Service-related Information Research Articles

Performance of Machine Learning Models to Predict Customer Satisfaction Scores for Lao National Convention Center

This research paper investigates the effectiveness of various machine learning (ML) models in predicting customer satisfaction scores for the Lao National Convention Center (LNCC). By utilizing a dataset containing customer feedback, demographic data, facilities, and service-related information, the study aims to identify the best performing model for predicting customer satisfaction. In this paper, the performance of ML to predict customer satisfaction scores from the questionnaire survey dataset is evaluated. The customer satisfaction score is categorized into five classes: strongly agree, agree, neutral, disagree and strongly disagree. The analysis will compare the performance of different models, including linear regression (LR), k-nearest neighbor (KNN), support vector machines (SVM), decision trees (DT) and random forest (RF). The results show that SVM model achieved the highest accuracy rate of 95.91%, followed by KNN and LR with an accuracy rate of 95.55% and 95.49%, respectively. The findings of this study have important implications for the use of ML in improving the service-related information for LNCC and providing valuable insights for decision-makers and developers.

The faster or richer the response, the better performance? An empirical analysis of online healthcare platforms from a competitive perspective

The emergence of online healthcare platforms has changed the competitive environment among physicians. However, little is known about how physicians can improve their performance in this new environment. Platforms also face challenges in comprehending the competitive mechanisms among physicians, which might hinder them from formulating strategic managerial decisions that foster sustained growth. In this light, we extract medical service-related information from physicians' response behavioral data on a prominent healthcare platform, and empirically investigate the factors affecting physicians' online performance from a competitive perspective as well as the gender differences in these effects. The results indicate that physicians' responses significantly impact their online performance, revealing a competitive relationship between physicians and their colleagues in the same department. Specifically, a fast response time and informative responses are positively correlated with the focal physician's performance, whereas colleagues' informative responses negatively impact the focal physician's performance, and this relationship is mediated by the focal physician's response informativeness. Nevertheless, there is no significant correlation between colleagues' response time and the focal physician's performance. The results also unveil that gender moderates the effect of response informativeness on the focal physician's performance. Specifically, colleagues' response informativeness has a more significant impact on male physicians' performance than on female physicians' performance, suggesting a greater propensity for competition among male physicians. Our findings could offer decision support for enhancing physician performance and healthcare platform management.

Online Agriculture Management System

This paper aims to give a comprehensive overview of Online Agriculture Management System is the web application which helps farmers by providing various kinds Agri related information and Agri services in the website. This website helps farmers by providing them a large online market to sell their produce. labourers and they can be updated with the latest agricultural developments with articles and blogs module. Admin can post latest news and articles and he can sell agriculture machinery products in the website. Workers can upload their resume and they can view work schedules after the login. To provide technology and services to the farmers, seller and farm labourers thus, helping then to expand their business and provide them with a wider market. Hence, improve the present farming processes and to provide knowledge about recent agricultural issues. To provide a helping hand to the farmers and farm labourers in improving their lives through the medium of technology, thereby, improving the Agricultural sector in the Indian economy

Federated reinforcement learning based task offloading approach for MEC-assisted WBAN-enabled IoMT

The exponential proliferation of wearable medical apparatus and healthcare information within the framework of the Internet of Medical Things (IoMT) introduces supplementary complexities pertaining to the elevated Quality of Service (QoS) of intelligent healthcare in the forthcoming 6G era. Healthcare services and applications need ultra-reliable data transfer and processing with ultra-low latency and energy usage. Wireless Body Area Network (WBAN) and Mobile Edge Computing (MEC) technologies enabled IoMT to handle large amounts of data sensing, transmission, and processing while maintaining good QoS. Traditional frame aggregation (FA) systems in WBAN, on the other hand, create an excessive number of control frames during data transmission, resulting in significant latency and energy consumption, as well as a lack of flexibility. A Federated Reinforcement Learning (FRL) based TO Approach is recommended in this research. In the beginning, different types of service-related information were separated into queues with equal QoS needs. The duration of the FA was then automatically determined by the aggregation vertex based on energy consumption, latency, and throughput using FRL. Finally, based on the existing status, the amount of tasks offloaded was determined. The simulation results demonstrate that, as compared to the baseline schemes, the suggested FRLTO efficiently reduces energy consumption and latency while enhancing throughput and total WBAN utilization. Numerical results show that the proposed scheme improves the throughput by 37.06% and reduced the energy consumption by around 69.84% and time delay by about 6.23%, as compared to the state-of-the-art existing baseline schemes.

