Search Results (107)

The article aims to identify consumer types based on their attitudes and behaviors toward phygital tools and solutions. The analysis was based on the authors’ empirical research. The research was conducted on a sample of 2160 Polish consumers. The study employed an online survey technique. To determine the types of consumers, a 20-item scale was used, allowing the respondents to express their attitudes toward solutions and tools that improve shopping in the phygital space. The extraction of types was carried out in two steps. The first was cluster analysis, conducted using the hierarchical Ward method with the square of the Euclidean distance, and the second was non-hierarchical cluster analysis using the k-means method. As a result of the analyses, three relatively homogeneous types of consumers were distinguished: phygital integrators, digital frequenters, and physical reality anchors. The behaviours of consumers from each type were examined in the context of their impact on sustainable consumption and the sustainable development of the planet. The proposed typology contributes to developing consumer behavior theory in sustainable consumption environments. It provides practical implications for designing customer experiences that are more inclusive, resource-efficient, and aligned with responsible consumption patterns. Understanding how different consumer groups engage with phygital tools allows businesses and policymakers to tailor strategies that support equitable access to digital services and foster more sustainable, adaptive consumption journeys in an increasingly digitized marketplace. Full article

As an emerging e-commerce model, live streaming e-commerce integrates instant interaction, content marketing, and online sales to bring consumers a new shopping experience. However, there are many risks in the process of live e-commerce transactions. Identifying key risk factors and implementing targeted control measures are crucial for promoting the sustainable and healthy development of live streaming e-commerce. This paper firstly constructs a business model of live streaming e-commerce transactions according to the transaction scenario and summarizes 24 risk factors from the three dimensions of live streaming e-commerce platforms, merchants, and anchors based on relevant national standards and other relevant literature. Secondly, the Delphi method is employed to modify and optimize the initial risk factors. On this basis, the social network model of risk factors is constructed to determine the influence relationship among risk factors. By calculating the degree centrality, factor types are segmented, and key risk factors as well as influence paths are identified. Finally, corresponding countermeasures and suggestions are proposed. The results indicate that Credit Evaluation System Perfection, Service Evaluation System Perfection, Qualification Audit Mechanism Perfection, Dispute Complaint Handling Channels Perfection, Risk Identification Mechanism Perfection, Platform Qualification, Merchant Qualification, and Merchant Credit are the critical risk factors affecting live streaming e-commerce transactions. Full article

The rapid expansion of online grocery shopping has intensified the need for strategically located distribution hubs that ensure efficient and sustainable operations. Traditional location models emphasize economic and logistical factors but often neglect energy efficiency and environmental sustainability. This paper proposes a hybrid decision-making model that integrates the analytic hierarchy process (AHP) and the spherical fuzzy technique for order of preference by similarity to ideal solution (SFTOPSIS) to address the complexities of delivery hub location selection. The AHP is used to determine the relative importance of key decision-making criteria, including cost, accessibility, infrastructure, competition, and sustainability, while SFTOPSIS ranks the candidate locations based on their proximity to the ideal solution. Spherical fuzzy sets allow for a more nuanced treatment of uncertainty, improving decision-making accuracy in dynamic environments. The results demonstrate that this hybrid approach effectively manages incomplete and uncertain data, delivering a robust ranking of candidate locations. By incorporating sustainability as a key factor, this study provides a structured and adaptive framework for businesses to optimize logistics operations in the post-pandemic landscape. The proposed methodology not only enhances decision-making in location selection but contributes to the development of more resilient and sustainable supply chain strategies. Full article

