+44 3308180992Review Article - International Research Journal of Engineering Science, Technology and Innovation ( 2025) Volume 11, Issue 1
Received: 20-Oct-2023, Manuscript No. irjesti-24-117644; Editor assigned: 23-Oct-2023, Pre QC No. irjesti-24-117644 (PQ); Reviewed: 06-Nov-2023, QC No. irjesti-24-117644; Revised: 10-Jan-2025, Manuscript No. irjesti-24-117644 (R); Published: 17-Jan-2025, DOI: 10.14303/2315-5663.2025.121
Families in today's fast-paced society sometimes struggle with domestic duties owing to hectic schedules, relying on outside aid. Based on user behavior and location, a "Rental Work Management System" app provides customized service provider suggestions. Meanwhile, a study article describes a suggested method for rental job management applications that use collaborative filtering to improve productivity and satisfaction. The weather has a huge influence on outdoor sectors, leading to the development of a weather-based recommendation system to improve worker safety and productivity. Furthermore, chatbots are becoming increasingly important in rental labor administration, enhancing productivity and user happiness. These developments offer simplified processes, empowered service providers, and adaptive answers to everyday problems.
Rental, Weather, Recommendation, Chatbots, Outdoor sectors
A ubiquitous issue occurs in the ever-changing terrain of the modern world, where busy schedules entangled with occupations and frenetic lifestyles have become the norm: The paucity of time for vital domestic activities (Singh S et al., 2015). This typical problem affects a wide range of society, with employed folks particularly caught up in a maelstrom of duties. As a result, making time for everyday tasks appears to be a luxury of a bygone era (Cai X et al., 2000). This issue has resulted in an increasing reliance on competent service providers to handle everyday domestic activities, changing a once-routine component of life into a critical challenge (Ma Y et al., 2006).
In this day and age, finding experienced persons who can manage a variety of domestic jobs has become a time consuming procedure (Mingguang Z et al., 2009). Seeking suggestions from friends, peers, or internet platforms has become a time-consuming process, with each advice representing a possible step toward the appropriate answer (Zhang H et al., 2010). Even when a list of qualified candidates is available, picking on the best applicant for a certain assignment might be difficult. Geographic proximity, pricing, job type, and availability must all be carefully considered, which sometimes results in lengthy decision- making timescales (Masmali O et al., 2020).
Beyond these considerations, there is a subset of home duties that even the most resourceful households fail to complete. These jobs need a degree of experience and elegance that beyond the capability of the average person. In some cases, skilled service providers' advice and assistance become non-negotiable. Identifying the best professional for the job may be a difficult task in and of itself, fraught with ambiguities and complexity. As a result, a slew of issues impedes the smooth coordination of home duties and the successful participation of trained personnel.
The notion of a "Rental Work Management System" emerges to address these issues and pave the way for a more streamlined and effective approach. This new online application aims to bridge the gap between those dealing with home responsibilities and experienced service providers willing to lend a hand. The world of technology comes in as a facilitator of solutions through this digital conduit, connecting the comfort of modern interfaces with the practicality of everyday living.
The suggested system, which is based on technology, promises to alter the way service providers and clients interact. It heralds a paradigm change away from human procedures and toward automated technology, providing a seamless experience for both parties. Like this transformation is exemplified by the use of numerous technologies, such as GPS (Global Positioning System) technology, real-time data processing, machine learning, and search engine technology. These parts work together to build a dynamic platform on which service providers can respond to customers' demands with exceptional efficiency, and clients may assign duties with the confidence of dependability and timeliness (Cao X, 1999).
The implementation of a complex suggestion process is critical to the success of this system (Fitsilis P, 2009). The client-side recommendation system is one of its key components. This algorithmically driven feature leverages user behavior and choices to generate tailored suggestions based on the user's geographical location and service needs (Azam F et al., 2017). The system guarantees that the ideas presented are relevant and correct by using content-based recommendation algorithms, reducing the uncertainty sometimes connected with selecting the proper service provider. The beauty of this component is its capacity to change in real time; when users engage with the web site and provide input on the recommended services, the suggestions evolve naturally, adapting to the users' changing requirements and preferences (Jindal R et al., 2013).
