Retail Organizations Research Articles

Import dependence of the European organic market and the role of Ukrainian exports

rates of organic retail trade (actual demand for organic products) and the growth rates of organic agricultural land areas (potential supply of organic products). This makes the European Union (EU) dependent on global imports. This research focuses on the import of organic products from Ukraine. To understand the underlying factors of the EU-27 countries’ import dependence on Ukrainian exports, this article proposes the following: 1) an analysis of Ukraine’s contribution to the total import volume to EU-27 countries by categories of organic products that have the most significant share in overall imports from Ukraine (these categories include: cereals, other than wheat and rice; soybeans; wheat; oilseeds, other than soybeans); 2) a study of the general trends of the European organic market’s import dependence on imports from Ukraine by the categories with the highest contribution; 3) clustering of the EU-27 countries based on organic market indicators; 4) an analysis of the European organic market using Ukrainian imports as an example. Ukrainian imports do not threaten the domestic European market; instead, countries with the highest level of dependence can use Ukrainian organic raw materials for processing and further re-export.

Scourge of replacing contemporary work environment with artificial intelligence (AI-dark-side): the role of capacity development in quality of work-life and organisational performance

Purpose The emergence of artificial intelligence (AI) which operates through technology and digital workspace has proven to transform organisations in recent times. However, there has been key concern over its efficiency among the workforce on how it may replace human intelligence in the contemporary work environment. This study aims to investigate the drawbacks otherwise known as the dark side of AI and its effect on employee quality of work−life and organisational performance through the lens of employee capacity development in reducing its shortcomings. Design/methodology/approach This study used a descriptive research design using a cross-sectional survey approach to administer the research instrument to 1,847 customer service officers of banks, customer agents of telecoms, customer care of retail organisations in Nigeria business environment across various units were respondents of this study, however, 862 participants were finally used. A simple random strategy was used to survey the study participants, and existing scales were adopted to form a new research instrument. A partial least square (PLS) based structural equation model (SEM) was adapted to analyse the collected data from the respondents. Findings The outcome of the study indicated that AI lacks creativity and has a negative impact on both employee quality of work−life and overall organisational performance. The outcome of the study demonstrated the drawbacks and the dark sides of AI as lack of emotional intelligence, lack of in-depth contextual knowledge, over-reliance on data quality and lack of ethical and moral decision analysis are the possible dark side of AI which adversely affect quality of work−life and overall performance of the organisations. The study concluded that it is difficult to replace human intelligence because of AI’s drawbacks and dark side. AI cannot function effectively beyond what is programmed in the system. Originality/value This study has offered a novel trajectory against the efficiency and possible benefits of AI that people are familiar with. It has changed the understanding of the researchers, policymakers and organisations that AI cannot replace human intelligence in the workplace without improvement on those established AI dark sides.

A Predictive Analysis of the Organized Retail Market in India Using Holt-Winter Exponential Smoothing Technique

The study aims to predict the size of the Indian organized retail market for future years. Most previous studies provided descriptive insights into the development of the organized retail industry without specifying any forecasting methods and approaches used in their predictions. This study attempts to employ the Holt-Winter exponential smoothing method for analyzing the Indian organized retail sector, a novel approach in this domain. The investigation began with a sales forecast for the preceding twenty-one years of data using different combinations of α and β coefficients. The best model was selected based on the α and β values that yielded the minimum statistical errors, as measured by MAD, MAPE, and MSE. The forecast results indicate a projected CAGR of 13.23% for the Indian organized retail market size over the next six years. The findings will provide retail marketers the latest market trends to inform sound business planning.