Background/Objectives: The problems with following a gluten-free (GF) diet result from the high cost of GF products, their limited availability for celiac disease (CD) patients, and their disputable quality. The aim of this cross-sectional study was to assess the frequency of buying and availability of GF cereal products in a population of Polish female CD patients. Methods: This study was conducted in a population of Polish female CD patients who were members of the Polish Celiac Society, and n = 547 respondents were included in this study. Participants were asked about the frequency of buying and problems with the availability of GF cereal products, which were compared by sub-groups stratified by age, place of residence, place of purchasing major grocery shopping and purchasing GF products online. Results: The majority of the studied female CD patients declared often purchasing GF flour, pasta, and bread, as well as never purchasing GF puff pastry, fried baked goods, dumplings, and crackers. The only product for which the majority of the studied participants declared problems with availability was dumplings. For younger respondents, a higher share declared often buying GF pasta (p = 0.0073), chips, crisps and puffs (p < 0.0001), and Asian-style noodles (p = 0.0269), as well as declared problems with the availability of GF wraps/tortillas (p = 0.0001), puff pastry (p = 0.0294), fried baked goods (p = 0.0008), biscuits/cookies (p = 0.0148), and Asian-style noodles (p = 0.0046) compared to older respondents, while for older respondents, a higher share declared often buying GF flour (p = 0.0358), and never buying GF wraps/tortillas (p = 0.0181). For respondents living in big cities, a higher share declared problems with the availability of GF pasta compared to respondents living in small towns/villages (p = 0.0245). For respondents purchasing major grocery shopping in hypermarkets, a higher share declared often buying GF biscuits/cookies compared to respondents purchasing in other shops (p = 0.0039), while for respondents purchasing in other shops, a higher share declared never buying puff pastry (p = 0.0076), dumplings (p = 0.0002), and wraps/tortillas (p = 0.0038), as well as declared problems with availability of GF puff pastry (p = 0.0246), biscuits/cookies (p = 0.0002), and breakfast cereals (p = 0.0011). For respondents not purchasing GF products online, a higher share declared never buying GF fried baked goods compared to respondents purchasing online at least occasionally (p = 0.0284), as well as a lower share declared problems with the availability of GF wraps/tortillas (45% vs. 33%, p = 0.0411). Conclusions: The population of Polish female CD patients seems quite diverse in terms of the chosen GF cereal products, with age, primary place of purchasing major grocery shopping and purchasing GF products online, but not the place of residence, as the major determinants. The declared problems with the availability of GF products are probably associated with two diverse mechanisms—either frequent purchasing (as individuals not purchasing may not be interested in such a product at all) or rare purchasing (which may result from poor availability). Increasing the availability of GF cereal products for a population of Polish female CD patients may allow them to obtain a more diverse diet. Full article

The ever-expanding market has made organic food a popular research topic, with the primary question being what factors facilitate or hinder consumers in making an organic purchase. The most relevant studies have been conducted in an offline context. As selling organic food online has become a common practice and is underresearched, this study aims to (1) explore the drivers and barriers of online organic food shopping and (2) investigate the shopping behavior of organic food from an omnichannel perspective. The results of partial least square structural equation modeling (PLS-SEM), with 278 valid samples, indicate that trust in organic labels and positive review sentiment significantly contribute to the intention to purchase organic food online, which in turn influences online purchase behaviors. For online shopping behavior, the investigation shows that Taiwanese consumers, on a monthly basis, make an average of 3.22 organic food purchases and spend US$156.44 through offline channels, whereas they make 2.34 purchases of organic food and spend US$114.71 via online channels. Organic vegetables and fruits are the most frequently purchased organic foods. Among online channels, consumers prefer visiting the websites of general grocery stores and specialty stores over social media platforms. Our findings suggest that the determinants of organic food shopping differ between offline and online contexts and reveal interesting behavioral patterns of online organic shopping. Full article

Consumer behavior has changed considerably over time. In recent decades, people have used resources at a rate that exceeds the total consumed throughout history. This paper aims to address the determinants of the smartphone purchase decision, emphasizing gender differences, price influence, and previous online shopping experience. The methodology used combines a bibliometric review of the literature to identify major trends in consumer behavior research and a quantitative research survey that provides insight into consumer behavior in the smartphone purchase process. The survey highlights brand preferences, purchase patterns, product selection criteria, and the influence of socioeconomic factors on the purchase decision, identifying the determinants of online versus physical store purchase decisions among young consumers in the northeastern, east, and southeastern regions of Romania. Thus, our analysis aims to identify the variables influencing consumer preferences and to assess the statistical significance of these differences using quantitative methods and relevant statistical tests. The collected data came from a valid sample of 456 respondents for the general analysis and 271 valid cases for the online shopping analysis. The analysis shows that gender is a significant predictor of online purchase decisions, with men being 2.65 times more likely to purchase a smartphone online than women. The collected data were analyzed using t-tests, Chi-square tests, and logistic regression to assess the influence of variables on online smartphone purchase intention. Full article