A Knowledge Graph Embedding Based Service Recommendation Method for Service-Based System Development

Web API is an efficient way for Service-based Software (SBS) development, and mashup is a key technology which merges several web services to deal with the increasing complexity of software requirements and expedite the service-based system development. The efficient service recommendation method is vital for the software development. However, the existing methods often suffer from data sparsity or cold start issues, which should lead to bad effects. Currently, this paper starts with SBS development, and proposes a service recommendation method based on knowledge graph embedding and collaborative filtering (CF) technology. In our model, we first construct a refined knowledge graph using SBS-service co-invocation record and SBS and service related information to mine the potential semantics relationship between SBS and service. Then, we learn the SBS and service entities in the knowledge graph. These heterogeneous entities (SBS and service, etc.) are embedded into the low-dimensional space through the representation learning algorithms of Word2vec and TransR, and the distances between SBS and service vectors are calculated. The input of recommendation model is SBS requirement (target SBS), the similarities functional SBS set is extracted from knowledge graph, which can relieve the cold start problem. Meanwhile, the recommendation model uses CF to recommend service to target SBS. Finally, this paper verifies the effectiveness of method on the real-word dataset. Compared with the several state-of-the-art methods, our method has the best service hit rate and ranking quality.

The conceptualization of smart tourism service platforms on tourist value co-creation behaviours: an integrative perspective of smart tourism services

The question remains how to integrate the core service proposals within a smart tourism platform setting for further facilitating tourist value co-creation behaviours in sustainable ways. This paper investigates the paradigm of smart tourism service (STS) platforms within the context of an ecosystem space. It conceptualizes concepts by laying a reasonable theoretical foundation (service-dominant logic) and proposing a scale for smart services. Applying sequential mixed methods to an exploratory research design, with seven interlocking stages and data from Fuzzy Delphi experts and tourist surveys in Taipei City, a smart city in Taiwan, this paper proposes a second-order scale with six dimensions, comprising smart services of attractions, transportation, accommodation, diet, purchase, and payment. The final 32-item STS scale is thoroughly developed and subsequently validated in different contexts (i.e. travellers in different phases of travel, pre-travel and during the trip, respectively). The scale significantly reveals the tourist-operated technologies for the provision of STS, determining the development of conceptual STS platforms in this paper. Next, the platforms disclose the locus between ICT functions, information-related services, tourist applications and behaviours, and sustainable value co-creation. The potential path of “STS → behaviour → sustainable value co-creation” explored herein is helpful for illustrating the conceptualization of STS platforms. Moreover, predictions from the platforms of tourists’ smart behaviours make it practically relevant in assessing demands about smart services for tourism. In the end, this paper describes the theoretical implications and managerial implications for tourism practitioners.

Information heterogeneity in queueing service systems: Differentiated pricing decision with word-of-mouth rating information on service value

<p style='text-indent:20px;'>In many service settings, customers are usually unaware of service-related information. Therefore, in such service systems, customers can only resort to online ratings posted on the review platforms to obtain the user-generated service information and make their joining/balking decisions depending on their service value assessment (consumption utility). Motivated by such phenomenon, we consider a single-server M/G/1 queue with two types of customers with heterogeneous access to service-related information, namely experienced and first-time customers, and price them differently. We first characterize the equilibrium behavior of experienced customers and investigate how the market size of first-time customers and the number of ratings collected by first-time customers affect the queueing behavior of experienced customers. We then construct the revenue function and obtain the differentiated pricing strategy of the service provider. Interestingly, through numerical examples, we show that when the service price for experienced customers is fixed, the impact of the number of ratings collected by first-time customers on optimal revenue and pricing is related to the variance of service value. When the service price for first-time customers is fixed, the effect of number of ratings collected by first-time customers on optimal revenue and pricing is related to the mean service value (real service value). In a word, compared to the full rationality setting, the bounded rationality setting of first-time customers who have imperfect knowledge of the real service value could bring different effects on the strategic behavior of customers and the pricing decision of the service provider.</p>

