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In recent times, the field of corporate intelligence has gained substantial prominence, employing advanced data analysis techniques to yield pivotal insights for instantaneous strategic and tactical decision-making. Expanding beyond rudimentary post observation and analysis, social media analytics unfolds a comprehensive exploration of diverse data streams encompassing social media platforms and blogs, thereby facilitating an all-encompassing understanding of the dynamic social customer landscape. During an extensive evaluation of social media presence, various indicators such as popularity, impressions, user engagement, content flow, and brand references undergo meticulous scrutiny. Invaluable intelligence lies within user-generated data stemming from social media platforms, encompassing valuable customer perspectives, feedback, and recommendations that have the potential to revolutionize numerous operational facets, including supply chain management. Despite its intrinsic worth, the actual business value of social media data is frequently overshadowed due to the pervasive abundance of content saturating the digital realm. In response to this concern, the present study introduces a cutting-edge system known as the Enterprise Just-in-time Decision Support System (EJDSS).

Leveraging deep learning techniques and advanced analytics of social media data, the EJDSS aims to propel business operations forward. Specifically tailored to the domain of marketing, the framework delineates a practical methodology for extracting invaluable insights from the vast expanse of social data. This scholarly work offers a comprehensive overview of fundamental principles, pertinent challenges, functional aspects, and significant advancements in the realm of extensive social data analysis. Moreover, it presents compelling real-world scenarios that vividly illustrate the tangible advantages companies stand to gain by incorporating social data analytics into their decision-making processes and capitalizing on emerging investment prospects.

To substantiate the efficacy of the EJDSS, a detailed case study centered around reverse logistics resource recycling is presented, accompanied by experimental findings that underscore the system’s exceptional performance. The study showcases remarkable precision, robustness, F1 score, and variance statistics, attaining impressive figures of 83.62%, 78.44%, 83.67%, and 3.79%, respectively.

This scholarly work offers a comprehensive overview of fundamental principles, pertinent challenges, functional aspects, and significant advancements in the realm of extensive social data analysis. Moreover, it presents compelling real-world scenarios that vividly illustrate the tangible advantages companies stand to gain by incorporating social data analytics into their decision-making processes and capitalizing on emerging investment prospects.

The new customer churn prediction (CCP) utilizing deep learning is developed in this work. Initially, the data are collected from the WSDM-KKBox’s churn prediction challenge dataset. Here, the time-varying data and the static data are aggregated, and then the statistic features and deep features with the aid of statistical measures and “Visual Geometry Group 16 (VGG16)”, accordingly, and the features are considered as feature 1 and feature 2. Further, both features are forwarded to the weighted feature fusion phase, where the modified exploration of driving training-based optimization (ME-DTBO) is used for attaining the fused features. It is then given to the optimized and ensemble-based dilated deep learning (OEDDL) model, which is “Temporal Context Networks (DTCN), Recurrent Neural Networks (RNN), and Long-Short Term Memory (LSTM)”, where the optimization is performed with the aid of ME-DTBO model. Finally, the predicted outcomes are attained and assimilated over other classical models.

The features are forwarded to the weighted feature fusion phase, where the ME-DTBO is used for attaining the fused features. It is then given to the OEDDL model, which is “DTCN, RNN, and LSTM”, where the optimization is performed with the aid of the ME-DTBO model.

The accuracy of the implemented CCP system was raised by 54.5% of RNN, 56.3% of deep neural network (DNN), 58.1% of LSTM and 60% of RNN + DTCN + LSTM correspondingly when the learning percentage is 55.

The proposed CCP framework using the proposed ME-DTBO and OEDDL is accurate and enhances the prediction performance.

This study aims to detect consumer sarcasm through inconsistencies in sentiment features between text and images of hotel reviews.

This paper proposes a model for sarcasm detection based on multimodal deep learning using reviews of three hotel brands collected from two travel platforms, which can identify emotional inconsistencies within a modality and across modalities. Text-image interaction information is explored using graph neural networks (GNN) to detect essential clues in sarcasm sentiment.

The research results show that the multimodal deep learning model outperforms other baseline models, which can help to understand hotel service evaluation and provide hotel managers with decision-making opinions.