Economic performance in modern manufacturing enterprises is often influenced by random dynamic events, requiring real-time scheduling to manage multiple conflicting production objectives simultaneously. However, traditional scheduling methods often fall short due to their limited responsiveness in dynamic environments. To address this challenge, this paper proposes an innovative online rescheduling framework called the Dual-Level Integrated Deep Q-Network (DLIDQN). This framework is designed to solve the dynamic multi-objective flexible job shop scheduling problem (DMOFJSP), which is affected by six types of dynamic events: new job insertion, job operation modification, job deletion, machine addition, machine tool replacement, and machine breakdown. The optimization focuses on three key objectives: minimizing makespan, maximizing average machine utilization ($U_{ave}$), and minimizing average job tardiness rate ($T{R}_{ave}$). The DLIDQN framework leverages a hierarchical reinforcement learning approach and consists of two integrated IDQN-based agents. The high-level IDQN serves as the decision-maker during rescheduling, implementing dual-level decision-making by dynamically selecting optimization objectives based on the current system state and guiding the low-level IDQN’s actions. To meet diverse optimization requirements, two reward mechanisms are designed, focusing on job tardiness and machine utilization, respectively. The low-level IDQN acts as the executor, selecting the best scheduling rules to achieve the optimization goals determined by the high-level agent. To improve scheduling adaptability, nine composite scheduling rules are introduced, enabling the low-level IDQN to flexibly choose strategies for job sequencing and machine assignment, effectively addressing both sub-tasks to achieve optimal scheduling performance. Additionally, a local search algorithm is incorporated to further enhance efficiency by optimizing idle time between jobs. The numerical experimental results show that in 27 test scenarios, the DLIDQN framework consistently outperforms all proposed composite scheduling rules in terms of makespan, surpasses the widely used single scheduling rules in 26 instances, and always exceeds other reinforcement learning-based methods. Regarding the $U_{ave}$ metric, the framework demonstrates superiority in 21 instances over all composite scheduling rules and maintains a consistent advantage over single scheduling rules and other RL-based strategies. For the $T{R}_{ave}$ metric, DLIDQN outperforms composite and single scheduling rules in 20 instances and surpasses other RL-based methods in 25 instances. Specifically, compared to the baseline methods, our model achieves maximum performance improvements of approximately 37%, 34%, and 30% for the three objectives, respectively. These results validate the robustness and adaptability of the proposed framework in dynamic manufacturing environments and highlight its significant potential to enhance scheduling efficiency and economic benefits. Full article

Waste from electrical and electronic equipment (WEEE) is expected to reach 82 million metric tons by 2030, with a global average of 7.8 kg/cap/year. In 2022, the amount of WEEE generated in Laos was 3.6 kg/cap/year, with no formal collection or treatment facilities in place. An examination of WEEE management and repair practices in the capital of Laos, Vientiane, was conducted, involving a review of the relevant literature and data gathered from interviews and online surveys of a total of 82 families, 17 junkshops, and 16 repair shops. Additionally, the environmental impact of smartphone repair activities was determined by utilizing data from existing life cycle assessment studies. The findings highlight the challenges of WEEE management, including infrastructure deficit and policy gaps. The informal sector plays a significant role in WEEE collection and dismantling. Manual dismantling takes place in junkshops prior to exportation to Vietnam, Thailand, and China. Reuse and repair are common practices and are present in both formal and informal contexts. Smartphone repair is a prevalent service alongside cooling and freezing equipment repair. The primary obstacles of repair are linked to the quality and availability of spare parts, the absence of repair guidelines and technical expertise, complicated designs, and consumer awareness. Notwithstanding the challenges associated with repairs, this case study demonstrates the environmental benefits of smartphone repair, achieving a reduction of 44% in GHG emissions compared to the conventional replacement over a 7-year observation period. Recommendations are proposed to enhance WEEE management systems and advance the repair movement. Full article

