1. Introduction

1.1. Research Background

The economy hotel sector in China has experienced substantial growth over the past decade, becoming a vital component of the country’s tourism and hospitality industry. A burgeoning middle class drives this expansion, increased domestic travel, and the rapid urbanization of various regions across China. As the sector grows, so does its contribution to the national economy, providing significant employment opportunities and fostering regional economic development.

In this dynamic landscape, big data has emerged as a crucial element in enhancing recruitment processes within these economy hotel chains. The utilization of big data analytics enables these establishments to refine their recruitment strategies, leading to more efficient and effective hiring practices. Through the analysis of large datasets, hotels can identify the best candidates by matching skills and experience with job requirements more accurately, predicting future workforce needs, and improving overall human resource planning.

The role of big data in this context is not just about the aggregation and analysis of information; it’s about transforming the recruitment process into a strategic asset that can yield competitive advantages. By leveraging predictive analytics, hotels can anticipate industry trends, understand job market dynamics, and tailor their recruitment efforts accordingly. This proactive approach not only enhances the quality of hires but also aligns workforce capabilities with organizational goals and industry requirements.

Moreover, the integration of big data in recruitment processes aligns with the broader trends of digital transformation in the hospitality industry. It signifies a shift towards more data-driven decision-making, which is crucial for economy hotels to thrive in a highly competitive market. The ability to quickly and efficiently process vast amounts of data can help these hotels not only to identify and attract the right talent but also to respond agilely to changing market conditions.

In summary, the expansion of China’s economy hotel sector, coupled with the integration of big data in recruitment, signifies a transformative phase in the industry. This synergy not only enhances the recruitment process but also contributes significantly to the sector’s growth and its role in the broader economic context. The work of Wang & Zhang emphasizes the increasing importance of big data in recruitment, suggesting that the strategic use of data analytics can lead to more informed decision-making and better outcomes in talent acquisition [1].

1.2. Literature Review

The literature on big data in hospitality recruitment highlights its transformative potential, particularly in decision-making and operational efficiency. In their study, Chen and Liu examine the role of big data analytics in streamlining recruitment processes within the hospitality sector. They argue that big data tools can significantly enhance the precision of candidate selection by analyzing vast arrays of applicant data against job requirements. Their research demonstrates how data-driven insights can lead to more informed decisions, reducing the time and costs associated with traditional recruitment methods.

Zhou further explores the impact of big data on the operational efficiency of recruitment in the hospitality industry. His findings suggest that big data not only optimizes the recruitment process but also improves the overall quality of hires. By leveraging predictive analytics and machine learning algorithms, hotels can forecast staffing needs, identify potential talent pools, and proactively address skill gaps. Zhou emphasizes that this proactive approach to recruitment, facilitated by big data, enables hotels to better align their workforce with strategic business objectives.

Both studies underscore the importance of integrating big data analytics into recruitment strategies to enhance efficiency and decision-making. They highlight how big data can provide a competitive edge by enabling more agile and responsive recruitment practices. This is particularly relevant in the fast-paced and ever-changing hospitality industry, where the ability to adapt to market trends and customer demands quickly is crucial.

The literature indicates that big data plays a pivotal role in transforming recruitment practices in the hospitality sector. It enhances the efficiency and effectiveness of recruitment processes, leading to better decision-making and improved operational outcomes. The insights gained from these studies provide a solid foundation for understanding how big data can be leveraged to innovate and improve recruitment in China’s economy hotel chains.

1.3. Research Gap

While the existing literature extensively discusses the advantages of big data in recruitment for the hospitality sector, there is a noticeable scarcity of research specifically addressing its application within China’s budget hotel industry. The studies by Chen & Liu, and Zhou provide valuable insights into the general benefits of big data in hospitality recruitment, but they fall short of delving into the unique challenges and opportunities faced by budget hotels in China [2,3]. These establishments operate under distinct market dynamics and cost constraints, necessitating tailored recruitment strategies that leverage big data effectively.

