572 |
0.563 |
0.864 |
|
|
Market
Development |
0.605 |
0.732 |
0.704 |
0.716 |
0.860 |
|
Collaborative
Foresight |
0.478 |
0.608 |
0.605 |
0.614 |
0.765 |
0.879 |
Cross Loadings |
|
Market Sensing Capability |
Customer
Loyalty |
Environmental Factors |
Unsystematic Risks |
Market
Development |
Collaborative Foresight |
MN |
0.779 |
0.535 |
0.378 |
0.404 |
0.478 |
0.382 |
F |
0.804 |
0.514 |
0.398 |
0.399 |
0.495 |
0.399 |
DPB |
0.836 |
0.570 |
0.387 |
0.368 |
0.472 |
0.352 |
WPP |
0.796 |
0.567 |
0.405 |
0.443 |
0.502 |
0.405 |
GQRP |
0.523 |
0.801 |
0.464 |
0.473 |
0.593 |
0.515 |
RCEL |
0.584 |
0.792 |
0.496 |
0.473 |
0.587 |
0.474 |
TSSCE |
0.559 |
0.822 |
0.459 |
0.441 |
0.567 |
0.471 |
SEM |
0.497 |
0.766 |
0.464 |
0.430 |
0.580 |
0.475 |
SV |
0.416 |
0.503 |
0.841 |
0.519 |
0.624 |
0.528 |
EV |
0.371 |
0.490 |
0.828 |
0.433 |
0.568 |
0.500 |
ELV |
0.444 |
0.505 |
0.858 |
0.466 |
0.585 |
0.501 |
AT |
0.397 |
0.462 |
0.506 |
0.842 |
0.589 |
0.507 |
CCB |
0.438 |
0.488 |
0.474 |
0.869 |
0.616 |
0.534 |
CP |
0.462 |
0.529 |
0.481 |
0.879 |
0.649 |
0.549 |
EIT |
0.539 |
0.642 |
0.608 |
0.635 |
0.866 |
0.665 |
VPS |
0.529 |
0.629 |
0.592 |
0.611 |
0.861 |
0.660 |
CEI |
0.480 |
0.592 |
0.567 |
0.576 |
0.837 |
0.635 |
EPMS |
0.531 |
0.650 |
0.652 |
0.639 |
0.874 |
0.669 |
Fu |
0.421 |
0.533 |
0.542 |
0.565 |
0.672 |
0.884 |
CMS |
0.401 |
0.478 |
0.517 |
0.495 |
0.628 |
0.858 |
E |
0.437 |
0.588 |
0.538 |
0.557 |
0.713 |
0.896 |
Heterotrait-Monotrait Ratio (HTMT) |
|
Market Sensing Capability |
Customer
Loyalty |
Environmental Factors |
Unsystematic Risks |
Market
Development |
Collaborative Foresight |
Market Sensing
Capability |
|
|
|
|
|
|
Customer
Loyalty |
0.837 |
|
|
|
|
|
Environmental
Factors |
0.604 |
0.739 |
|
|
|
|
Unsystematic Risks |
0.608 |
0.697 |
0.691 |
|
|
|
Market
Development |
0.713 |
0.867 |
0.838 |
0.836 |
|
|
Collaborative
Foresight |
0.572 |
0.731 |
0.734 |
0.728 |
0.880 |
|
According to the results of Table 8, the second-order constructs have the necessary divergent validity.
5.10. Evaluation of the Structural Part of the Research Conceptual
Model
Considering that the evaluation of the measurement models provided satisfactory results, the next step is the evaluation of the structural model. The standard evaluation criteria that should be considered at this stage include a coefficient of determination (R2), effect sizes (f2), validated redundancy assessment based on blindfolding method (Q2), and significance and size of path coefficients.
Structural model coefficients for relationships between constructs are derived from the estimation of a series of regression equations. Before assessing the structural relationships, collinearity should be checked to ensure that the regression results are not biased. Values above 5 for VIF indicate the existence of a collinearity problem between predictor constructs. The results related to the structural model collinearity test are reported in Table 9, according to this table, the obtained values are at a lower level than the value of five.