This research can help hoteliers in two ways: detecting service quality and formulating strategies. By selecting reference hotel brands, hoteliers can better assess their level of service quality (optimal resource allocation ensues); therefore, sarcasm detection research is not only beneficial for hotel managers seeking to improve service quality. The multimodal deep learning method introduced in the present study can be replicated in other industries to help travel platforms optimize their products and services.

本研究通过分析酒店评论文本和图像之间情感特征的不一致性来检测消费者的讽刺。

本文提出了一种基于多模态深度学习的讽刺检测模型, 使用从两个旅行平台收集的三个酒店品牌的评论, 该模型能够识别模态内部和模态之间的情感不一致性。利用图神经网络（GNN）探索文本-图像交互信息, 以检测讽刺情感中的关键线索。

研究结果显示, 多模态深度学习模型优于其他基线模型, 这有助于理解酒店服务评估, 并为酒店经理提供决策建议。

该研究可以在两方面帮助酒店业者：检测服务质量和制定策略。通过选择参考酒店品牌, 酒店业者可以更好地评估其服务质量水平（随之而来的是最佳资源分配）, 因此, 讽刺检测研究不仅有助于寻求提高服务质量的酒店经理。本研究介绍的多模态深度学习方法可以在其他行业复制, 帮助旅行平台优化其产品和服务。

While many studies have focused on the effectiveness of service recovery after encountering service failure, few have considered for pre-recovery opportunities. Drawing on service guarantee research and the inoculation theory, this study examines the effect of pre-recovery strategy on customer’s satisfaction when faced with possible service failure. This study takes into consideration the uncertainty of service failure, including specific or unpredicted failures.

Three experimental studies were conducted to examine our research hypotheses, which include a total of 1203 subjects recruited from online and offline platforms.

The findings reveal that service guarantees and inoculation messages both effectively mitigate the negative consequences of a service failure. Specifically, the pre-recovery strategy is particularly effective for specific service failures. In addition, we thoroughly examined the mediating role of customer attribution.

This research significantly contributes to the existing literature on service research by delving into the potential positive impact of proactive measures undertaken by service enterprises. Furthermore, the findings of this research offer valuable insights for practitioners.

This study aims to explore the attraction, influence and persuasive effect of advertising (with or without metaphor) across different types of hotels (luxury vs. budget hotels).

Two independent variables – namely, the hotel category (luxury versus budget) and the use of rhetorical figures (text with metaphors versus text without metaphors) – serve as the basis for the investigation. The research design is a 2 × 2 within-subjects construct based on eye-tracking methodology complemented by questionnaire-based data collection. Participants were engaged with hotel advertisements under four specific conditions, each reflecting a distinct combination of the two independent variables.

The findings reveal the augmented attention-grabbing prowess of advertisements that use metaphors compared to those that do not. Furthermore, the study findings reveal that the hotel category significantly influences the efficacy of advertising; advertisements promoting luxury hotels were found to stimulate superior impression recall, garner higher customer affinity and prompt a more potent intention to patronize the hotel. Finally, the study shows that neither congruent nor incongruent pairings of visual (budget or luxury hotel) and verbal elements (with or without metaphor) yield additional advertising benefits.

This research addresses the limited empirical guidance available for hotel operators at various levels regarding the design of advertisements incorporating rhetorical devices. It highlights the potential benefits of using figurative rhetoric, especially metaphors, in advertising for both budget and luxury hotels. The study reveals a notable correlation between hotel category, particularly luxury offerings, and customer attention, retention, preference and intention to visit. Furthermore, it encourages hotel operators, regardless of their hotel’s ranking, to incorporate metaphorical language into their advertising designs, underscoring the need for strategic integration in overall advertisement planning.

Using high-resolution online eye-tracking technology, this study innovatively examines the influence of hotel category and rhetorical devices on the effectiveness and attention-grabbing potential of advertisements. Through this approach, the study seeks to offer valuable managerial and advertising insights for those involved in hotel marketing.