In the online shopping context, brands aim to achieve a high level of profit by providing better customer satisfaction by using various artificial intelligence tools. They try creating a satisfactory customer experience by creating a system that provides never-ending customer support by using dialog-based chatbots, especially in the field of customer service. However, there is a lack of research investigating the impact of business and customer-based chatbot activities together on online purchase intention and the intention to reuse chatbots. This research considers the use of chatbots as a marketing tool from both customer and business perspectives and aims to determine the factors that affect the customers’ intention to purchase online and reuse chatbots. Accordingly, the impact on customer satisfaction with chatbot usage, which is based on chatbots’ communication quality and customers’ motivations to use chatbots, on online purchase intention and intention to reuse chatbots was examined. Through an online questionnaire with two hundred and ten participants, employing structural equation modeling, we revealed that customer satisfaction with chatbot usage has a greater impact on the intention to reuse chatbots than on online purchase intentions. In addition, chatbot communication quality has a greater impact on customer satisfaction with chatbot usage than customers’ motivation to use chatbots. To solidify these findings, confirmatory factor analysis, along with reliability and validity assessments, were implemented within the analytical framework to provide robust support for the study’s hypotheses. These findings not only provide empirical evidence and implications for companies in online shopping but also extend the understanding of AI tools in marketing, highlighting their subtle impact on consumer decision-making in the dynamic digital marketplace. Full article

This study leverages approximately 7500 reviews from Skytrax to explore the determinants of airport service quality and their influence on passenger recommendations. The dataset includes various features such as terminal cleanliness, terminal seating, terminal signs, food and beverages, airport shopping, WiFi connectivity, and airport staff. The research employs a comprehensive methodology encompassing statistical data analysis, predictive modelling, and interaction effects analysis. The descriptive analysis of time-series data highlighted trends and fluctuations in service quality and recommendations, providing insights into temporal dynamics. Multiple machine learning models, including logistic regression, Random Forest, SVM, KNN, Gradient Boosting, and Neural Networks, were developed in this study and cross-validated for airport recommendation based on Skytrax’s online reviews. Among others, Gradient Boosting emerged as the most accurate model with an 88.15% mean accuracy. Interaction effects revealed significant combined influences, such as terminal cleanliness and terminal seating, on passenger recommendations. This multifaceted approach offers robust insights into factors influencing airport recommendations and guides improvements in airport management to enhance passenger satisfaction. Future work will focus on a general-purpose machine learning framework and its toolbox development for airport service quality analysis based on online reviews from various sources. Full article

This study explores the context of buy-online-and-return-in-store (BORS) policy from an environmental perspective and conducts a comprehensive analysis through the theory of planned behavior (TPB). Adding environmental awareness and awareness of consequences provides a new perspective on how sustainable practices can be enhanced through an omni-channel retail strategy. Survey responses from 405 participants were analyzed using structural equation modeling. Results show that attitudes, subjective norms, and perceived behavioral control are key determinants of practical BORS policy. The study found that environmental awareness significantly influenced BORS adoption directly and indirectly by enhancing perceived behavioral control, whereas consequence awareness primarily affected adoption by shaping consumer attitudes. Compared with consumers who choose to return online, consumers who prefer in-store returns show higher environmental awareness, highlighting the environmental advantages of BORS. In addition, the BORS policy improves overall shopping satisfaction by integrating the convenient process of online purchases and offline returns, allowing consumers to switch freely between different channels. These findings provide valuable insights for retailers and policymakers seeking to promote sustainable consumer behavior to effectively promote the importance of sustainable retail practices. Full article