Service Discovery Method Based on Knowledge Graph and Word2vec

Mashup is a new type of application that integrates multiple Web APIs. For mashup application development, the quality of the selected APIs is particularly important. However, with the rapid development of Internet technology, the number of Web APIs is increasing rapidly. It is unrealistic for mashup developers to manually select appropriate APIs from a large number of services. For existing methods, there is a problem of data sparsity, because one mashup is related to a few APIs, and another problem of over-reliance on semantic information. To solve these problems in current service discovery approaches, we propose a service discovery approach based on a knowledge map (SDKG). We embed service-related information into the knowledge graph, alleviating the impact of data sparsity and mining deep relationships between services, which improves the accuracy of service discovery. Experimental results show that our approach has obvious advantages in accuracy compared with the existing mainstream service discovery approaches.

Secure biometric authentication with de-duplication on distributed cloud storage.

Cloud computing is one of the evolving fields of technology, which allows storage, access of data, programs, and their execution over the internet with offering a variety of information related services. With cloud information services, it is essential for information to be saved securely and to be distributed safely across numerous users. Cloud information storage has suffered from issues related to information integrity, data security, and information access by unauthenticated users. The distribution and storage of data among several users are highly scalable and cost-efficient but results in data redundancy and security issues. In this article, a biometric authentication scheme is proposed for the requested users to give access permission in a cloud-distributed environment and, at the same time, alleviate data redundancy. To achieve this, a cryptographic technique is used by service providers to generate the bio-key for authentication, which will be accessible only to authenticated users. A Gabor filter with distributed security and encryption using XOR operations is used to generate the proposed bio-key (biometric generated key) and avoid data deduplication in the cloud, ensuring avoidance of data redundancy and security. The proposed method is compared with existing algorithms, such as convergent encryption (CE), leakage resilient (LR), randomized convergent encryption (RCE), secure de-duplication scheme (SDS), to evaluate the de-duplication performance. Our comparative analysis shows that our proposed scheme results in smaller computation and communication costs than existing schemes.

VARTOTOJŲ ELGESIO INTERNETE ANALIZĖ

In most cases the internet has a positive effect on its users – it offers unrestrained communication, expression of opinions and sense of openness. On the other hand, excessive usage leads to internet addiction. The most important features of the internet include rapid spread of information and news, access to tremendous amount of information, personalization and anonymity. After the literature review of theoretical research of authors from Lithuania and abroad, and application of theoretical analytical, resumptive, analogical, deductive and comparative methods, the features of XXI century media literacy were identified. A questionnaire allowed to examine the internet’s specifics and nature of users’ media literacy online. Empirical research was conducted to analyse the internet users’ behavior based on age groups as well as to identify their level of literacy. A big part of today’s daily activity and free time is related to usage of the internet media. Many services take an online form. Therefore, the issue of Lithuanian users’ online behavior becomes a concurrent to the development of welfare society, and media literacy gets to be one of the necessities for future skills. Instead of searching for and passively using the given information, internet users start to make their own, evaluate it and spread further. The users are enabled to fashion their private social networks, they become responsible for organization of the whole personal communication infrastructure. This research points out the main reasons of Lithuanian internet users not using e-services: the lack of media literacy and insufficient spread of the service-related information. The biggest difference in literacy among age groups was noticed while evaluating personal predisposition to internet addiction. Levels of literacy based on age groups were significantly different in terms of ability to critically evaluate online information. KEY WORDS: internet, media, media literacy, internet user’s behavior, welfare society. DOI: http://dx.doi.org/10.15181/tbb.v71i2.1102