The postponement principle concerns defining when and where value is added, usually referring to hardware components for physical products. However, in modern supply chains, software’s importance is increasing, impacting the timing and location of value-adding operations. Lacking insights into software-driven implications for postponement, we aim at elaborating on the postponement principle by contextualizing its evolution when integrating different objects (i.e. hardware and software).

We adopted an abductive approach to elaborate on the existing knowledge with original empirical insights. A single-case study with four subcases allowed us to explore postponement dimensions in the context of a global high-tech enterprise offering products that integrate hardware and software objects. As global supply chains involve multiple jurisdictions with heterogeneous regulations, we also analyzed in depth the emerging fiscal and legal implications.

Besides where and when value is added, the study illustrates that deciding who (i.e. what legal entity) is carrying out what operation on what kind of object is highly important. Moreover, fiscal and legal implications for the various legal entities strongly depend on what operations are executed and in which jurisdiction (where). The study identifies critical interrelationships among postponement dimensions when integrating hardware and software objects, highlighting the importance of understanding and managing their reciprocity with the emerging fiscal and legal risks.

We elaborate on the postponement principle by contextualizing its applications when integrating hardware and software objects in global supply chains, which include multiple jurisdictions. By formalizing the impact of the who dimension, the study contributes to developing the interorganizational perspective for postponement. Moreover, it extends the traditional cost perspective for postponement beyond the trade-off between responsiveness and cost-efficiency, suggesting that firms applying global postponement should extend their focus to also examine fiscal and legal risks for all the legal entities involved.

This study examines the relationship between the emphasis on organic food in Afyonkarahisar's cuisine and the growing demand for healthier and sustainable options. This study aimed to understand the factors influencing the intention to consume organic foods.

This study uses a quantitative research method. It uses scales adapted from previous research to assess the health and safety of organic food's functional, hedonic, and social values. Data were collected using the purposive sampling method, and a sample size of 410 individuals was analyzed using the PLS-SEM method. Measurement and structural models were evaluated to assess validity, reliability, and model fit.

The results indicate that implementing health and safety practices significantly and positively affects perceptions of organic food's functional, hedonic, altruistic, and social values. Additionally, consumers' attitudes toward organic food are greatly influenced by their perceptions of functional and social value, whereas hedonic and altruistic values do not have a significant positive effect.

The results of this study have a beneficial impact on food industry practitioners and policymakers. Emphasizing health and safety practices in producing and promoting organic foods can improve consumer perceptions and attitudes toward organic products. Understanding the importance of practical benefits and social issues can help companies respond effectively to consumer preferences and market organic foods.

This study contributes significantly to the existing literature by investigating the relationship between organic food emphasis and consumer perceptions in the unique context of Afyonkarahisar, a city renowned for its gastronomy. While focusing on Afyonkarahisar provides valuable insights into the specific values that influence consumers' attitudes toward organic food, it is essential to highlight that this research extends beyond the local context to offer broader implications and significance.

With the advancement of digital transformation, it is important for e-retailers to use artificial intelligence (AI) for customer engagement (CE), as CE enables e-retail brands to succeed. Essentially, AI e-marketing (AIeMktg) is the use of AI technological approaches in e-marketing by blending customer data, and Retail 4.0 is the digitisation of the physical shopping experience. Therefore, in the era of Retail 4.0, this study investigates the factors influencing the use of AIeMktg for transforming CE.

The primary data were collected from 305 e-retailer customers, and the analysis was performed using a quantitative methodology.

The results reveal that AIeMktg has tremendous applications in Retail 4.0 for CE. First, it enables marketers to swiftly and responsibly use data to anticipate and predict customer demands and to provide relevant personalised messages and offers with location-based e-marketing. Second, through a continuous feedback loop, AIeMktg improves offerings by analysing and incorporating insights from a 360-degree view of CE.

The main contribution of this study is to provide theoretical underpinnings of CE, AIeMktg, factors influencing the use of AIeMktg, and customer commitment in the era of Retail 4.0. Subsequently, it builds and validates structural relationships among such theoretical underpinning variables in transforming CE with AIeMktg, which is important for customers to expect a different type of shopping experience across digital channels.