The suggested system broadens its effect by meeting the diverse needs of service providers. It includes a recommendation component for service providers, the technological foundation of which is based on collaborative filtering algorithms. This smart algorithm examines past data on client bookings and the services requested. This data is analyzed to identify patterns and trends that show places with strong demand for certain services. With this knowledge, service providers may make smart job-selection decisions, maximizing their time and resources while capitalizing on market trends.
In addition, the system envisions an assistive chatbot, a virtual helper aimed to improve accessibility for visually impaired users as well as those seeking expedited support. This chatbot acts as a competent advisor, assisting users ISSN: 2315-5663 with a variety of activities ranging from service provider searches and reservations to obtaining relevant information and troubleshooting. Its presence enhances the user experience, transforming the system into one that is really user-centric and friendly.
The system's benefits are extended to outdoor businesses, where weather can have a substantial influence on work schedules, safety, and overall worker satisfaction. A weather- based recommendation component appears, providing customized activity ideas based on current weather conditions. This aspect acknowledges the symbiotic link between weather and outdoor employment, and provides workers with cautious advice that improve their work environment, safety, and productivity.
The rental work management system does not work in isolation; rather, it is a symphony of interconnected components that work together to ease the inherent difficulties of modern life. As technology continues to reshape daily operations, this system exemplifies how innovation may break through boundaries and improve people's quality of life. This digital progress bridges the gap between time limits, geographical complexity, and the ever- changing needs of everyday life by linking competent service providers with individuals in need.
Problem statement
The rising demands of modern lifestyles have resulted in a lack of time for people to perform home duties. As a result, there is a rising demand for effective and simple methods to link people with experienced service providers who can help them with their regular domestic responsibilities. Existing methods of locating and choosing service providers can require time-consuming techniques such as soliciting recommendations from others or manually looking for qualified individuals. Furthermore, the complexities of various domestic activities, as well as people' different tastes, complicate the process of locating the correct service provider. To solve these issues, the rental work management system presents a client-side recommendation tool, with the goal of personalizing suggestions based on user location and service needs. It monitors user behavior using content-based algorithms to identify the best service providers. The problem is in developing an accurate system that matches user preferences with appropriate suppliers, which is critical for increasing customer happiness and expediting the process. A suggestion mechanism is also offered within the rental work management mechanism to improve service provider efficiency and opportunity. It provides customized job recommendations based on user data such as location and service type. It also supports location-based searches and identifies high-demand locations for targeted service advertising. And this app has an assistive chatbot tool for visually impaired persons and regular people to help them book and reserve such service providers utilizing the voice assistant feature. And this function includes all of the options available to regular users. Due to a lack of real-time, tailored, and location-specific solutions, weather-dependent workers such as painters, plumbers, and carpenters lack efficient activity suggestions. To overcome this, a research based component is required to provide precise, location based recommendations, hence improving productivity and planning.
Significant
The rental work management system includes a client- side suggestion tool to quickly link customers with professional service providers. It optimizes provider selection based on preferences and geography, increasing efficiency and consumer happiness. It is powered by smart technology and algorithms. Furthermore, by intelligently matching providers with jobs based on user data, the system's service provider recommendation component improves the industry. This increases efficiency, personalization, and data-driven insights, allowing for better rental job management and the generation of new business opportunities. Additionally, this online application provides an assistive chatbot for both visually impaired and regular consumers. This function offers the same booking and reservation options as normal. At the same time, the research-driven component enhances the workflow of weather-dependent personnel by providing real- time activity suggestions. This invention improves decision making, productivity, and safety by connecting weather and work duties, allowing for more efficient planning and response.
Objectives
Hypotheses
The increasing number of employed individuals who lack the time or expertise to manage their household chores has led to a growing demand for "Rental Work Management Systems". This system can help users to find and select qualified service providers, based on their location, needs, and preferences. And the system uses collaborative filtering recommendation and content-based filtering recommendation algorithms to identify patterns and trends in demand for services, and make weather-based recommendations to avoid scheduling jobs during inclement weather. Additionally, assistive chatbots can be used to improve accessibility and provide timely support to users. Not only that this hypothesis is testable, because it can be evaluated by conducting a study to compare the effectiveness of the "Rental Work Management System" to other methods of finding and managing service providers. The study could measure factors such as the time it takes to find a qualified service provider, the cost of the services, and the overall satisfaction of the users.
