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Given the competitive landscape in the higher education setting, it is important that universities adopt strategies that create competitive advantage for them. Universities must leverage their resources efficiently to address this goal. Creating a positive brand image is one such strategy. The purpose of this paper is to conceptualize university brand image as its heritage, service quality and trustworthiness and investigate their relationship with student’s satisfaction. It also investigates the role of university reputation as a mediating variable.

Data were collected through a mixed method approach. The first stage involved qualitative interviews and focused group discussions with students to understand the factors responsible for student satisfaction with their respective universities. The second stage involved administering a survey questionnaire in two geographies – the USA and India to investigate the hypothesized relationship. The authors use regression analyses to test these relationships.

Findings indicate that a distinct brand image plays an important role in students’ level of satisfaction across both the USA and India. Service quality has a greater impact on student satisfaction levels across both contexts (as compared to university heritage and trustworthiness). The authors also find a positive mediating effect of university reputation in the relationship between university brand image and student satisfaction levels.

The current research contributes to the services marketing literature in the university context. It offers a framework for decision making in universities. It suggests that universities must work toward developing their brand image by focusing on its three dimensions – heritage, trustworthiness and service quality.

The purpose of this study is to comprehensively explore the password manager adoption landscape, delving into crucial factors such as performance, trust, social influence, self-efficacy, risk perception, security concerns, enjoyment and facilitating conditions. It also aims to contribute meaningful insights to security product research and practice.

A survey was used to investigate the characteristics of adoption intention for password managers. In total, 156 participants from a public university located in the Midwest region of the USA voluntarily completed the survey. Partial least squares structural equation modeling was used to estimate and validate causal relationships and the proposed research model.

Through empirical validation, this study demonstrates that constructs such as social influence, web-specific self-efficacy and perceived risk directly impact trust in password managers. Facilitating conditions and perceived security controls are identified as direct influencers on performance expectancy, deviating from the pathways of the traditional framework. Moreover, the model introduces novel elements crucial for comprehending password manager adoption, including “web-specific self-efficacy” and “perceived security control.”

The paper systematically reviews existing research on password managers, shedding light on crucial factors significantly influencing adoption behavior. By introducing deviations from conventional frameworks and theories, the study emphasizes the innovative nature of its model. It also formulates strategies to catalyze wider adoption and promote effective design of password managers, increasing user engagement rates.

Studies on mining text and generating intelligence on human resource documents are rare. This research aims to use artificial intelligence and machine learning techniques to facilitate the employee selection process through latent semantic analysis (LSA), bidirectional encoder representations from transformers (BERT) and support vector machines (SVM). The research also compares the performance of different machine learning, text vectorization and sampling approaches on the human resource (HR) resume data.

LSA and BERT are used to discover and understand the hidden patterns from a textual resume dataset, and SVM is applied to build the screening model and improve performance.

Based on the results of this study, LSA and BERT are proved useful in retrieving critical topics, and SVM can optimize the prediction model performance with the help of cross-validation and variable selection strategies.

The technique and its empirical conclusions provide a practical, theoretical basis and reference for HR research.

The novel methods proposed in the study can assist HR practitioners in designing and improving their existing recruitment process. The topic detection techniques used in the study provide HR practitioners insights to identify the skill set of a particular recruiting position.

To the best of the authors’ knowledge, this research is the first study that uses LSA, BERT, SVM and other machine learning models in human resource management and resume classification. Compared with the existing machine learning-based resume screening system, the proposed system can provide more interpretable insights for HR professionals to understand the recommendation results through the topics extracted from the resumes. The findings of this study can also help organizations to find a better and effective approach for resume screening and evaluation.

Unmanned aerial vehicles (UAVs), known for their exceptional flexibility and maneuverability, have become an integral part of mobile edge computing systems in edge networks. This paper aims to minimize system costs within a communication cycle. To this end, this paper has developed a model for task offloading in UAV-assisted edge networks under dynamic channel conditions. This study seeks to efficiently execute task offloading while satisfying UAV energy constraints, and validates the effectiveness of the proposed method through performance comparisons with other similar algorithms.

To address this issue, this paper proposes a task offloading and trajectory optimization algorithm using deep deterministic policy gradient, which jointly optimizes Internet of Things (IoT) device scheduling, power distribution, task offloading and UAV flight trajectory to minimize system costs.

The analysis of simulation results indicates that this algorithm achieves lower redundancy compared to others, along with reductions in task size by 22.8%, flight time by 34.5%, number of IoT devices by 11.8%, UAV computing power by 25.35% and the required cycle for per-bit tasks by 33.6%.

A multi-objective optimization problem is established under dynamic channel conditions, and the effectiveness of this approach is validated.

The purpose of this paper is to deconstruct the multi-faceted dimensions of Chinese travellers’ image of boutique hotels with a large amount of online textual data from social media (53,427 reviews written from 2014 to 2018), reinforcing the value creation of user-generated content via social media.

With the aid of Python, a computer language, online textual reviews (53,427 reviews) of 86 high-end boutique hotels in seven cities (Beijing, Shanghai, Hangzhou, Nanjing, Chengdu, Qingdao and Sanya) were collected from the top-ranked online travel agency in China, Ctrip.com. Then, the overall perceived image of boutique hotels was revealed with the aid of Python.

The results showed multiple dimensions of the image of boutique hotels. The overall image can be grouped into eight dimensions (room, service, food, environment, entertainment, location, price and value, and uniqueness). An affective image based on eight dimensions was further developed in the Chinese boutique hotel context. It appears that online data from social media are beneficial for hotel managers to learn travellers’ overall perceptions of boutique hotels and help put more effective management strategies in place in the hospitality industry.