The Convergence of Artificial Intelligence and Human Marketing: A Framework for Enhanced Customer Insights and Personalization

This article examines the transformative integration of artificial intelligence and human expertise in modern marketing analytics, focusing on customer insight generation and personalization strategies. We present a comprehensive framework for human-AI collaboration in marketing operations through a mixed-methods approach combining quantitative analysis of customer data from 127 retail organizations and qualitative interviews with 34 marketing professionals. The findings reveal that organizations implementing AI-enhanced customer analytics in conjunction with human-driven creative strategy achieved a 47% improvement in customer engagement metrics and a 31% increase in conversion rates compared to traditional approaches. The article demonstrates that while AI excels at real-time pattern recognition and predictive modeling of customer behavior, human marketers provide crucial emotional intelligence and contextual interpretation that significantly enhance the effectiveness of personalization efforts. The article introduces the Dual Intelligence Marketing Framework (DIMF), which outlines optimal integration points between AI capabilities and human expertise throughout the customer journey. The framework addresses critical challenges in implementation, including technical infrastructure requirements, talent adaptation, and ethical considerations. This article contributes to both theoretical understanding and practical application of AI in marketing, offering actionable insights for organizations seeking to leverage the complementary strengths of artificial and human intelligence in their marketing operations.

Leadership essential to steer success within the retail sector

PurposeThe purpose of this investigation was to determine the leadership necessary for the retail sector which is having to embrace complexity induced through accelerating changes in consumer behaviour and expanding information accessibility.Design/methodology/approachSemi-structured interviews were used for across 10 disparate retail organisations deployed as case studies safeguarding all quality considerations were fully integrated.FindingsThe findings reveal brands are coping with greater complexity than before. Loyalty is eroding whilst social media increases the complexity for brands to uphold control of their image. Developing the market without sacrificing exclusivity or alienating core consumers necessitates effective leadership. The deductions revealed that despite some leadership styles seemingly more conducive, that specific qualities are more important for the retail sector.Research limitations/implicationsBy selecting ten organisations every effort was made to ensure a greater degree of credibility in the findings. It is acknowledged that this investigation is sector specific. Nonetheless, there exists considerable transferability value as the role of leadership within the retail sector can mirror expectations within other environments.Originality/valueIt was validated that leaders need to foster an organisational culture promoting continuous learning whilst introducing a consumer-first perspective across the business. The findings reinforce how specific traits, behaviours and styles are imperative amongst leaders preserving the success of the retail organisation with less focus on specific leadership styles.

Business intelligence solution implementation challenges: A comparative analysis of service based start-ups, small & medium and large enterprises

The term Business Intelligence (BI), first used in 1989 to describe an umbrella concept by Howard Dressner involving methods for improving business decision-making based on facts. It gradually evolved into data-driven decision support systems (DSS) in the contemporary era of technology, replacing terms like executive information systems. BI Solution works as a combination system of collection and storage of data to generate 'actionable information and knowledge at the right time, in the right location, and in the right form. Currently, BI solutions act as a priority for chief information officers (CIO), as a Gartner survey reported, to enhance business effectiveness and innovation by generating quality information of the company and using it in the decision-making process. Over the last few decades, business data volumes have increased tremendously due to the rise of business information systems such as ERP, CRM, etc. International Data Corporation (IDC) highlighted in the 6th annual study that the digital universe comprising structured and unstructured data will grow 300 times to 40,000 exabytes from 130 exabytes by 2020. The size of data will double every two years from 2012 onwards. India has witnessed the increased implementation of business intelligence solutions in the past decade, especially in the service industry, comprising retail, healthcare, BFSI, and IT organizations, to enhance their business practices & procedures and achieve competitive advantage. The present research aims to do a comparative study of business intelligence solution implementation challenges among service industry start-ups, small & medium, and large enterprises.

Navigating the Customer Experience Landscape: Unraveling the Dynamics of AI-driven Chatbot Services for FMCG Retailers

This paper evaluates the impact of AI-based chatbot services on the FMCG retail sector’s customer satisfaction. The data of 615 respondents was collected from FMCG customers in organized retail shops in and around the Albaha region of Saudi Arabia. The study acquired data from consumers who used chatbot services for the purchase of FMCS products. Seven hypotheses were formulated and tested to explain the relationships between these variables. It was found that responsiveness is related to customer satisfaction and affects the conversational tone. Further, the adopted conversational tone also influenced customer satisfaction, and the customers were able to process it better. Privacy concerns have an important role in how customers communicate online. This study contributes to the choice of FMCG retailers as far as customizing the chatbot services to ensure that their conversational tone is responsive and mindful of privacy matters in an attempt to increase satisfaction. Further this study can be generalized in other geographic regions with similar setup in order to understand the consumer behavior.

