Understanding User Experiences in Home Health Software: An Online Reviews Analytics

Mahmud Alrahhal, Ferhat Bozkurt

Abstract


The growing population has increased the demand for people to benefit from home health care (HHC) agencies, therefore, raising the need to use home health software (HHS) products to improve the quality of service and management of HHC companies. HHS products play a key role in managing and scheduling appointments, which leads to saving time and improving the productivity of HHC companies. Analyzing online reviews of HHS products helps the decision-makers in improving their products which leads to more satisfied customers (i.e. HHC agencies) and increases the quality of the HHS applications. In this study, a total of 5338 online reviews were gathered from the SoftwareAdvise website using a customized crawler. After data preprocessing and imputation, machine learning (ML) techniques including the Latent Dirichlet Allocation (LDA) topic model were used to reveal the major topics that influenced the customers’ perception. Self-organized maps (SOM) were utilized to segment the customers according to their ratings behavior which eventually led to six groups. Finally, classification and regression trees (CART) were used to predict customer outcomes based on criteria ratings. The results of this study revealed that customers have similar rating behaviors in terms of ease of use and functionality criteria in HHS. In addition, users of HHS were interested mostly in ten features including scheduling, management, support, privacy, integration, ease of use, accessibility, controllability, flexibility, and efficiency. These outcomes help researchers define their features while delivering surveys for evaluating customers’ satisfaction with HHS products.


Keywords


Home health, online reviews, machine learning, customer satisfaction

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References


Afrizal, A.D., Rakhmawati, N.A., Tjahyanto, A., 2019. New filtering scheme based on term weighting to improve object based opinion mining on tourism product reviews. Procedia Computer Science 161, 805-812.

Ahani, A., Nilashi, M., Ibrahim, O., Sanzogni, L., Weaven, S., 2019a. Market segmentation and travel choice prediction in Spa hotels through TripAdvisor’s online reviews. International Journal of Hospitality Management 80, 52-77.

Ahani, A., Nilashi, M., Yadegaridehkordi, E., Sanzogni, L., Tarik, A.R., Knox, K., Samad, S., Ibrahim, O., 2019b. Revealing customers’ satisfaction and preferences through online review analysis: The case of Canary Islands hotels. Journal of Retailing and Consumer Services 51, 331-343.

Ahani, A., Nilashi, M., Zogaan, W.A., Samad, S., Aljehane, N.O., Alhargan, A., Mohd, S., Ahmadi, H., Sanzogni, L., 2021. Evaluating medical travelers’ satisfaction through online review analysis. Journal of Hospitality and Tourism Management 48, 519-537.

Alrahhal, M., Bozkurt, F., Analyzing Big Social Data for Evaluating Environment-Friendly Tourism in Turkey. Journal of Intelligent Systems: Theory and Applications 6(2), 130-142.

Amatulli, C., De Angelis, M., Stoppani, A., 2019. Analyzing online reviews in hospitality: Data-driven opportunities for predicting the sharing of negative emotional content. Current Issues in Tourism 22(15), 1904-1917.

Attik, M., Bougrain, L., Alexandre, F., 2005. Self-organizing map initialization, Artificial Neural Networks: Biological Inspirations–ICANN 2005: 15th International Conference, Warsaw, Poland, September 11-15, 2005. Proceedings, Part I 15. Springer, pp. 357-362.

Bankole, A.O., Girdwood, T., Leeman, J., Womack, J., Toles, M., 2023. Identifying unmet needs of older adults transitioning from home health care to independence at home: A qualitative study. Geriatric Nursing 51, 293-302.

Bi, J.-W., Liu, Y., Fan, Z.-P., Zhang, J., 2020. Exploring asymmetric effects of attribute performance on customer satisfaction in the hotel industry. Tourism Management 77, 104006.

Bian, Y., Ye, R., Zhang, J., Yan, X., 2022. Customer preference identification from hotel online reviews: A neural network based fine-grained sentiment analysis. Computers & Industrial Engineering 172, 108648.