Research question
How do people "Optimizing Service Provider Selection and Availability in a rental work management app for employed individuals' household chores"? The focus of this research question is to ask how people living a busier lifestyle can easily accomplish their daily household chores with the help of a “Rental work management web application”. Because most employed individuals have to seek the help of other skilled workers to manage their daily household chores, and it may take time to decide on the 'right person'. Additionally, some household chores are impossible to do by themselves without the guidance and aid of an expertise/service provider. To minimize these issues, an application on a rental work management system is being developed. We are creating an App that allows customers to find the relevant skilled service providers at the required task from the vicinity of their locality. Customers can choose a service provider based on ratings and weather reports, and if the service provider is not available, they can select another service provider. This App allows the customer to be in direct contact with the service provider, providing a valuable service to employed individuals who are busy with their work schedules and unable to pay much attention to their daily household chores.
Summary of paper content
This research paper addresses the pressing challenges faced by individuals leading busy lives in efficiently managing their household chores. The conceptual framework of a cutting- edge "Rental Work Management System" app is introduced to bridge the gap between the demand for skilled service providers and the need for efficient chore management. The paper delves into the intricacies of the proposed app, exploring the algorithmic foundations behind each key component.
It investigates how personalized suggestions are generated based on user behavior and geographical data, optimizing service provider choices. Collaborative filtering techniques are highlighted for enhancing job management efficiency. The paper further investigates the integration of weather based recommendations to enhance worker safety, efficiency, and overall job satisfaction.
Moreover, the incorporation of an assistive chatbot is discussed in the context of aiding visually impaired users and delivering expedited support. The interplay of these components is emphasized to create a user-centric and efficient household chore management ecosystem. The methodology section outlines the intricate steps undertaken to develop and seamlessly integrate these components, showcasing the role of machine learning algorithms, data analysis, and iterative design.
The literature review emphasizes the value of measuring software complexity for evaluating the efficacy of sizable object-oriented software systems. To provide a more thorough assessment of software complexity, Theisz and colleagues' research proposed a hybrid set of ten complexity metrics that included the advantages of existing metrics. The Chidamber and Kemerer metrics suite and the MOOD metrics suite are two more academics' proposed complexity metrics, however they might not give a complete picture of software complexity. Overall, the literature analysis underlines the necessity for thorough software complexity measurements, and more study is required to determine how well these metrics perform across various frameworks and programming languages.
Not only that. The paper "component-based software engineering: Technologies, development frameworks, and quality assurance schemes" by Cai et al. provides an overview of Component Based Software Engineering (CBSE), including its fundamental concepts, development explain on how CBSE can lead to more productivity, enhanced application quality, and lower development expenditures. They additionally bring attention to the difficulties faced by CBSE, such as the importance for efficient quality assurance programs and standardized component models. The article concludes by outlining promising research paths for CBSE's future development. The paper "a complexity metrics set for large-scale object oriented software systems" proposes a set of metrics to measure software complexity in large-scale object-oriented systems. The authors collected data from three open- source systems and applied the proposed metrics to measure their complexity. Results showed that the proposed metrics could provide useful information to identify the potential risks in software systems. The study also identified areas for future research, including the relationship between complexity and software quality, and the validation of the proposed metrics on a larger sample of systems.
The authors of this article, Mingguang Z, Haohua Z, Weiyi Q, Shijun M, and Chuanyi W, offer a set of measures for evaluating the complexity of large object-oriented software systems. Size, coupling, cohesion, and inheritance metrics are among the metrics. The results of the experiments the authors performed on two large software systems demonstrated that the suggested metrics are useful for evaluating software complexity. And on the other hand, Zhang H, Wu L, Qi W, Wu J, and Ma S provide a new measurement technique based on object-oriented design principles for large-scale object-oriented software systems. The proposed method was tested by the authors on a number of large-scale software systems, and the findings demonstrated that it is useful for determining which software components require restructuring as well as quantifying software complexity.
Omar M and Badreddin O proposes about comprehensive set of model-driven complexity metrics for software systems in their research paper. The metrics are based on modeling the software system as a set of interconnected components and measuring the complexity of each component. The proposed metrics are evaluated through a case study and the results show that they are effective in identifying the most complex components of the software system.