Reducing Technical Debt through Strategic Leadership in Retail Technology Systems

In the fast-paced and highly competitive landscape of retail, the management of technical debt has become a critical challenge for organizations striving to maintain robust and scalable technology systems. Technical debt, the accumulated cost of prioritizing quick fixes or suboptimal solutions over more sustainable and efficient options, can significantly hinder an organization's ability to innovate, adapt, and grow. This paper explores the role of strategic leadership in effectively reducing technical debt within retail technology systems. By examining key leadership strategies, such as fostering a culture of continuous improvement, investing in long-term technology planning, and emphasizing cross-functional collaboration, the paper highlights how retail organizations can systematically address and mitigate technical debt. Strategic leadership in the retail sector involves a comprehensive understanding of both business and technological imperatives. Leaders must recognize the implications of technical debt not only in terms of immediate financial costs but also in the broader context of business agility, customer experience, and competitive advantage. The paper emphasizes the need for leaders to adopt a proactive approach, prioritizing technical debt reduction as a strategic objective that aligns with the organization's overall goals. One of the central arguments of the paper is that effective technical debt management requires a shift from reactive to proactive leadership. This involves anticipating potential issues, encouraging innovation, and making informed decisions that balance short-term gains with long-term sustainability. The paper discusses various leadership practices that contribute to this shift, including the implementation of robust governance frameworks, the adoption of agile methodologies, and the promotion of a learning-oriented organizational culture.

Reducing Technical Debt through Strategic Leadership in Retail Technology Systems

In the fast-paced and highly competitive landscape of retail, the management of technical debt has become a critical challenge for organizations striving to maintain robust and scalable technology systems. Technical debt, the accumulated cost of prioritizing quick fixes or suboptimal solutions over more sustainable and efficient options, can significantly hinder an organization's ability to innovate, adapt, and grow. This paper explores the role of strategic leadership in effectively reducing technical debt within retail technology systems. By examining key leadership strategies, such as fostering a culture of continuous improvement, investing in long-term technology planning, and emphasizing cross-functional collaboration, the paper highlights how retail organizations can systematically address and mitigate technical debt.Strategic leadership in the retail sector involves a comprehensive understanding of both business and technological imperatives. Leaders must recognize the implications of technical debt not only in terms of immediate financial costs but also in the broader context of business agility, customer experience, and competitive advantage. The paper emphasizes the need for leaders to adopt a proactive approach, prioritizing technical debt reduction as a strategic objective that aligns with the organization's overall goals.One of the central arguments of the paper is that effective technical debt management requires a shift from reactive to proactive leadership.

Turnover Intention’s Employees Retail: The Role Performance And Work Engagment

This study aims to determine the effect of employee performance and work engagement on turnover intention both partially and simultaneously at retail organization. This research uses a quantitative approach. The sampling used is accidental sampling through google form with a total of 496 respondents of retail organization employees. Employee performance measurement tools are measured by the Individual Work Performance Questionnaire (IWPQ) of 18 items. Work engagement is measured by the Utrecht Work Engagement Scale-9 (UWES-9) with a total of 9 items. Turnover intention was measured by Turnover Intention Scale (TII) by Mobley with 3 items. Data were processed using Jamovi 2.4 software with multiple linear regression models. Hypothesis testing results show that employee performance has no effect on turnover intention. While the work engagement variable has a negative effect on turnover intention. Simultaneously, employee performance and work engagement positively affect turnover intention by 6.7%.

A study on impulse buying behaviour among consumers in organized retail stores in Varanasi district

A study on impulse buying behaviour among consumers in organized retail stores in Varanasi district

Balancing act: Tackling organized retail fraud on e-commerce platforms with imbalanced learning text models

As online shopping expands rapidly, so does the prevalence of fraud, resulting in significant losses for retailers. According to the 2020 National Retail Federation (NRF) report, organized retail crime costs retailers nearly $800,000 per billion in sales, with an expected global annual increase of over fourteen percent. This paper introduces a text-based fraud detection framework to mitigate these losses efficiently. The framework comprises four key components: text preprocessing, representation, knowledge extraction via machine learning algorithms, and model evaluation. By integrating data augmentation techniques, the framework enhances classifier performance in detecting fraud. The proposed method, employing a combination of FastText and Random Forest classifiers, achieves an impressive F1 score of 0.833 and AUC score of 0.99 on an augmented dataset, surpassing conventional keyword-based models. Informed by best practices in fraud detection, this scalable framework promises a solution to combat the escalating fraud associated with the exponential growth of online shopping.