Bilgihan, A., Seo, S., Choi, J., 2018. Identifying restaurant satisfiers and dissatisfiers: Suggestions from online reviews. Journal of Hospitality Marketing & Management 27(5), 601-625.

Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J, 1984. Classification And Regression Trees. CRC Press, Boca Raton, FL, USA.

Cappanera, P., Scutellà, M.G., 2015. Joint assignment, scheduling, and routing models to home care optimization: A pattern-based approach. Transportation Science 49(4), 830-852.

Cascio, C.N., O'Donnell, M.B., Bayer, J., Tinney Jr, F.J., Falk, E.B., 2015. Neural correlates of susceptibility to group opinions in online word-of-mouth recommendations. Journal of Marketing Research 52(4), 559-575.

Chatterjee, S., Goyal, D., Prakash, A., Sharma, J., 2021. Exploring healthcare/health-product ecommerce satisfaction: A text mining and machine learning application. Journal of Business Research 131, 815-825.

Chen, H., Wang, P., Ren, T., Pan, Z., Liu, J., Ma, Y., 2023. NT-Com: A combined machine learning model for picking up first arrival. Computers & Geosciences 173, 105321.

Ding, K., Choo, W.C., Ng, K.Y., Ng, S.I., 2020. Employing structural topic modelling to explore perceived service quality attributes in Airbnb accommodation. International Journal of Hospitality Management 91, 102676.

Du, G., Tian, Y., Ouyang, X., 2022. Multi-resources co-scheduling optimization for home healthcare services under the constraints of service time windows and green transportation. Applied Soft Computing 131, 109746.

Du, Y., Liu, D., Duan, H., 2022. A textual data-driven method to identify and prioritise user preferences based on regret/rejoicing perception for smart and connected products. International Journal of Production Research 60(13), 4176-4196.

Eslami, S.P., Ghasemaghaei, M., Hassanein, K., 2018. Which online reviews do consumers find most helpful? A multi-method investigation. Decision Support Systems 113, 32-42.

Forgas, S., Moliner, M.A., Sánchez, J., Palau, R., 2010. Antecedents of airline passenger loyalty: Low-cost versus traditional airlines. Journal of Air Transport Management 16(4), 229-233.

Galicia, A., Talavera-Llames, R., Troncoso, A., Koprinska, I., Martínez-Álvarez, F., 2019. Multi-step forecasting for big data time series based on ensemble learning. Knowledge-Based Systems 163, 830-841.

Gavilan, D., Avello, M., Martinez-Navarro, G., 2018. The influence of online ratings and reviews on hotel booking consideration. Tourism Management 66, 53-61.

Haggag, O., Grundy, J., Abdelrazek, M., Haggag, S., 2022. A large scale analysis of mHealth app user reviews. Empirical Software Engineering 27(7), 196.

Harrison, K.L., Leff, B., Altan, A., Dunning, S., Patterson, C., Ritchie, C.S., 2020. What’s happening at home: a claims-based approach to better understand home clinical care received by older adults. Medical care 58(4), 360.

Heng, Y., Gao, Z., Jiang, Y., Chen, X., 2018. Exploring hidden factors behind online food shopping from Amazon reviews: A topic mining approach. Journal of Retailing and Consumer Services 42, 161-168.

Herrero, Á., San Martín, H., Hernández, J.M., 2015. How online search behavior is influenced by user-generated content on review websites and hotel interactive websites. International Journal of Contemporary Hospitality Management.

Hu, F., Trivedi, R.H., 2020. Mapping hotel brand positioning and competitive landscapes by text-mining user-generated content. International Journal of Hospitality Management 84, 102317.

Huang, J., 2017. The dining experience of Beijing Roast Duck: A comparative study of the Chinese and English online consumer reviews. International Journal of Hospitality Management 66, 117-129.