Causes, indicators and impacts of climate change: understanding the public discourse in Goat based agro-pastoral livelihood zone, Ethiopia

This study assessed the perceived causes, indicators and impacts of climate change by disaggregating farmers in to adaptor and non-adaptor groups in Goat based agro-pastoral livelihood zone of Ethiopia. The collected quantitative and qualitative data were analysed in descriptive statistics, linear regression, anomaly index, Likert rating scale and conceptual narrations. The findings demonstrated that an increasing temperature and a decreasing rainfall trends were perceived by farmers across the study decades. Higher deforestation rate, rash natural resource exploitation, poor soil and water management rehearses and alarming population growth in descending order were identified as climate change causes. Livestock and crop yield decline, livestock/human diseases epidemics and death, as well as recurrent conflicts due to grazing land were its associated impacts. The status and nature of climate change causes, indicators and impacts were however significantly diverse within similar awareness groups. To mitigate its adverse impacts, the farmers were thus applied livestock, crop and non-agriculture related adaptation strategies. Shortage of finance and eligible household labor combined with the absence of climate related information, training and extension services were hindered farmers to take any measure to the climate change. Therefore, to encourage the farmers’ responsiveness, the finding underlines the importance of supplying applicable as well as legitimate natural resource exploitation system, followed by access to climate related information, awareness rising trainings, credit and input delivery services at local and community level.

Psychological burden of the COVID-19 pandemic and its associated factors among frontline doctors of Bangladesh: a cross-sectional study.

Background: Frontline doctors are the most vulnerable and high-risk population to get the novel coronavirus disease 2019 (COVID-19) infection. Hence, we aimed to evaluate the anxiety, depression, sleep disturbance and fear of COVID-19 among frontline doctors of Bangladesh during the pandemic, and the associated factors for these psychological symptoms. Methods: In total, 370 frontline doctors who were involved in the treatment of suspected or confirmed COVID-19 patients during the pandemic took part in an online cross-sectional study. Recruitment was completed using convenience sampling and the data were collected after the start of community transmission of COVID-19 in the country. Anxiety and depression, sleep disturbance, and fear of COVID-19 were assessed by the Patient Health Questionnaire-4, two-item version of the Sleep Condition Indicator, and the Fear of Coronavirus-19 scale, respectively. Socio-demographic information, health service-related information, co-morbidity, and smoking history were collected for evaluating risk factors. The proportion of psychological symptoms were presented using descriptive statistics and the associated factors were identified using multinomial logistic regression analysis. Results: Of the doctors, 36.5% had anxiety, 38.4% had depression, 18.6% had insomnia, and 31.9% had fear of COVID-19. In multinomial logistic regression, inadequate resources in the workplace were found as the single most significant predictor for all psychological outcomes: anxiety and/or depression (severe, OR 3.0, p=0.01; moderate, OR 5.3, p=0.000; mild, OR 2.3, p=0.003), sleep disturbance (moderate, OR 1.9, p=0.02), and fear of COVID-19 (severe, OR 1.9, p=0.03; moderate, OR 1.8, p=0.03). Conclusions:The study demonstrated a high burden of psychological symptoms among frontline doctors of Bangladesh during the COVID-19 pandemic situation. Inadequate resources are contributing to the poor mental health of Bangladeshi doctors. The supply of sufficient resources in workplaces and mental health counseling may help to mitigate the burden of the psychological symptoms identifiedamong the respondents..

Psychological burden of the COVID-19 pandemic and its associated factors among frontline doctors of Bangladesh: a cross-sectional study