Market Basket Analysis

Abstract: Market Basket analysis is a technique applied by retailers to understand customer’s shopping behaviour from their stores. The result of the effective analysis may improve supplier’s profitability, quality of service and customer satisfaction. The purpose of this project is to make use of anonymized data on customers’ transactional orders to focus on descriptive analysis on the customer purchase patterns, items which are bought together and units that are highly purchased from the store to facilitate reordering and maintaining adequate product stock. Market Basket Analysis is an important aspect of a retail organization's analytical framework for deciding where products should be placed and developing sales promotions for various segments of consumers to increase customer loyalty and, as a result, benefit. Market Basket Analysis is a data mining technique that can be used in various fields, such as marketing and etc. The frequent itemsets are mined from the database using the Apriori algorithm and then the association rules are generated. The project will assist supermarket managers in determining the relationship between the items that their customers purchase.

Driving Performance Excellence: Insights from Retail Sector on Transformasional Leadership and Knowledge Sharing Behavior

This research delves into the pivotal role of Knowledge-sharing behavior and Transformational Leadership in enhancing employee performance within the retail sector. By investigating the relationships between these key factors, the study aims to provide valuable insights for optimizing workforce effectiveness and driving organizational success. The research methodology involves a quantitative approach, with data collected through surveys from 96 retail sector employees in Bandung. Instruments validated by reputable researchers were utilized to measure transformative leadership, Knowledge-sharing behavior, and employee performance. Data quality was assessed using outer loading, AVE, Composite Reliability, and Cronbach's Alpha, followed by analysis through Partial Least Squares. The findings are expected to shed light on how fostering a culture of knowledge exchange and implementing transformative leadership practices can positively impact employee performance in the retail industry. Ultimately, this research seeks to offer practical recommendations for retail organizations looking to enhance operational efficiency, employee engagement, and overall performance in a competitive market environment.

The Effect of Work Engagement and Burnout to Retail Employee Performance

The purpose of this study was to determine how work engagement and burnout affect the performance of retail employees in this study using quantitative research methods, with a total of 350 respondents who are store employees of one of the retail organizations in Jabodetabek. Data collection is done offline by distributing questionnaires and using online methods via google form. The data analysis process uses Jamovi 2.4 software and uses multiple linear regression analysis methods. Based on the research results, it is known that (1) Work engagement has no effect on employee performance, (2) Burnout has a positive effect on employee performance, (3) Work engagement and burnout positively affect employee performance by 31.5%.

A Study on Store Operations at Spencer’s Retail Ltd.

There are certain aspects that are vital to be observed in any retail store such as front-end operations, back-end operations, store layout, customer relation management, visual merchandising, inventory management, promotional activities. Etc. This study explains all these aspects in particular and emphasizes for an effective way to the retailers to improve their ability to achieve the targeted goals and enhance the profitability of the organization. The project is aimed at the operations (business development) of a store engaged in retail business and studies its management, growth expansion program and other points. Business development is two types first is the income generation and by increasing the business of store. The marketing and business development team is responsible for business development of store and in turn of the organization. They should be creative and proactive to attain the task given .They must know how to take competitive advantage over the competitors. The business development plays an important role in retailing as the organized retailer operates at very thin margins. So any additional income to the store is advantage to the organization. Based on the information presented before on the aspects relating to concepts, objectives, purposes and importance .It is understood that the concept of “business development” is one of the important areas in retailing. Further it is also understand that based on the profile of the organization presented in further study, the organizations is one of the leading and important retail organizations at present.