Huang, S., Zhang, J., Yang, C., Gu, Q., Li, M., Wang, W., 2022. The interval grey QFD method for new product development: Integrate with LDA topic model to analyze online reviews. Engineering Applications of Artificial Intelligence 114, 105213.

Jia, S.S., 2020. Motivation and satisfaction of Chinese and US tourists in restaurants: A cross-cultural text mining of online reviews. Tourism Management 78, 104071.

Jiménez, F.R., Mendoza, N.A., 2013. Too popular to ignore: The influence of online reviews on purchase intentions of search and experience products. Journal of Interactive Marketing 27(3), 226-235.

Jones, A., Costa, A.P., Pesevski, A., McNicholas, P.D., 2018. Predicting hospital and emergency department utilization among community-dwelling older adults: Statistical and machine learning approaches. PLoS One 13(11), e0206662.

Jung, Y., Suh, Y., 2019. Mining the voice of employees: A text mining approach to identifying and analyzing job satisfaction factors from online employee reviews. Decision Support Systems 123, 113074.

Koeleman, P.M., Bhulai, S., van Meersbergen, M., 2012. Optimal patient and personnel scheduling policies for care-at-home service facilities. European Journal of Operational Research 219(3), 557-563.

Kohonen, T., 1990. The self-organizing map. Proceedings of the IEEE 78(9), 1464-1480.

Kuspinar, A., Hirdes, J.P., Berg, K., McArthur, C., Morris, J.N., 2019. Development and validation of an algorithm to assess risk of first-time falling among home care clients. BMC geriatrics 19(1), 1-8.

Li, H., Liu, Y., Tan, C.-W., Hu, F., 2020. Comprehending customer satisfaction with hotels: Data analysis of consumer-generated reviews. International Journal of Contemporary Hospitality Management 32(5), 1713-1735.

Li, Q., Yang, Y., Li, C., Zhao, G., 2023. Energy vehicle user demand mining method based on fusion of online reviews and complaint information. Energy Reports 9, 3120-3130.

Liang, D., Dai, Z., Wang, M., 2021. Assessing customer satisfaction of O2O takeaway based on online reviews by integrating fuzzy comprehensive evaluation with AHP and probabilistic linguistic term sets. Applied Soft Computing 98, 106847.

Liu, J., Zhou, Y., Jiang, X., Zhang, W., 2020. Consumers’ satisfaction factors mining and sentiment analysis of B2C online pharmacy reviews. BMC Medical Informatics and Decision Making 20(1), 1-13.

Lucini, F.R., Tonetto, L.M., Fogliatto, F.S., Anzanello, M.J., 2020. Text mining approach to explore dimensions of airline customer satisfaction using online customer reviews. Journal of Air Transport Management 83, 101760.

Luo, J.M., Vu, H.Q., Li, G., Law, R., 2020. Topic modelling for theme park online reviews: Analysis of Disneyland. Journal of Travel & Tourism Marketing 37(2), 272-285.

Ma, B., Zhang, D., Yan, Z., Kim, T., 2013. An LDA and synonym lexicon based approach to product feature extraction from online consumer product reviews. Journal of Electronic Commerce Research 14(4), 304.

Marine-Roig, E., 2019. Destination image analytics through traveller-generated content. Sustainability 11(12), 3392.

Mathayomchan, B., Taecharungroj, V., 2020. “How was your meal?” Examining customer experience using Google maps reviews. International Journal of Hospitality Management 90, 102641.

McArthur, C., Ioannidis, G., Jantzi, M., Adachi, J.D., Giangregorio, L., Hirdes, J., Papaioannou, A., 2020. Development and validation of the fracture risk scale home care (FRS-HC) that predicts one-year incident fracture: an electronic record-linked longitudinal cohort study. BMC Musculoskeletal Disorders 21, 1-9.

Moro, S., Lopes, R.J., Esmerado, J., Botelho, M., 2020. Service quality in airport hotel chains through the lens of online reviewers. Journal of Retailing and Consumer Services 56, 102193.