Table 9. VIF values of the structural model.
Latent Variable |
Market Sensing Capability |
Customer
Loyalty |
Environmental Factors |
Unsystematic Risks |
Market
Development |
Collaborative Foresight |
Market Sensing Capability |
- |
1.000 |
- |
- |
- |
- |
Customer Loyalty |
- |
- |
- |
- |
1.767 |
- |
Environmental Factors |
- |
- |
- |
- |
1.741 |
- |
Unsystematic Risks |
- |
- |
- |
- |
1.678 |
- |
Market Development |
- |
- |
- |
- |
- |
1.000 |
Collaborative Foresight |
- |
- |
- |
- |
- |
- |
If the intervening structures do not have collinearity problems, the next step is to check the determination coefficient values for the endogenous structures. The coefficient of determination measures the variance that is explained within each of the endogenous constructs, and in other words, it is a measure to measure the explanatory power of the model. The coefficient of determination is between 0 and 1, and as a general guideline, the values of 0.75, 0.50, and 0.25 for the coefficient of determination are considered significant, moderate, and weak, respectively. According to Table 10, the values of the coefficient of determination of endogenous variables in the structural model are in the range of 0.464 to 0.718, which according to the mentioned instructions, the explanatory power of the model is at an average level.
Table 10. Determining coefficients of the structural model.
Endogenous Variable |
R Square |
Customer Loyalty |
0.464 |
Market Development |
0.718 |
Collaborative Foresight |
0.585 |
Researchers can also examine the effect of removing a specific predictive structure on the value of the coefficient of determination of the endogenous variable, which is referred to as the effect size f2 and is a value greater than the size of the path coefficient. Quoted by Cohen in 1988 and as a rule of thumb; values higher than 0.02, 0.15, and 0.35 represent small, medium, and large values for this index, respectively.
According to the results of Table 11, the effect size of the predictor variables is in the range of 0.182 to 1.409, which shows that the predictor variables have a moderate to high effect on their respective endogenous variables.
Another tool that is used to evaluate the prediction accuracy of the path model is the calculation of Geiser and Stone’s Q2 value. As a guideline, Q2 values should be greater than zero for an endogenous structure to indicate the accuracy of the structural model prediction of that structure. Generally, Q2 values higher than 0,
Table 11. Effect size values f2.
Latent Variable |
Market Sensing Capability |
Customer
Loyalty |
Environmental Factors |
Unsystematic Risks |
Market
Development |
Collaborative Foresight |
Market Sensing Capability |
- |
0.865 |
- |
- |
- |
- |
Customer Loyalty |
- |
- |
- |
- |
0.258 |
- |
Environmental Factors |
- |
- |
- |
- |
0.182 |
- |
Unsystematic Risks |
- |
- |
- |
- |
0.250 |
- |
Market Development |
- |
- |
- |
- |
|
1.409 |
Collaborative Foresight |
- |
- |
- |
- |
- |
- |
0.25, and 0.50 indicate small, medium, and large predictive power of the path model, respectively (Table 12).
Table 12. The predictive power values of Q2.
Endogenous Variable |
SSO |
SSE |
Q2 (=1 − SSE/SSO) |
Customer Loyalty |
1572.000 |
1116.957 |
0.289 |
Market Development |
1572.000 |
746.352 |
0.525 |
Collaborative Foresight |
1179.000 |
651.489 |
0.447 |
Equal to Table 12, the Q2 value obtained for all endogenous variables is higher than 0, as can be seen, these values are in the range of 0.289 to 0.525, which shows the predictive power of the predictor variables. Between these endogenous variables are at a medium to high level.
In Table 13, the path coefficients and the significance of these coefficients are reported.
Table 13. Path coefficients and significant numbers of the structural model.