The gap in research becomes evident when considering the rapid expansion of the budget hotel sector in China and its increasing reliance on digital technologies. Despite this growth, detailed analyses of how big data can be harnessed to address the specific recruitment needs of these hotels are limited. There is a need for an in-depth exploration of the ways in which big data analytics can be integrated into the recruitment processes of China’s economy hotel chains, considering factors like market segmentation, competitive pressures, and the evolving expectations of both employers and job seekers in the digital age.

The potential of big data to transform recruitment practices in this sector is not fully understood or documented. Research is needed to examine the practical applications of big data in improving the efficiency and effectiveness of recruitment, identifying the best practices, and overcoming the challenges specific to China’s budget hotel industry. This includes understanding the data sources, analytical methods, and technological tools that can be most beneficial for these hotels.

Finally, the research gap identified is the lack of focused studies on the application of big data in recruitment within China’s budget hotel industry. Addressing this gap is crucial for developing a comprehensive understanding of how big data can be leveraged to enhance recruitment outcomes, drive operational efficiencies, and contribute to the strategic growth of budget hotels in China’s competitive hospitality market.

1.4. Research Framework

To address the identified research gap, this paper adopts a structured approach, articulated through a series of interconnected sections that collectively aim to explore the application of big data in the recruitment processes of China’s economy hotel chains. The framework is designed to offer a comprehensive analysis of the current state, challenges, and innovative solutions within this sector. Here is how the paper is structured:

1.4.1. Introduction

Sets the stage by highlighting the significance of the study, underscoring the importance of big data in the recruitment processes of the hospitality industry, particularly within China’s economy hotel sector. It establishes the research context and outlines the paper’s objectives.

1.4.2. Literature Review

Provides a synthesis of existing studies on big data in hospitality recruitment, emphasizing the benefits and challenges observed in the broader industry. This section also critically examines the extent to which these findings apply to the budget hotel sector in China, thus leading to the identification of the research gap.

1.4.3. Case Description

Presents a detailed overview of the economy hotel sector in China, focusing on the operational and strategic nuances that influence recruitment practices. This section will use real-world data and examples to illustrate the current use of big data in recruitment within this context.

1.4.4. Analysis of the Problem

Delves into the specific challenges faced by China’s economy hotel chains in their recruitment processes. It analyzes how the underutilization of big data contributes to these issues and examines the implications for operational efficiency and talent acquisition.

1.4.5. Suggestions

Based on the problem analysis, this section proposes innovative solutions and strategies for integrating big data into the recruitment practices of economy hotels in China. It will explore how these hotels can leverage big data analytics and related technologies to enhance their recruitment outcomes.

1.4.6. Conclusion

Summarizes the key findings of the research, emphasizing how the study addresses the research gap. It highlights the potential business and societal impacts of adopting big data-driven recruitment strategies in China’s economy hotel sector. The conclusion also outlines the limitations of the current research and suggests directions for future studies.

Following this framework aims to provide a thorough understanding of how big data can be utilized to innovate and improve recruitment management in China’s economy hotel chains, thus filling the existing research void.

2. Case Description

2.1. An Overview of China’s Budget Hotel Industry

China’s budget hotel industry has experienced significant growth over the past few decades, becoming an important part of domestic tourism and business travel. These hotels attract a wide range of low - and middle-income travelers with their affordable prices and reasonable quality. With the rapid development of China’s economy and the vigorous development of domestic tourism, budget hotels have sprung up all over large, medium, and small cities to meet the market demand for budget accommodation.

2.2. Operational and Strategic Details of Recruitment Practices

At the operational level, traditional recruitment methods such as Posting job advertisements, campus recruitment, and human resource recommendations are still prevalent in budget hotels. However, with the intensification of market competition and the development of technology, these hotels began to seek more efficient and accurate recruitment methods. Strategically, the application of big data is beginning to be seen as a key means of optimizing the recruitment process and improving the quality of recruitment.

2.3. The Application of Big Data in Recruitment

In recent years, some leading budget hotel chains have begun to actively integrate big data analytics into their recruitment processes. By analyzing job applicants’ online behavior, historical work experience, and skill matches, these hotels can more accurately select and attract the right candidates. For example, a well-known budget hotel chain successfully built a candidate evaluation model by analyzing a large amount of resume data and interview feedback, which greatly improved recruitment efficiency and employee job fit.