The relationship between cognitive image and affective image should be further investigated in future research. Theoretical implications are discussed from both cognitive image and affective image perspectives in the boutique hotel context. Managerial implications are highlighted to help industry managers understand the travellers’ perceptions of the hotels, via online data from social media, and put more effective hotel strategies in hospitality industry.

By using textual online data from social media, this paper deconstructs both the cognitive image and the affective image of boutique hotels. The dimensions of the most frequently mentioned concepts related to the Chinese boutique hotel industry are profoundly deconstructed, as is the uniqueness of the image of boutique hotels. The work is valuable for promoting effective marketing strategies in the hotel industry.

The authors hope to filter valuable information from online reviews, obtain objective and accurate information about the demands of auto consumers and help auto companies develop more reasonable production and marketing strategies for healthy and sustainable development. This paper aims to discuss the aforementioned objectives.

The authors collected review data from online automotive forums and generated a corpus after pre-processing. Then, the authors extracted consumer demands and topics using the LDA model. Finally, the authors used a trained Word2vec tool to extend the consumer demand topics.

Different types of vehicle consumers have the same demands, such as “Space,” “Power Performance,” and “Brand Comparison,” and distinct demands, such as “Appearance,” “Safety,” “Service,” and “New Energy Features”; consumers who buy new energy vehicles are still accustomed to comparing with the brands or models of fuel vehicles; new energy vehicles consumers pay more attention to services and service quality during the purchasing and using process.

The development time of new energy vehicles is relatively short, with some models being available for only one year or even six months. The smaller amount of available data may impact the applicability of topic models. The sample size, especially for new energy vehicles, needs to be increased to improve the general applicability of topic models further.

First, this measure helps online review websites improve their existing review publication mechanisms, enhance the overall quality of online review content, increase user traffic and promote the healthy development of online review websites. Second, this allows for timely adjustments in future product production and sales plans and further enhances automotive companies' ability to leverage online reviews for Internet marketing.

The authors have improved the accuracy and stability of the fused topic model, providing a scientific and efficient research tool for multi-dimensional topic mining of online reviews. With the help of research results, consumers can more easily understand the discussion topics and thus filter out valuable reference information. As a result, automotive companies may gain information about consumer demands and product quality feedback and thus quickly adjust production and marketing strategies to increase sales and market share.

The purpose of this paper is to summarize the China Agricultural Economic Review (CAER)'s second annual conference, which was organized by the CAER editorial office and International Food Policy Research Institute (IFPRI).

The conference theme was “Agriculture and the Wealth of Nations”, aiming to explore the importance of agriculture as well as the relationship and interaction between agriculture and the whole economy. The attendees from 14 countries discussed the related issues, and a number of distinguished scholars and policy makers were also invited to present at the conference. This summary presents the topics covered at the conference and highlights discussion points.

Action items were identified which could be appropriately organized into the following sections: agricultural trade and rural labor issues; rural governance and public policy; climate change and food security; rural land and rural finance issues.

The paper illustrates how the academic platform established by the CAER‐IFPRI conference, enables scholars from varied cultures and fields to get together to share their researches and ideas.

Despite the importance of top management team (TMT) gender diversity in a firm's strategic decisions and the high degree of innovation activities that several firms have experienced in recent years, little or no research has examined how TMT gender diversity affects a firm's open innovation decision. The authors examine how TMT gender diversity impacts firms' open innovation activities. The authors further examine how this impact is affected by women executives' personal attributes and institutional conditions.

The sample comprised of 62,745 firm-year observations (9,831 firms) from 25 countries from 1990 to 2010. The authors employed the system generalized method of moments (GMM) estimation technique to estimate the results.

Employing novel panel data on co-owned patents across 25 economies, the authors find that proportion of women in TMTs has a positive impact on open innovation activities. Moreover, the authors find that women managers' power and institutional gender parity strengthen the association between gender diversity and open innovation.

The findings of this study indicate that firms committed to optimizing their open innovation policies and practices should include women in TMTs and create such conditions that are supportive for women executives to effectively express their innate inclinations. Importantly, our study supports the business case for gender diversity in top leadership positions by providing a compelling evidence for the positive impact of TMT gender diversity on open innovation.

This study contributes to the gender diversity literature by showing how women leaders' values and character become embedded in their companies' strategy and present empirical evidence that having women in TMTs increase the likelihood of conducting open innovation. Further, the authors show how women executives' power and institutional level gender parity provide boundary conditions that moderate the relationship between TMT gender diversity and open innovation.

This study aims to explore the effect of digitalization on the promotion of enterprise R&D cooperation, and it analyzes the microimpact mechanism and boundary conditions of enterprise digitalization on enterprise R&D cooperation.

Based on survey data sourced from the World Bank Enterprise Surveys of the business environment of Chinese enterprises in 2012, this study applies multiple regression methods to test theoretical hypotheses.

Enterprise digitalization positively affects the breadth and intensity of enterprise R&D cooperation. Employees’ digital literacy plays an intermediary role between enterprise digitalization and enterprise R&D cooperation. The subordinate attributes of enterprises weaken the positive relationship between enterprise digitalization and the breadth and intensity of enterprise R&D cooperation. The shareholding of state-owned enterprises reinforces the positive relationship between digitalization and the intensity of enterprise R&D cooperation. However, such shareholding shows no significant regulatory effect on digitalization and the breadth of enterprise R&D cooperation.

Focusing on the digital transformation of the enterprise, this study discusses its impact mechanism on enterprise R&D cooperation, including the impact on the intensity and breadth of R&D cooperation. The study further examines the regulatory effect of organizational inertia on enterprise digital and R&D cooperation from two aspects: resource rigidity and routine rigidity. It emphasizes the significance of the digital literacy of employees in enterprise digitalization and discusses the micromechanism of enterprise digitalization and enterprise R&D cooperation.