Paths |
Direct Effects |
β |
T statistics |
P values |
Market Sensing Capability -> Customer Loyalty |
0.681 |
19.487 |
0.000 |
Customer Loyalty -> Market Development |
0.358 |
9.213 |
0.000 |
Environmental Factors -> Market Development |
0.299 |
9.398 |
0.000 |
Unsystematic Risks -> Market Development |
0.343 |
11.110 |
0.000 |
Market Development -> Collaborative Foresight |
0.765 |
20.030 |
0.000 |
Paths |
Indirect Effects |
β |
T statistics |
P values |
Market Sensing Capability -> Customer Loyalty -> Market Development |
0.244 |
6.905 |
0.000 |
Customer Loyalty -> Market Development -> Collaborative Foresight |
0.274 |
8.059 |
0.000 |
Market Sensing Capability -> Customer Loyalty -> Market Development -> Collaborative Foresight |
0.187 |
6.330 |
0.000 |
Environmental Factors -> Market Development -> Collaborative Foresight |
0.228 |
7.843 |
0.000 |
Unsystematic Risks -> Market Development -> Collaborative Foresight |
0.263 |
8.985 |
0.000 |
Market Sensing Capability -> Customer Loyalty -> Market Development |
0.244 |
6.905 |
0.000 |
Customer Loyalty -> Market Development -> Collaborative Foresight |
0.274 |
8.059 |
0.000 |
Market Sensing Capability -> Customer Loyalty -> Market Development -> Collaborative Foresight |
0.187 |
6.330 |
0.000 |
Environmental Factors -> Market Development -> Collaborative Foresight |
0.228 |
7.843 |
0.000 |
Unsystematic Risks -> Market Development -> Collaborative Foresight |
0.263 |
8.985 |
0.000 |
Paths |
Total Effects |
β |
T statistics |
P values |
Market Sensing Capability -> Customer Loyalty |
0.681 |
19.487 |
0.000 |
Market Sensing Capability -> Market Development |
0.244 |
6.905 |
0.000 |
Market Sensing Capability -> Collaborative Foresight |
0.187 |
6.330 |
0.000 |
Customer Loyalty -> Market Development |
0.358 |
9.213 |
0.000 |
Customer Loyalty -> Collaborative Foresight |
0.274 |
8.059 |
0.000 |
Environmental Factors -> Market Development |
0.299 |
9.398 |
0.000 |
Environmental Factors -> Collaborative Foresight |
0.228 |
7.843 |
0.000 |
Unsystematic Risks -> Market Development |
0.343 |
11.110 |
0.000 |
Unsystematic Risks -> Collaborative Foresight |
0.263 |
8.985 |
0.000 |
Market Development -> Collaborative Foresight |
0.765 |
20.030 |
0.000 |
According to Table 13, the significant numbers of all assumed relationships in the conceptual model of the research are at a level higher than 1.96 and it shows that the defined relationships between the variables are all significant. Therefore, considering that all the indicators examined by the structural model are at a favorable level, the structural model of the research has the necessary validity.
Figure 4. GT model resulting from coding.
6. Conclusion
The model design results suggest that sustainable success in mixed and Omni channel retailing relies on key market-sensing capabilities, including understanding market needs, securing funding, defining the business persona, and managing physical product warehouses. Customer loyalty emerges as the most critical factor for retailers, influenced by quality, pricing, respect for cultural and environmental values, as well as trust, safety, and security for customers, employees, and third parties. Effective experience management and stakeholder involvement further strengthen customer loyalty.
Since retail sales grow in the context of environmental factors including social variables, environmental variables, and economic-legal variables, they always face unsystematic risks such as technological progress, changes in the conditions of beneficiaries, and competitors’ policies, which are necessary to develop a suitable strategy for customer loyalty. They should be given a suitable answer. These risks are one of the reasons why some retailers experience failure or repeat an experience several times.
Therefore, it is suggested to choose a market development strategy with the categories of innovation and technology expansion policies, diversity of products/services, commercial-economic influence at the global level, and effective collaborative management with stakeholders in the path of retail sales maturity. Because by using it, combined and all-channel retails will be able to achieve collaborative foresight and experience futurism, sustainable competitive advantage, and entrepreneurship in a special way. However, it should be kept in mind that the way to respond to this type of situation is done as necessary and depending on the situation.