This study proposes a conceptual model and aims (1) to understand how co-creation experience influences guest engagement, (2) to analyse the effect of satisfaction and affective image between co-creation experience and guest engagement, and (3) to analyse the effect of guest engagement on behavioural intentions.

A questionnaire-based online survey was conducted targeting U.S.-based hotel customers. Developed on Qualtrics and distributed via Mechanical Turk, the survey garnered 465 useable responses from individuals who had visited international chain hotels within the last year. Data analysis was performed using Structural Equation Modelling (SEM) in IBM SPSS Amos.

The findings revealed significant direct effects of co-creation experience on affective image and guest satisfaction, with guest satisfaction notably influencing guest engagement, and guest engagement directly influencing behavioural intention. However, both co-creation experience and affective image showed minimal direct impact on guest engagement. The study also highlighted the importance of indirect effects and total effects in understanding the dynamics between the constructs analysed.

The findings revealed significant direct effects of co-creation experience on affective image, with affective image notably influencing guest engagement, and guest engagement directly influencing behavioural intention. Although satisfaction had direct effects on guest engagement, co-creation experience showed minimal direct impact on guest satisfaction and on guest engagement.

This study advises managers to use co-creation primarily to enrich guest experiences and establish emotional connections rather than as a tool for directly enhancing engagement. It recommends that managers invest in strategies to enhance guest engagement beyond co-creation given the direct link between guest engagement and behavioural intentions, and the minimal direct impact between co-creation, guest engagement and satisfaction.

This study demonstrates that affective image is a pivotal mediator between co-creation experience and guest engagement. The findings provide valuable implications for hospitality practitioners in designing and managing co-creation experiences, emphasizing the importance of fostering a positive affective image in the value co-creation process to attain positive behavioural outcomes.

As ecommerce becomes more prevalent, traditional brick-and-mortar retailers such as warehouse clubs (WCs) face the challenging task of maintaining and growing their customer base. This study aims to unravel the combined impact of retail agglomeration and ecommerce activities on consumer foot traffic (also referred to as “footprint”) at WC stores, placing an emphasis on the locational strategies adopted by WCs in this evolving retail landscape.

Mobile-based customer foot traffic data for Costco, a major U.S. WC chain, is sourced for our analysis. We use Principal Component Analysis (PCA) to identify dimensions of general merchandise (GM) and narrow-range merchandise (NM) retail agglomeration. Two-stage least squares (2SLS) regressions are used to explore how the intensity of ecommerce activities and WC locational choices within retail agglomerations impact WC foot traffic.

Our analysis highlights a notable decline in WC store visits attributable to both GM and NM ecommerce activities, with GM ecommerce presenting a more significant competitive challenge to WCs. Regarding retail agglomerations, proximity to GM clusters that include a diverse range of supercenters, department stores, and club stores, is associated with an increase in WC customer visits within their vicinity. In contrast, the influence of NM agglomerations is mixed; clusters adjacent to grocery stores lead to higher WC customer traffic compared to those focused on other specialized stores. These findings underscore the strategic importance of location in mitigating the adverse effects of ecommerce competition. Additionally, our study uncovers intricate dynamics between GM and NM retail clusters and ecommerce activities, demonstrating varied impacts on WC customer footprint.

Access to customer footprint data illustrates the potential of this data source for retail decision making and researchers. Our analysis is limited to one chain, notably Costco.

Our findings underscore the need for retailers to adeptly navigate the evolving retail landscape, including the confluence between physical and digital retail environments, to secure future success. In particular, our results emphasize the benefits of locating stores within mixed retail agglomerations and underline the need to consider the broader retail landscape in location decisions.

The rise of ecommerce in the U.S. has reshaped consumer behavior and altered local shopping districts’ communal dynamics. This change may spur policy interventions to help physical stores compete with online retailers, emphasizing the importance of retail diversity and community-centric environments to sustain communal retail interactions amidst digital advancements.

The paper makes use of a unique dataset to provide a first assessment of the combined effects of retail agglomeration and ecommerce activities on consumer foot traffic for WC retailers. Thus, this paper provides insights into the impacts on consumer shopping behavior from the dynamic interactions between physical retail clusters and online shopping behaviors.