Nabal, M., Bescos, M., Barcons, M., Torrubia, P., Trujillano, J., Requena, A., 2014. New symptom-based predictive tool for survival at seven and thirty days developed by palliative home care teams. Journal of Palliative Medicine 17(10), 1158-1163.

Namukasa, J., 2013. The influence of airline service quality on passenger satisfaction and loyalty: The case of Uganda airline industry. The TQM Journal 25(5), 520-532.

Nasiri, M.S., Shokouhyar, S., 2021. Actual consumers' response to purchase refurbished smartphones: Exploring perceived value from product reviews in online retailing. Journal of Retailing and Consumer Services 62, 102652.

Nicolas, C., Kim, J., Chi, S., 2021. Natural language processing-based characterization of top-down communication in smart cities for enhancing citizen alignment. Sustainable Cities and Society 66, 102674.

Nilashi, M., Abumalloh, R.A., Alghamdi, A., Minaei-Bidgoli, B., Alsulami, A.A., Thanoon, M., Asadi, S., Samad, S., 2021a. What is the impact of service quality on customers’ satisfaction during COVID-19 outbreak? New findings from online reviews analysis. Telematics and Informatics 64, 101693.

Nilashi, M., Abumalloh, R.A., Minaei-Bidgoli, B., Zogaan, W.A., Alhargan, A., Mohd, S., Azhar, S.N.F.S., Asadi, S., Samad, S., 2022a. Revealing travellers’ satisfaction during COVID-19 outbreak: moderating role of service quality. Journal of Retailing and Consumer Services 64, 102783.

Nilashi, M., Ahani, A., Esfahani, M.D., Yadegaridehkordi, E., Samad, S., Ibrahim, O., Sharef, N.M., Akbari, E., 2019a. Preference learning for eco-friendly hotels recommendation: A multi-criteria collaborative filtering approach. Journal of Cleaner Production 215, 767-783.

Nilashi, M., bin Ibrahim, O., Ithnin, N., 2014. Multi-criteria collaborative filtering with high accuracy using higher order singular value decomposition and Neuro-Fuzzy system. Knowledge-Based Systems 60, 82-101.

Nilashi, M., Ibrahim, O., Yadegaridehkordi, E., Samad, S., Akbari, E., Alizadeh, A., 2018. Travelers decision making using online review in social network sites: A case on TripAdvisor. Journal of computational science 28, 168-179.

Nilashi, M., Samad, S., Alghamdi, A., Ismail, M.Y., Alghamdi, O., Mehmood, S.S., Mohd, S., Zogaan, W.A., Alhargan, A., 2022b. A new method for analysis of customers’ online review in medical tourism using fuzzy logic and text mining approaches. International Journal of Information Technology & Decision Making 21(06), 1797-1820.

Nilashi, M., Samad, S., Minaei-Bidgoli, B., Ghabban, F., Supriyanto?, E., 2021b. Online reviews analysis for customer segmentation through dimensionality reduction and deep learning techniques. Arabian Journal for Science and Engineering 46(9), 8697-8709.

Nilashi, M., Yadegaridehkordi, E., Ibrahim, O., Samad, S., Ahani, A., Sanzogni, L., 2019b. Analysis of travellers’ online reviews in social networking sites using fuzzy logic approach. International Journal of Fuzzy Systems 21, 1367-1378.

Nilsson, L., Lindblad, M., Johansson, N., Säfström, L., Schildmeijer, K., Ekstedt, M., Unbeck, M., 2023. Exploring nursing-sensitive events in home healthcare: A national multicenter cohort study using a trigger tool. International Journal of Nursing Studies 138, 104434.

Pahlevani, D., Abbasi, B., Hearne, J.W., Eberhard, A., 2022. A cluster-based algorithm for home health care planning: A case study in Australia. Transportation Research Part E: Logistics and Transportation Review 166, 102878.