7. Discussion
The world is moving towards economic prosperity (Sun et al., 2023), even if some retailers are still mourning the loss of their economic opportunities. Most businesses believe that “the world is your oyster” and this is a popular saying to encourage people to pursue opportunities regardless of geographic boundaries and build their lives as they desire (Batrancea, 2023).
This article is the result of the adaptation and review of authentic data published by Amazon Company with mixed and OC retailing in the UAE as one of the emerging economies. The most important contribution of this study lies in its innovative approach, which can be a way to understand the complex dynamics of retail maturity. In addition, the validity and reliability of the research findings are guaranteed by combining both quantitative and qualitative methods, and the GT approach further strengthens the power of the proposed final model (Guetterman et al., 2019).
A review of past researches showed that the topic of this research focused on sustainable survival and success in the UAE as an emerging economy has not been investigated so far, which shows the value and originality of this research. The resulting codes from the research process highlight the unique characteristics and challenges of the retail landscape in these economies, which are summarized in the research model. Hence, this study proposes a new model for understanding the evolution of hybrid retailing and OC practices and acts as a bridge for the gap between theoretical frameworks and practical implementation in the retail sector.
Sustainable development deals with meeting the needs of the present without compromising the ability of future generations to meet their own needs (Faccia et al., 2023), and it needs to identify the factors influencing the start-up and non-brand retail sales conditions that affect the SDGs. Also, find solutions to overcome these conditions in order to grow them by using benchmarking of successful companies in the form of an integrated model.
Retail sales in the UAE have made amazing progress due to the high security of investment, the growth of entrepreneurship, and the application of Porter’s diamond model in the field of international competition (Bouchra & Hassan, 2023). Strong infrastructure in the UAE, including the creation of 20-minute smart cities, the possibility of experiencing unprecedented innovations in technology (Faccia et al., 2023) and the expansion of the variety of shopping methods conditions show that the chances of success of mixed and OC retails are more than The past has increased.
The ability of business owners to sustain and survive depends on the level of information they possess as well as their willingness to participate in the development process. As many brands owe their success to Amazon’s experience, for new retailers looking to establish a strong presence in the market, Amazon’s approach is inspiring. Amazon has revolutionized the retail industry by effectively using a combination of online and offline channels to provide customers with a seamless shopping experience.
Researchers believe that understanding the current landscape of new, non-brand retail stores and the factors influencing their success requires a comprehensive examination of Amazon, which has not been thoroughly studied from multiple perspectives. This article presents an analysis based on authentic data from Amazon, focusing on mixed and Omni channel B2C retail sales in the UAE, an emerging economy, to explore the factors contributing to the success of these retail sales through a combined research approach.
The result of this study is similar to the research findings of many researchers such as (Bijmolt et al., 2021; Čirjevskis, 2023; Fuxman et al., 2022; Gao & Huang, 2021; Ismail Albalushi & Naqshbandi, 2022; Ivanova, 2021; Kumar & Ayodeji, 2021; Liu & Lu, 2023; de Sousa et al., 2021).
New and unbranded B2C retailers can be inspired by the model presented in this article to create and achieve integration between offline and online shopping channels in a seamless and attractive way with the aim of creating a comprehensive OC experience for customer retention and loyalty and improving acquisition processes and be inspired by your work. As long as retailers are willing to position themselves for growth and success in the ever-evolving retail landscape, the model derived from the present research provides them with valuable practical insights.
The consequence of maintaining customer loyalty and using the market development strategy is collaborative foresight. In light of that, futurism, gaining competitive advantage and entrepreneurship will be realized and the rapid growth of combined and OC retail sales under the shadow of technology-based infrastructure development in the UAE is a guarantee of playing the role of the market leader in the Persian Gulf retail industry. In addition, adopting strategies and applying it correctly and timely will be a sure solution for efficient operations in the retail industry in the current situation.
Strategies are influenced by competition and internal factors, which mutually affect one another (Apriani et al., 2022; Ferdinand & Ciptono, 2022; Mweemba et al., 2022) and provide indirect benefits, such as enhancing the financial literacy of retail managers, a crucial early step in any career (Ambuli, 2022; Chen & Chen, 2023; Liu & Lu, 2023; Mihalčová et al., 2014; Thapa & Nepal, 2015). Our arguments draw on the resource-based view (RBV) theory, emphasizing that retailers must possess essential resources to gain competitive advantage and improve performance.