The popularity of online shopping in China has increased significantly, creating new development opportunities for the express delivery industry. However, the rapid expansion of the express industry has also created challenges in the parcel sorting process. The demanding nature of parcel sorting work, which is characterized by intense and prolonged repetitive tasks, makes individuals particularly vulnerable to the effects of fatigue. Fatigue is a complex condition that encompasses both physiological and psychological exhaustion. It often results in reduced energy levels and diminished functionality, significantly impacting an individual’s performance at work and their overall well-being. This study aimed to investigate how physiological and psychological fatigue affects sorting efficiency and to identify appropriate rest periods that will allow employees to maintain their performance levels. The research involved fifteen participants who took part in a 60 min continuous sorting experiment and a similar experiment with scheduled breaks. During both trials, we collected data on participants’ electromyography (EMG) and electrodermal activity (EDA), as well as subjective fatigue ratings (RPE). Signal features such as the median frequency (MF) of EMG and the skin conductance level (SCL) were analyzed to assess physiological and psychological fatigue, respectively. The results show that physiological fatigue mainly affects sorting efficiency in the first 30 min, while psychological fatigue becomes more influential in the following half-hour period. In addition, subjective fatigue levels during the first 30 min are primarily determined by psychological factors, while beyond that point, both physiological and psychological fatigue contribute to subjective fatigue. Rest periods of 415–460 s, based on EDA recovery times, effectively support sorting efficiency and participants’ recovery. This study highlights the complex ways in which fatigue affects parcel sorting performance and provides valuable theoretical and practical insights for establishing labor quotas and optimizing work schedules in the parcel sorting industry. Full article

The unprecedented production and sharing of data, opinions, and comments among people on social media and the Internet in general has highlighted sentiment analysis (SA) as a key machine learning approach in scientific and market research. Sentiment analysis can extract sentiments and opinions from user-generated text, providing useful evidence for new product decision-making and effective customer relationship management. However, there are concerns about existing standard sentiment analysis tools regarding the generation of inaccurate sentiment classification results. The objective of this paper is to determine the efficiency of off-the-shelf sentiment analysis APIs in recognizing low-resource languages, such as Greek. Specifically, we examined whether sentiment analysis performed on 300 online ordering customer reviews using the Meaning Cloud web-based tool produced meaningful results with high accuracy. According to the results of this study, we found low agreement between the web-based and the actual raters in the food delivery services related data. However, the low accuracy of the results highlights the need for specialized sentiment analysis tools capable of recognizing only one low-resource language. Finally, the results highlight the necessity of developing specialized lexicons tailored not only to a specific language but also to a particular field, such as a specific type of restaurant or shop. Full article

The mode is a fundamental descriptive statistic in data analysis, signifying the most frequent element within a dataset. The range mode query (RMQ) problem expands upon this concept by preprocessing an array A containing n natural numbers. This allows for the swift determination of the mode within any subarray A[a..b], thus optimizing the computation of the mode for a multitude of range queries. The efficacy of this process bears considerable importance in data analytics and retrieval across diverse platforms, including but not limited to online shopping experiences and financial auditing systems. This study is dedicated to exploring and benchmarking different algorithms and data structures designed to tackle the RMQ problem. The goal is to not only address the theoretical aspects of RMQ but also to provide practical solutions that can be applied in real-world scenarios, such as the optimization of an online shopping platform’s understanding of customer preferences, enhancing the efficiency and effectiveness of data retrieval in large datasets. Full article

As a product of the development of e-commerce over a specific period of time, the “new retail model” breaks the barriers between the traditional retail industry and e-commerce. Supported by Internet technology, it builds a new business model of “physical store + e-commerce + logistics” through the integration of online, offline, and logistics, which also leads to a great change in consumer behavior. Therefore, in order to meet consumer demand and achieve the long-term development of shopping malls, while taking into account the fair allocation of urban space resources, the indicators and methods of shopping mall site selection evaluation in the new retail era will be significantly different from traditional shopping mall site selection decisions. In this paper, the Wuhan East Lake Hi-Tech Zone is selected as the research object, and a comprehensive AHP-GIS assessment model is proposed. By investigating the impact of consumers’ behavioral changes on shopping mall location in the new retail era, a suitability evaluation system containing eight evaluation indicators is constructed, and the weights of each factor are determined using hierarchical analysis. At the same time, GIS is used to process the spatial analysis of the indicators, and combined with the weights of the factors, superposition analysis and quantitative research are carried out. Finally, based on the correlation analysis between ratings and customer flow, the suitability evaluation results are further supported in order to provide a more objective and scientific basis for the location of shopping malls from the perspective of the change in consumer behavior under the new retail model, and to put forward universal suggestions for the construction and development of shopping malls in the future. Full article