Qiang, Z., Hai-Chao, C., Zu-Yuan, L., Bai-Wei, F., Cheng-Sheng, Z., Xide, C., Xiao, W., 2023. Multi-stage design space reduction technology based on SOM and rough sets, and its application to hull form optimization. Expert Systems with Applications 213, 119229.

Qiao, Z., Wang, G.A., Zhou, M., Fan, W., 2018. The impact of customer reviews on product innovation: Empirical evidence in mobile apps. Analytics and Data Science: Advances in Research and Pedagogy, 95-110.

Ray, A., Bala, P.K., Rana, N.P., 2021. Exploring the drivers of customers’ brand attitudes of online travel agency services: A text-mining based approach. Journal of Business Research 128, 391-404.

Rita, P., Moro, S., Cavalcanti, G., 2022. The impact of COVID-19 on tourism: Analysis of online reviews in the airlines sector. Journal of Air Transport Management 104, 102277.

Russell, D., Burgdorf, J.G., Washington, K.T., Schmitz, J., Bowles, K.H., 2023. “Second set of eyes:” Family caregivers and post-acute home health care during the COVID-19 pandemic. Patient Education and Counseling 109, 107627.

Sánchez-Franco, M.J., Arenas-Márquez, F.J., Alonso-Dos-Santos, M., 2021. Using structural topic modelling to predict users’ sentiment towards intelligent personal agents. An application for Amazon’s echo and Google Home. Journal of Retailing and Consumer Services 63, 102658.

Sezgen, E., Mason, K.J., Mayer, R., 2019. Voice of airline passenger: A text mining approach to understand customer satisfaction. Journal of Air Transport Management 77, 65-74.

Sievert, C., Shirley, K., 2014. LDAvis: A method for visualizing and interpreting topics, Proceedings of the workshop on interactive language learning, visualization, and interfaces. pp. 63-70.

Sim, Y., Lee, S.K., Sutherland, I., 2021. The impact of latent topic valence of online reviews on purchase intention for the accommodation industry. Tourism Management Perspectives 40, 100903.

softwareadvise, https://www.softwareadvice.com/. (Accessed 26.04.2023.

Song, J., Hobensack, M., Bowles, K.H., McDonald, M.V., Cato, K., Rossetti, S.C., Chae, S., Kennedy, E., Barrón, Y., Sridharan, S., 2022. Clinical notes: An untapped opportunity for improving risk prediction for hospitalization and emergency department visit during home health care. Journal of biomedical informatics 128, 104039.

Song, J., Woo, K., Shang, J., Ojo, M., Topaz, M., 2021. Predictive risk models for wound infection-related hospitalization or ED visits in home health care using machine-learning algorithms. Advances in Skin & Wound Care 34(8), 1-12.

Song, Y., Liu, K., Guo, L., Yang, Z., Jin, M., 2022. Does hotel customer satisfaction change during the COVID-19? A perspective from online reviews. Journal of Hospitality and Tourism Management 51, 132-138.

Steinberg, D., Colla, P, 1997. CART: Classification and Regression Trees. Salford Systems; CRC Press, San Diego, CA, USA.

Szymanski, D.M., Hise, R.T., 2000. E-satisfaction: an initial examination. Journal of retailing 76(3), 309-322.

Tiryaki, V.M., 2023. Mass segmentation and classification from film mammograms using cascaded deep transfer learning. Biomedical Signal Processing and Control 84, 104819.

Topaz, M., Woo, K., Ryvicker, M., Zolnoori, M., Cato, K., 2020. Home Health Care Clinical Notes Predict Patient Hospitalization and Emergency Department Visits. Nursing research 69(6), 448.

Van Eenoo, L., van der Roest, H., Onder, G., Finne-Soveri, H., Garms-Homolova, V., Jonsson, P.V., Draisma, S., van Hout, H., Declercq, A., 2018. Organizational home care models across Europe: A cross sectional study. International journal of nursing studies 77, 39-45.