2.4. Case Study

For example, Huazhu Hotel Group uses big data technology to analyze the ability of job seekers and predict recruitment trends, realizing accurate prediction of talent demand and optimization of recruitment decisions. In this way, Huazhu not only improved the efficiency and effectiveness of recruitment but also optimized the structure and quality of employees. In addition, a budget hotel brand introduced AI and machine learning algorithms to analyze the effectiveness of online job advertisements and adjust recruitment strategies so as to attract the right job seekers more effectively. The application of these technologies enables hotels to process and analyze a large amount of recruitment data in a short period of time and quickly identify the best candidates.

2.5. Conclusion

Through detailed case descriptions, this study can see that China’s budget hotel industry has begun to adopt big data and related technologies in recruitment practices actively. These practices not only improve the efficiency and effectiveness of the recruitment process but also have a profound impact on hotel operations and strategic planning. Despite the challenges in technology investment and data management, the application of big data and artificial intelligence has shown great potential and value in improving recruitment quality and operational efficiency. With further development and application of technology, it is expected that China’s budget hotel industry will continue to achieve innovation and enhancement in recruitment management.

3. Analysis on the Problem

3.1. Problem Identification

The Chinese economy hotel sector, despite its rapid growth, faces several recruitment challenges that hinder its ability to acquire and retain the right talent. Two primary issues are skill mismatches and process inefficiencies, which are exacerbated in the context of big data and technological advancements.

3.1.1. Skill Mismatches

The disparity between the skills required by economy hotels and those possessed by job applicants is a significant concern. Li & Zhou highlight that the rapid evolution of customer expectations and technological integration in the hospitality industry demands a workforce with a new set of skills, including digital literacy, customer service excellence, and adaptive problem-solving [4]. However, the current recruitment practices often fail to assess and match these evolving skills with job requirements accurately.

3.1.2. Process Inefficiencies

Sun & Li discuss the inefficiencies inherent in the traditional recruitment processes of the Chinese economy hotel sector [5]. These inefficiencies manifest in various ways, such as prolonged hiring times, high costs associated with recruitment, and the inability to process and analyze large volumes of applicant data effectively. The lack of streamlined processes and reliance on conventional recruitment methods result in missed opportunities to capture and utilize data for making more informed and strategic hiring decisions.

These challenges are not isolated but are interrelated, contributing to a cycle that perpetuates inefficiency and ineffectiveness in recruitment. Skill mismatches lead to a workforce that may not be fully equipped to meet the demands of the modern hospitality environment, while process inefficiencies prevent the quick identification and integration of suitable candidates into the organization.

The integration of big data and analytics could be pivotal in addressing these issues. However, the current underutilization of these technologies suggests a gap between the potential of big data to transform recruitment practices and the actual application within the economy hotel sector. This gap underscores the need for a strategic approach to integrating data-driven decision-making processes into the recruitment strategies of these hotels.

For the time being, the Chinese economy hotel sector faces significant recruitment challenges, primarily in terms of skill mismatches and process inefficiencies. To remain competitive and responsive to the rapidly changing market dynamics, there is a pressing need for these hotels to embrace big data and analytics, transforming their recruitment processes to be more aligned with the digital age. Addressing these challenges through innovative recruitment strategies can significantly enhance the sector’s ability to attract, hire, and retain the right talent, ultimately contributing to improved service quality and business growth.

3.2. Reasons Analysis

The recruitment challenges in China’s economy hotel sector, specifically skill mismatches and process inefficiencies, can be attributed to several underlying causes, notably inadequate data utilization, and outdated recruitment strategies.

3.2.1. Inadequate Data Utilization

Gao & Zhang point out that the economy hotel sector often fails to leverage the vast amounts of data at its disposal effectively [6]. This under-utilization stems from a lack of robust data analytics infrastructure and the necessary expertise to analyze and interpret data for recruitment purposes. Many hotels still rely on traditional data processing methods, which are not only time-consuming but also inadequate for extracting actionable insights from complex datasets. This limitation hinders the ability of hotels to identify and target the right talent, leading to skill mismatches and suboptimal hiring decisions.