The evaluation of software development using complexity measures is covered in the research paper. The author Cao, X is providing a thorough review of different software complexity indicators and how well they work for evaluating software quality. The paper also discusses how these metrics are really used in software development and offers information on the constraints of complexity measurements. The study comes to the conclusion that complexity measures are helpful tools for assessing software quality, but they shouldn't be the only ones used.
Customer recommendation
In here, after having some sessions with our supervisor then I finalized how to do this component, and identified and gather what are the requirements that I need for my component. So, as the first step we need to identify the key stakeholders who will be using the recommendation system. These could be customers, service providers, and administrators of the "Rental Work Management System". Then we need to analyze the data related to user behavior and preferences to identify patterns and trends in how users search for service providers. This data could include search history, location data, service provider ratings, and reviews. Define use cases that outline how users will interact with the recommendation system. Use cases can help identify the key features and functionalities that the system should have, such as location-based search, personalized recommendations, and real-time updates. Then we can create prototypes of the recommendation system and conduct testing with users to gather feedback and identify areas for improvement. By following these steps, we can gather requirements for the client-side Figure 1. Overall process. recommendation component and ensure that the system provides relevant and personalized recommendations to users. Not only that,
Figure 1. Overall process.
Service provider recommendation
After that, we'll go overuse scenarios that show how service providers will interact with the recommendation system. The system's key features and functionalities will be determined using these use cases as a guide. These features might include real-time updates on job availability, personalized recommendations based on their talents and preferences, and location-based job searches. Once the use cases are defined, we will proceed to create prototypes of the recommendation system. These prototypes will be designed specifically for service providers, allowing them to navigate the system, view job recommendations, and interact with the available features. We will conduct thorough testing sessions with service providers to gather their feedback and identify areas for improvement. Their input will be invaluable in refining the recommendation system and ensuring that it meets their needs and expectations. By following this iterative process, we will be able to gather the requirements for the service provider recommendation component. Our goal is todevelop a system that provides relevant and personalized job recommendations to service providers, ultimately enhancing their job search experience and increasing their chances of securing suitable rental jobs (Figure 3).
Figure 2. Collect data on service providers to suggest popular jobs.
After that, we'll go overuse scenarios that show how service providers will interact with the recommendation system. The system's key features and functionalities will be determined using these use cases as a guide. These features might include real-time updates on job availability, personalized recommendations based on their talents and preferences, and location-based job searches. Contains cleaning, formatting, and putting it in the correct structure for analysis. This step ensures that the data is consistent and ready for further processing. Relevant features will be selected for analysis based on their ability to predict service providers' rental preferences. Key features may include the service provider's location, the types of services they specialize in, and their rental history. Additional features, such as weather data or real time service availability, may also be extracted from the collected data to enhance the recommendation accuracy. ï?· The performance of the recommendation system will be evaluated using metrics such as accuracy. By comparing the recommended rental jobs with service providers' actual preferences and selections, we can assess the effectiveness of the system and make necessary improvements. ï?· The service provider recommendation system will be deployed as a service provider-side component within the Rental Work Management System. It will be designed to be user-friendly, scalable, and secure, providing service providers with a seamless and efficient experience in accessing and interacting with the recommendations (Figure 2). Once the use cases are defined, we will proceed to create prototypes of the recommendation system. These prototypes will be designed specifically for service providers, allowing them to navigate the system, view job recommendations, and interact with the available features. We will conduct thorough testing sessions with service providers to gather their feedback and identify areas for improvement. Their input will be invaluable in refining the recommendation system and ensuring that it meets their needs and expectations. By following this iterative process, we will be able to gather the requirements for the service provider recommendation component. Our goal is to Figure 3. Identification of system architecture. The diagram illustrates the overall system for recommending activities based on service provider-side recommendations. Initially, service providers can log in to our system using their email and password. Upon logging in, they will be directed to the service provider UI page. The system will then suggest the most popular rental jobs in service provider can register their skills and service details. Service providers have the option to search for rental jobs. Once a service provider logs in and provides their location details and service type, the recommendation component analyzes their behavior and preferences to recommend relevant rental jobs. Service providers can select these jobs based on their interest and availability. Also, when a service provider creates a user profile, the system recommends to the service provider the areas with the highest demand for those rental jobs. (The most popular areas for those rental jobs). That way the service providers can know the areas where their work is being used the most. In addition, the system allows service providers to advertise their services and offers, and based on their job category. Those ads are automatically generated by the system. Service providers can manage their profiles and view booking details, enabling them to stay organized and up to date with their scheduled jobs. Overall, the system provides a user-friendly interface for service providers, offering personalized recommendations, job search capabilities, advertising options, profile management, and booking details to enhance their experience and streamline their rental work management process. develop a system that provides relevant and personalized job recommendations to service providers, ultimately enhancing their job search experience and increasing their chances of securing suitable rental jobs (Figure 3).