On the other hand, the significant matter of competitive intelligence is recommended to be considered throughout every phase of a business’s operations. This is due to the fact that competitive intelligence has the potential to function as a catalyst across all sectors, driving innovation and enabling businesses to respond effectively to market dynamics. By integrating competitive intelligence into their strategies, companies can enhance their decision-making processes and improve their overall performance in the competitive landscape (Kazemi & Soltani, 2024).
Retail business managers in emerging economies possess a strong vision for the future and can achieve significant success through sustainable competitive advantage and entrepreneurial growth. Additionally, adopting a market development strategy for combined and Omni channel retail sales, while considering environmental actions and non-seismic risks, is an effective solution for addressing retail store owners’ and managers’ concerns about customer loyalty. This clarity enables them to better understand their path toward maturity and success.
8. Research Limitations and Outlook
The authors encountered some limitations in conducting this study and tried to overcome them largely by developing implementation methods. 1) The main limitation of this study was the diversity of emerging economies. Because emerging economies include a wide range of countries with different levels of development, cultural backgrounds, and economic structures, and designing a model that can explain the diverse nature of these economies was complex and required a deep understanding of the specific fields under investigation. 2) This research has been done in the field of Amazon retail and the specific position of mixed and OC retail in the UAE. Although the researchers tried to compare the grounded model with the known cases in emerging economies, this was not possible due to the lack of valid data and limited the generalization of the findings. To overcome this limitation, a mixed method was used. However, it may not be directly applicable to other emerging economies. 3) Limited access to data, the impossibility of collecting data systematically, and the limited number of previous researches was one of the primary challenges of the authors, which prolonged the time of data collection, and due to the dynamism and complexity of emerging economies, it may have limited the scope and depth of research. In addition, since only English articles were analyzed due to the proficiency of the authors, language barriers prevented effective data collection and may have affected the accuracy and comprehensiveness of the model. In response to this limitation, researchers used diverse and comprehensive data in English. 4) The statistical population of the research was one of the countries of the Middle East, and it is possible that there is an interpretation bias due to the GT approach as an inductive research approach. The researchers overcame this limitation by using a quantitative approach.
According to the limitations of this study and the dynamics of emerging economies, retailers should be careful in applying strategies according to the needs, purchasing patterns, and behavior of consumers, regulations, and environmental factors, and after identifying business risks, develop the market. In this direction, combined and OC retailers would do well to consider the role of e-commerce and mobile payments, among other transformative technological advancements with regard to consumer personality. Because the modernization of the retail sector and sustainable growth without adopting innovative business models, supply chain management techniques, customer interaction strategies, and marketing approaches are very risky. Other researchers are suggested to study the role of ideal consumption and increasing disposable income. Also, considering the challenges and opportunities facing small and independent retailers on the path of maturity in emerging economies and considering the impact of global trends such as green innovation, and social responsibility, provide methods for retailers to use their strengths, such as localization, personalized service, community engagement, and other industry stakeholders to compete with larger retail chains.
Acknowledgements
We sincerely thank all the respected professors and business owners who helped us with their guidance in the process of this research. Words cannot express the depth of our gratitude to each and every one of you. Your drive, dedication, and unwavering cooperation have truly been the driving force behind this work. Not only have you consistently demonstrated your skills and expertise, but you have also done so with grace and humility. It is necessary to appreciate the countless sacrifices made in this way.
Funding
We, the authors of this research, have not received any financial support from any organization or institution. We have decided to conduct this research solely based on our expertise and experience in the field of retailing, as well as relying on the latest information and relevant achievements in this field.
Credit Authorship Contribution Statement
Farzad Kazemi: Conceptualization, Software, Writing original draft, Formal analysis, Methodology.
Bahara Hosseinzadah: review & Resources.
Professor Seyyed Ali Delbari: Technical supervision.
Ethical Approval
The article does not involve any studies with human participants or animals conducted by the authors.
NOTES
*First author.