3.2.2. Outdated Recruitment Strategies

Wu & Wang highlight that many economy hotels in China continue to use traditional recruitment strategies, which are increasingly becoming obsolete in the digital era [7]. These outdated methods often involve manual screening of applications, standard job postings, and conventional interview processes, which lack the dynamism and flexibility required to attract and assess the modern workforce. Such strategies are not equipped to handle the scale and complexity of today’s job market, where digital platforms and online networks play a crucial role in talent acquisition and recruitment marketing.

The persistence of these outdated recruitment strategies is partly due to a reluctance to change and a lack of awareness of the benefits that big data and advanced analytics can bring to the recruitment process. Additionally, there is often a gap in the skill sets required to implement and manage data-driven recruitment processes effectively, which exacerbates the issue.

To address these problems, the economy hotel sector must evolve its recruitment practices, embracing more sophisticated data analytics tools and digital platforms that can streamline the hiring process, improve the accuracy of candidate matching, and ultimately enhance the efficiency and effectiveness of recruitment. This transition not only involves the adoption of new technologies but also a cultural shift towards valuing data-driven decision-making and continuous learning and adaptation in recruitment practices.

4. Suggestions

4.1. Strategy for Problem 1: Skill Mismatches

To address the issue of skill mismatches in China’s economy hotel sector, the deployment of sophisticated data analytics for accurate candidate profiling and matching is crucial. Zhang & Ren and Liu & Zhao both emphasize the potential of advanced data analytics to revolutionize recruitment by enabling more precise matching of candidates’ skills with job requirements [8,9].

4.1.1. Implementation of Advanced Data Analytics

1. Candidate Profiling

Utilizing data analytics tools can help in creating comprehensive profiles for each candidate. These profiles would include not only basic information such as education and work experience but also insights into candidates’ soft skills, behavioral traits, and potential for growth. Machine learning algorithms can analyze past employment history, educational background, and even social media activities to provide a holistic view of each candidate.

2. Matching Algorithms

Developing sophisticated algorithms that can match candidates with job vacancies more accurately is essential. These algorithms can assess the compatibility between a candidate’s profile and the job’s requirements by considering various factors, including skill sets, cultural fit, and career aspirations. This approach can significantly improve the quality of hires by ensuring that the selected candidates possess the requisite skills and are likely to thrive in their new roles.

3. Continuous Learning and Improvement

The data analytics system should be designed to learn continuously from each recruitment cycle, using feedback and outcomes to refine the matching algorithms. This iterative process will enhance the accuracy and efficiency of candidate profiling and matching over time.

4. Integration with Recruitment Platforms

The analytics tools should be seamlessly integrated with existing recruitment platforms and job portals to facilitate a smooth flow of data and enable real-time matching and decision-making.

4.1.2. Benefits

Implementing sophisticated data analytics for recruitment can yield several benefits:

1. Reduction in Skill Mismatches

By ensuring a better alignment between candidates’ skills and job requirements, the frequency of skill mismatches can be significantly reduced.

2. Improved Hiring Quality

More accurate matching will likely lead to higher employee satisfaction and retention rates as employees find their roles more fulfilling and suited to their skills.

3. Enhanced Efficiency

Streamlining the recruitment process through data analytics can save time and resources, allowing hotels to focus on strategic aspects of human resources management.

By embracing advanced data analytics for candidate profiling and matching, China’s economy hotel sector can overcome the challenges of skill mismatches, thereby enhancing its recruitment outcomes and overall operational efficiency.

4.2. Strategy for Problem 2: Process Inefficiencies

To tackle the issue of process inefficiencies in recruitment within China’s economy hotel sector, the adoption of automated recruitment systems powered by artificial intelligence (AI) and machine learning is recommended. The insights provided by Wang & Cheng and Zhao & Li underline the effectiveness of such technologies in streamlining recruitment processes and enhancing overall efficiency [10,11].