Figure 3. Identification of system architecture.
The diagram illustrates the overall system for recommending activities based on service provider-side recommendations. Initially, service providers can log in to our system using their email and password. Upon logging in, they will be directed to the service provider UI page. The system will then suggest the most popular rental jobs in service provider can register their skills and service details.
Service providers have the option to search for rental jobs. Once a service provider logs in and provides their location details and service type, the recommendation component analyzes their behavior and preferences to recommend relevant rental jobs. Service providers can select these jobs based on their interest and availability. Also, when a service provider creates a user profile, the system recommends to the service provider the areas with the highest demand for those rental jobs. (The most popular areas for those rental jobs). That way the service providers can know the areas where their work is being used the most.
In addition, the system allows service providers to advertise their services and offers, and based on their job category. Those ads are automatically generated by the system. Service providers can manage their profiles and view booking details, enabling them to stay organized and up to date with their scheduled jobs. Overall, the system provides a user-friendly interface for service providers, offering personalized recommendations, job search capabilities, advertising options, profile management, and booking details to enhance their experience and streamline their rental work management process.
Chatbot (Raza platform)
In this section, we describe the approach we used to develop and evaluate our component, including the technologies, algorithms, and evaluation metrics.
Recommendation for activities based on weather conditions
The methodology begins with the collection and cleaning of a dataset that contains weather conditions and corresponding activity data. The dataset is carefully curated, ensuring its representativeness and reliability. Cleaning procedures are applied to remove any duplicate entries, handle missing values, and address outliers or inconsistencies. This ensures the dataset is of high quality and ready for analysis.
Next, three machine learning algorithms, namely K Nearest Neighbors (KNN), decision trees, and logistic regression, are selected for initial modeling. These algorithms are chosen based on their suitability for classification tasks and their relevance to weather-related predictions. Each algorithm has unique characteristics and capabilities, providing a comprehensive evaluation framework (Figure 4).
Figure 4. recommendation process.
The first step of data pre-processing is to collect the raw data, the dataset used for this analysis was acquired from an online data repository company called Kaggle can be found. The data was for the all-around the Sri Lanka weather data and job conditions for the period 1st January 2020 to 1st January 2023. The file format was a file (CSV) which contains 54788 rows and 7 columns, the columns which were identified as
Next, three machine learning algorithms, namely K Nearest Neighbors (KNN), Decision Trees, and Logistic Regression, are selected for initial modeling. These algorithms are chosen based on their suitability for classification tasks and their relevance to weather-related predictions. Each algorithm has unique characteristics and capabilities, providing a comprehensive evaluation framework.
The selected models are trained and evaluated using a split of the dataset into training and testing sets. The training data is used to train each model, while the testing data is employed to evaluate their performance.
Appropriate evaluation metrics, such as accuracy, precision, recall, and F1-score, are used to assess the models' accuracy and effectiveness in predicting activities based on weather conditions. This step allows for a comparison of the models' performance, helping identify the most accurate algorithm among the three.
Once the most accurate model is identified, it undergoes refinement to improve its performance. This refinement involves adjusting the model's hyper parameters, such as the number of neighbors in KNN, the maximum depth of decision trees, or the regularization strength in logistic regression. Techniques like cross-validation or grid search may be employed to identify the optimal combination of hyper parameters. The refinement process aims to enhance the model's accuracy, generalization capabilities, and overall predictive power.
This study has culminated in the development of a comprehensive "Rental Work Management System" comprising four core components: customer recommendation, service provider recommendation, chatbot integration, and weather-based activity recommendations. These components collectively aim to enhance the efficiency and user experience within the system.
The customer recommendation component was meticulously designed to identify key stakeholders and analyze user behavior and preferences. Through an in depth analysis of search history, location data, ratings, and reviews, this component generated personalized service provider suggestions. Real-time feedback loops were thoughtfully integrated to ensure that recommendations remained relevant and tailored to individual users. Consequently, the result is a secure, user-friendly client side recommendation system that optimizes user engagement and satisfaction.
