As the e-commerce industry rapidly expands, the number of users and items continues to grow, making it increasingly difficult to capture users’ purchasing patterns. Sequential recommendation models have emerged to address this issue by predicting the next item that a user is likely to purchase based on their historical behavior. However, most previous studies have focused primarily on modeling item sequences using item IDs without leveraging rich item-level information. To address this limitation, we propose a sequential recommendation model called ITS-Rec that incorporates various types of textual item information, including item titles, descriptions, and online reviews. By integrating these components into item representations, the model captures both detailed item characteristics and signals related to purchasing motivation. ITS-Rec is built on a self-attention-based architecture that enables the model to effectively learn both the long- and short-term user preferences. Experiments were conducted using real-world Amazon.com data, and the proposed model was compared to several state-of-the-art sequential recommendation models. The results demonstrate that ITS-Rec significantly outperforms the baseline models in terms of Hit Ratio (HR) and Normalized Discounted Cumulative Gain (NDCG). Further analysis showed that online reviews contributed the most to performance gains among textual components. This study highlights the value of incorporating textual features into sequential recommendations and provides practical insights into enhancing recommendation performance through richer item representations.

Knowledge consulting services are one of the fastest growing fields in the knowledge service industry since the 2010s and have been emerging as a core area of the knowledge economy. Accordingly, consulting services are actively sought and provided in various fields, including business strategy and management, accounting, and ICT, and global consulting firms have experienced rapid growth. However, previous research evaluating the performance or service quality of knowledge consulting services is relatively scarce. In particular, there are barely any studies that apply the data envelopment analysis (DEA) model to measure the relative operating efficiencies of consulting firms in the global consulting service field. This study measured the operating efficiency of 27 global consulting firms using DEA. As global consulting firms are managed differently depending on the characteristics of the country in which they operate, the 27 global consulting firms were classified into three groups by region (USA, Europe, Asia) to measure their meta-efficiency (ME), group efficiency (GE), and technology gap ratio (TGR) and identify the causes of inefficiency at global consulting firms. The contextual variables within consulting firms that affect efficiency were analyzed using Tobit regression. Based on the analysis results, this study suggests strategies for enhancing the operating efficiency and realizing sustainable growth in global consulting firms.

Hwang J, et al. Korean J Radiol. 2025 Jan;26(1):65-74. https://doi.org/10.3348/kjr.2024.0689

The purpose of this study is to verify the importance of follow-up management and customer relationship that may significantly influence consulting success. This study aimed to examine follow-up consulting service and customer relationships after providing consulting service to SMEs with previous consulting experience. The hypotheses were established based on theoretical consideration and previous studies. To investigate the effects of follow-up consulting services on the client's perceived reciprocity, relational embeddedness, corporate trust, and long-term relationship orientation, this study set the research subject as SMEs in Korea with the experience of receiving consulting services. A total of 244 copies of the survey were used to conduct basic statistical research on respondents. The empirical analyses in this study were verified under the conditions satisfying the significance level of 0.05, 0.01, and 0.001, and the statistics were analyzed using SPSS 18.0 and Amos 18.0. As a result of testing the hypothesis with the structural equation model, it was confirmed that follow-up consulting service has significant effects on perceived reciprocity, as well as relational embeddedness, corporate trust, and long-term relationship orientation.

Online review texts serve as a valuable source of auxiliary information for addressing the data sparsity problem in recommender systems. These reviews often reflect user preferences across multiple item attributes and can be effectively incorporated into recommendation models to enhance both the accuracy and interpretability of recommendations. Review-based recommendation approaches can be broadly classified into implicit and explicit methods. Implicit methods leverage deep learning techniques to extract latent semantic representations from review texts but generally lack interpretability due to limited transparency in the training process. In contrast, explicit methods rely on hand-crafted features derived from domain knowledge, which offer high explanatory capability but typically capture only shallow information. Integrating the complementary strengths of these two approaches presents a promising direction for improving recommendation performance. However, previous research exploring this integration remains limited. In this study, we propose a novel recommendation model that jointly considers implicit and explicit representations derived from review texts. To this end, we incorporate a self-attention mechanism to emphasize important features from each representation type and utilize Bidirectional Encoder Representations from Transformers (BERT) to capture rich contextual information embedded in the reviews. We evaluate the performance of the proposed model through extensive experiments using three real-world datasets. The experimental results demonstrate that our model outperforms several baseline models, confirming its effectiveness in generating accurate and explainable recommendations.

With the rise of misinformation epidemic, this study aims to empirically investigate the consequences of an online commenting platform's activity-capping policy on abusers' and regular users' activities. Utilizing a quasi-experimental setting, we find that restrictive policies not only curtail the activity of the abusers but also promote the activity of regular users. Results show that the policy has an asymmetric effect on abusers and regular users-while it effectively reduces the actions of the malicious users by 1.8%, it promotes the activities of the regular users by 2.2%. To better understand the behavioral change of the regular users, we draw from the rational economic perspective of voting decisions and provide initial evidence that such policy measures reinforce the subjective probability of being influential on the outcome. This study will provide valuable implications to managers and policymakers to estimate the consequences of and to combat against malicious behaviors and to promote free speech in online platforms.