4.2.1. Implementation of AI-Powered Recruitment Systems

1. Automating Screening Processes

AI can be utilized to automate the initial screening of applications, quickly filtering out candidates who do not meet the basic criteria for a job. This automation speeds up the recruitment process, reduces manual workload, and ensures that the human resources team can focus on more strategic tasks.

2. Machine Learning for Enhanced Candidate Selection

Machine learning algorithms can analyze historical recruitment data to identify patterns and preferences that lead to successful hires. These insights can then be used to refine the selection process, ensuring that candidates who are most likely to succeed in the organization are prioritized.

3. Predictive Analytics for Workforce Planning

AI systems can use predictive analytics to forecast future recruitment needs based on business growth, turnover rates, and market trends. This proactive approach allows for better planning and resource allocation, reducing the time and cost associated with last-minute hiring.

4. Integration with Digital Platforms

The automated recruitment system should be integrated with digital platforms like LinkedIn, job portals, and social media to widen the search for candidates and leverage data from these sources for more informed decision-making.

4.2.2. Benefits

The introduction of AI and machine learning in recruitment offers several advantages:

1. Streamlined Recruitment Process

Automation can significantly reduce the time-to-hire by swiftly managing large volumes of applications and efficiently shortlisting candidates.

2. Improved Candidate Quality

AI-enhanced systems can better identify candidates who not only fit the job requirements but also align with the company’s culture and values, thus improving the quality of hires.

3. Data-Driven Decisions

Utilizing AI and machine learning enables data-driven decision-making in recruitment, minimizing biases and improving the objectivity and fairness of the selection process.

4. Cost-Effectiveness

By automating routine tasks and optimizing recruitment strategies, AI-powered systems can help reduce the overall cost of hiring. Implementing automated recruitment systems equipped with AI and machine learning technologies will address the process inefficiencies in China’s economy hotel sector. This strategic move can lead to more efficient, effective, and scalable recruitment processes, positioning these hotels to meet their workforce needs better and adapt to the rapidly evolving hospitality industry landscape.

5. Conclusion

5.1. Key Findings

This study has unearthed significant insights into the recruitment challenges and opportunities within China’s economy hotel sector, which is predominantly influenced by the integration of big data and artificial intelligence (AI). The analysis revealed that while the sector is burgeoning, it grapples with issues like skill mismatches and process inefficiencies. Big data analytics and AI have emerged as pivotal in addressing these challenges, offering a pathway to refine recruitment strategies and enhance operational efficiency. The suggestions put forth, focusing on advanced data analytics for skill matching and AI-driven systems for streamlining recruitment processes, highlight the potential for substantial improvements in talent acquisition and management.

5.2. Research Significance

The implications of this research extend far beyond the academic realm, offering substantial business value to the economy hotel sector and potentially the broader hospitality industry. By delineating how big data and AI can transform recruitment practices, the study provides a blueprint for hotels to gain a competitive edge, improve employee alignment with organizational goals, and ultimately enhance service quality and customer satisfaction. This research contributes to the ongoing discourse on digital transformation in the hospitality industry, illustrating how technological advancements can solve enduring problems like inefficient recruitment and skill discrepancies.

5.3. Limitations and Future Studies

While this research provides a comprehensive overview of the impact of big data on recruitment in China’s economy hotel sector, it predominantly relies on secondary data sources. The absence of primary data might limit the depth of insights into the nuanced experiences of individual hotels and their specific recruitment challenges. Future research should aim to incorporate primary data through methodologies such as surveys or interviews with hotel management and HR professionals. This approach would not only enrich the understanding of the practical applications of big data and AI in recruitment but also help identify bespoke challenges and opportunities at a granular level. Furthermore, longitudinal studies could shed light on the long-term effects of these technological integrations on the sector’s recruitment outcomes and overall performance.

By addressing these limitations and exploring these future research avenues, scholars and industry practitioners can continue to build on the foundational knowledge established in this study, driving further innovation and improvement in recruitment practices within the hospitality industry.