Similarly, the service provider recommendation component identified key stakeholders and employed data analysis to elevate service providers' job search experiences. This involved comprehending search patterns, utilizing location data, and gaining insights into rental preferences. The outcome was a secure and user friendly service provider-side recommendation system. Service providers can now efficiently manage their profiles, access job recommendations, and receive automatic job suggestions, significantly enhancing their overall work management.
In the realm of accessibility, the chatbot integration emerged as a pivotal feature, providing crucial assistance and information to visually impaired users. A systematic development approach ensured user needs were met, and the chatbot seamlessly integrated into the rental management system, contributing to a more inclusive and user-friendly interface.
Lastly, the weather-based activity recommendation component harnessed the power of machine learning algorithms to predict activities based on weather conditions. A series of algorithms were tested, including the decision tree classifier algorithm with an impressive accuracy of 99.96%, the K-nearest neighbor’s classifier algorithm at 98.97%, and the Logistic Regression algorithm achieving 76.79% accuracy.
This feature, by analyzing weather data alongside user preferences, promotes safety and satisfaction by offering suitable activity recommendations. In conclusion, the "Rental Work Management System" introduced through this research stands as a testament to the convergence of innovation and practicality. Its components collectively offer a comprehensive solution to the intricacies of rental labor management. Recommendations for stakeholders and users ensure that the system's implementation is not just successful but continues to evolve in response to changing needs.
The discussion section delves into the multifaceted aspects and implications of the "Rental Work Management System" and its individual components. Firstly, the effectiveness of the recommendation systems is a focal point. Both the customer and service provider recommendation components have proven to significantly enhance the user experience within the system. By analyzing user behavior and preferences, these systems deliver tailored suggestions, thereby streamlining the process of connecting customers with suitable service providers and vice versa. Challenges encountered during their implementation, such as data analysis complexities, were overcome to ensure optimal performance.
The impact of the chatbot integration on usability, particularly for visually impaired users, is a noteworthy aspect. This feature has been instrumental in providing essential information and assistance within the system, increasing accessibility and inclusivity. User feedback played a crucial role in shaping the chatbot's development, resulting in a more user-friendly interface. The practicality of weather-based activity recommendations is another dimension of discussion. Integrating machine learning algorithms with weather data and user preferences has the potential to greatly enhance user safety and satisfaction. This feature aligns well with the system's goal of addressing real-world challenges in rental labor management.
Scalability and security considerations are vital in any system discussion. Ensuring that the "Rental Work Management System" can handle a growing user base and protect sensitive user data is paramount for its long-term success.
Future research directions and enhancements are proposed to further optimize the system's functionality and usability, encompassing advanced machine learning techniques, expanded chatbot capabilities, and additional factors in activity recommendations. The discussion thus underlines the dynamic and evolving nature of the system, poised to meet the demands of an ever-changing environment.
In conclusion, this research has unveiled a comprehensive "Rental Work Management System" that addresses contemporary challenges in labor management with practicality and innovation. This system's unique blend of components, including customer and service provider recommendation features, a user-friendly chatbot integration, and weather-based activity recommendations, provides a holistic solution to streamline rental labor processes and improve user experiences.
The benefits of this research go beyond just the creation of the system. It places a strong emphasis on streamlining complicated procedures, giving service providers more control, and being flexible enough to accommodate changing social demands. By offering tailored recommendations, the system ensures that users can make informed decisions efficiently, leading to increased productivity and satisfaction. Recommendations provided for stakeholders, policymakers, and users are essential for the successful implementation and continuous improvement of the system. Stakeholders can leverage the system to optimize their rental labor management processes, while policymakers should consider regulatory aspects, particularly concerning data privacy and security. User training programs and resources are recommended to maximize the benefits of the system, and ongoing collaboration between stakeholders, researchers, and developers is encouraged to ensure its continued success.
As the world evolves, so too will the "Rental Work Management System." Future research directions suggest further refinement and enhancement, including the incorporation of advanced machine learning techniques, expansion of the chatbot's capabilities, and the consideration of additional factors in activity recommendations. The system's adaptability and commitment to user-centric solutions ensure its relevance and effectiveness in an ever-changing landscape.