PURPOSE OF VISIT AND SATISFACTION OF BRANDED APPAREL IN SELECTED RETAIL STORES AT COIMBATORE CITY

Todays’ trend in shopping people has an attitude to spend time in organized shopping malls and retail stores. To hang on the customer inside a malls and growing organized retail outlet measuring customer satisfaction in such stores is very important. This study focuses on profiling retail customers and measuring their level of satisfaction in the selected stores in the Coimbatore city. Primary data has been collected using structured questionnaire. It has been found that visiting retail outlets have become a group activity. Word of mouth by friends, family, colleagues and Advertisement, influences majority of customer. Majority of respondents came for shopping inside store, rest came for hang out, enjoy crowd, meeting friends and getting refreshed. Femina Retail outlet was found to have higher level of satisfaction followed by Lifestyle, Pantaloons and Westside.

Efficient customer behaviour prediction in Indian metropolitan cities for E‐commerce applications

AbstractThe primary objective of this manuscript is to find the service quality factors, which influence customer satisfaction, promotional efforts taken by the retailer, and the perception of customers towards the organized retail sector. This investigation uses both offline and online retail sales datasets. Particularly, in the case of the offline retail sale dataset, an energy‐efficient analysis is conducted to collect scalable, accurate, and real‐time data. The collected data is used in enhancing several aspects of e‐commerce operations, including customer experience optimization and inventory management, which results in better decision‐making with increased efficiency. After data collection, the outliers are eliminated by implementing the z‐score technique. The removal of outliers increases the variability of collected data which reduces statistical power. The statistically sufficient data are given to the Randomized Grasshopper Optimization Algorithm (RGOA) for optimal instance selection. Finally, the selected optimal instances are given to the deep neural network (DNN) model for future customer behaviour prediction. The efficacy of the RGOA‐DNN model is analysed by using evaluation measures like mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean percentage error (MPE) and mean absolute percentage error (MAPE). The numerical analysis states that the RGOA‐DNN model obtained a minimal error rate with MSE of 0.09 and 0.10, RMSE of 0.13 and 0.14, MAE of 0.12 and 0.16, MPE of 0.10 and 0.11 and MAPE of 0.08 and 0.10 on the offline and online retail sales datasets. The RGOA‐DNN model has a minimal error rate in future customer behaviour prediction related to the conventional regression models. Furthermore, the elimination of inactive instances or selection of optimal instances reduces the model complexity to linear and computational time to 22.10 and 23.12 s on the offline and online retail sales datasets.

Comparision of Consumer Preferences in Regards to Food Retail Formats in Tirupati City, India

This study conducted in the year 2023 aimed to compare consumer preferences for various food retail formats in Tirupati city, focusing on organized and unorganized sectors, and to identify factors influencing their choices. A representative sample of 120 consumers was selected using simple random sampling, and primary data was collected through personal interviews. The findings revealed that 73.33 percent of the consumers in showed a moderate perception of both organized (62.50%) and unorganized (73.33%) food retail formats. Kirana stores (unorganized) and supermarkets (organized) were the preferred choices for purchasing food and groceries. The reasons for preferring organized retail formats included a wide range of products and better product presentation, while unorganized retail formats were favoured for more bargaining opportunities and locally sourced products. Income levels were found to influence retail format choices, with lower-income groups more likely to shop at unorganized formats. The SWOT analysis indicated that organized stores benefit from a wide range of products but face competition from unorganized formats and online platforms, while, unorganized stores build strong customer relationships but struggle with limited product variety and changing consumer preferences.

Differential price pass‐through in organic and conventional fresh fruit and vegetable markets

AbstractOrganic food production growth has remained relatively slow compared to organic retail sales growth in the United States. This paper questions whether the conduct of downstream agents plays any role in explaining the difference. Mainly, we shed light on structural differences between organic and conventional fresh fruit and vegetable markets by examining differential price pass‐through rates. We estimate a rolling‐window retail pricing model using retail and wholesale price data from five metropolitan statistical areas with terminal markets in the United States. We find that pass‐through rates are 10 to 15 percentage points lower in the organic market, and the differences are statistically significant. We also find that the gap between pass‐through rates narrows as the organic market share increases. Our results suggest the organic market is significantly less competitive than the conventional market. The implication is that farmers may have less incentive to convert to organic farming as they may not capture the full retail price premium consumers pay.