Vesanto, J., Alhoniemi, E., 2000. Clustering of the self-organizing map. IEEE Transactions on neural networks 11(3), 586-600.

Wan, K.X., Vidavsky, I., Gross, M.L., 2002. Comparing similar spectra: from similarity index to spectral contrast angle. Journal of the American Society for Mass Spectrometry 13(1), 85-88.

Wang, X., Tang, L.R., Kim, E., 2019. More than words: Do emotional content and linguistic style matching matter on restaurant review helpfulness? International Journal of Hospitality Management 77, 438-447.

Wang, Y., Lu, X., Tan, Y., 2018. Impact of product attributes on customer satisfaction: An analysis of online reviews for washing machines. Electronic Commerce Research and Applications 29, 1-11.

Wei, X., Taecharungroj, V., 2022. How to improve learning experience in MOOCs an analysis of online reviews of business courses on Coursera. The International Journal of Management Education 20(3), 100675.

Williams, T., Betak, J., 2018. A Comparison of LSA and LDA for the Analysis of Railroad Accident Text. Procedia computer science 130, 98-102.

Wu, J.-J., Chang, S.-T., 2020. Exploring customer sentiment regarding online retail services: a topic-based approach. Journal of Retailing and Consumer Services 55, 102145.

Xiang, Z., Schwartz, Z., Gerdes Jr, J.H., Uysal, M., 2015. What can big data and text analytics tell us about hotel guest experience and satisfaction? International journal of hospitality management 44, 120-130.

Xu, X., 2021. What are customers commenting on, and how is their satisfaction affected? Examining online reviews in the on-demand food service context. Decision Support Systems 142, 113467.

Yan, X., Wang, J., Chau, M., 2015. Customer revisit intention to restaurants: Evidence from online reviews. Information Systems Frontiers 17, 645-657.

Yang, C., Yu, R., Ji, H., Jiang, H., Yang, W., Jiang, F., 2021. Application of data mining in the provision of in-home medical care for patients with advanced cancer. Translational Cancer Research 10(6), 3013.

Zablocki, A., Schlegelmilch, B., Houston, M.J., 2019. How valence, volume and variance of online reviews influence brand attitudes. Ams Review 9, 61-77.

Zhang, J., Zhang, A., Liu, D., Bian, Y., 2021. Customer preferences extraction for air purifiers based on fine-grained sentiment analysis of online reviews. Knowledge-Based Systems 228, 107259.

Zhang, L., Hanks, L., 2018. Online reviews: The effect of cosmopolitanism, incidental similarity, and dispersion on consumer attitudes toward ethnic restaurants. International Journal of Hospitality Management 68, 115-123.

Zhang, N., Liu, R., Zhang, X.-Y., Pang, Z.-L., 2021. The impact of consumer perceived value on repeat purchase intention based on online reviews: by the method of text mining. Data Science and Management 3, 22-32.

Zhang, Y., Zhang, Q., Chen, W., Shi, W., Cui, Y., Chen, L., Shao, J., 2023. Hydrogeochemical analysis and groundwater pollution source identification based on self-organizing map at a contaminated site. Journal of Hydrology 616, 128839.

Zhao, K., Zhang, P., Lee, H.-M., 2022. Understanding the impacts of user-and marketer-generated content on free digital content consumption. Decision Support Systems 154, 113684.

Zhu, M., Chen, W., Hirdes, J.P., Stolee, P., 2007. The K-nearest neighbor algorithm predicted rehabilitation potential better than current Clinical Assessment Protocol. Journal of clinical epidemiology 60(10), 1015-1021.

Zibarzani, M., Abumalloh, R.A., Nilashi, M., Samad, S., Alghamdi, O., Nayer, F.K., Ismail, M.Y., Mohd, S., Akib, N.A.M., 2022. Customer satisfaction with Restaurants Service Quality during COVID-19 outbreak: A two-stage methodology. Technology in Society 70, 101977.


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