Background: Frontline doctors are the most vulnerable and high-risk population to get the novel coronavirus disease 2019 (COVID-19) infection. Hence, we aimed to evaluate the anxiety, depression, sleep disturbance and fear of COVID-19 among frontline doctors of Bangladesh during the pandemic, and the associated factors for these psychological symptoms. Methods: In total, 370 frontline doctors who were involved in the treatment of suspected or confirmed COVID-19 patients during the pandemic took part in an online cross-sectional study. Recruitment was completed using convenience sampling and the data were collected after the start of community transmission of COVID-19 in the country. Anxiety and depression, sleep disturbance, and fear of COVID-19 were assessed by the Patient Health Questionnaire-4, two-item version of the Sleep Condition Indicator, and the Fear of Coronavirus-19 scale, respectively. Socio-demographic information, health service-related information, co-morbidity, and smoking history were collected for evaluating risk factors. The proportion of psychological symptoms were presented using descriptive statistics and the associated factors were identified using multinomial logistic regression analysis. Results: Of the doctors, 36.5% had anxiety, 38.4% had depression, 18.6% had insomnia, and 31.9% had fear of COVID-19. In multinomial logistic regression, inadequate resources in the workplace were found as the single most significant predictor for all psychological outcomes: anxiety and/or depression (severe, OR 3.0, p=0.01; moderate, OR 5.3, p=0.000; mild, OR 2.3, p=0.003), sleep disturbance (moderate, OR 1.9, p=0.02), and fear of COVID-19 (severe, OR 1.9, p=0.03; moderate, OR 1.8, p=0.03). Conclusions: The study demonstrated a high burden of psychological symptoms among frontline doctors of Bangladesh during the COVID-19 pandemic situation. Inadequate resources are contributing to the poor mental health of Bangladeshi doctors. The supply of sufficient resources in workplaces and mental health counseling may help to mitigate the burden of the psychological symptoms identified among the respondents..

Determinants of consumers’ continuance intention to use social recommender systems: A self-regulation perspective

Social recommender systems have transformed how consumers seek product- or service-related information. Based on Bagozzi's self-regulation framework, this study examines the determinants of consumers' continuance intention to use social recommender systems. We found that appraisal factors (perceived information quality and perceived system quality) are strong predictors of emotional reaction (recommendation satisfaction and trust in recommendation). Particularly, perceived information quality has a significant effect on both recommendation satisfaction and trust in recommendation. However, the positive effect of perceived system quality is only on recommendation satisfaction. Both recommendation satisfaction and trust in recommendation positively affect consumers' continuance intention to use social recommender systems.

Psychological burden of the COVID-19 pandemic and its associated factors among frontline doctors of Bangladesh: a cross-sectional study

Background: Frontline doctors are the most vulnerable and high-risk population to get the novel coronavirus disease 2019 (COVID-19) infection. Hence, we aimed to evaluate the anxiety, depression, sleep disturbance and fear of COVID-19 among frontline doctors of Bangladesh during the pandemic, and the associated factors for these psychological symptoms. Methods: In total, 370 frontline doctors who were involved in the treatment of suspected or confirmed COVID-19 patients during the pandemic took part in an online cross-sectional study. Recruitment was completed using convenience sampling and the data were collected after the start of community transmission of COVID-19 in the country. Anxiety and depression, sleep disturbance, and fear of COVID-19 were assessed by the Patient Health Questionnaire-4, two-item version of the Sleep Condition Indicator, and the Fear of Coronavirus-19 scale, respectively. Socio-demographic information, health service-related information, co-morbidity, and smoking history were collected for evaluating risk factors. The proportion of psychological symptoms were presented using descriptive statistics and the associated factors were identified using multinomial logistic regression analysis. Results: Of the doctors, 36.5% had anxiety, 38.4% had depression, 18.6% had insomnia, and 31.9% had fear of COVID-19. In multinomial logistic regression, inadequate resources in the workplace were found as the single most significant predictor for all psychological outcomes: anxiety and/or depression (severe, OR 3.0, p=0.01; moderate, OR 5.3, p=0.000; mild, OR 2.3, p=0.003), sleep disturbance (moderate, OR 1.9, p=0.02), and fear of COVID-19 (severe, OR 1.9, p=0.03; moderate, OR 1.8, p=0.03). Conclusions: The study demonstrated a high burden of psychological symptoms among frontline doctors of Bangladesh during the COVID-19 pandemic situation. Inadequate resources are contributing to the poor mental health of Bangladeshi doctors. The supply of sufficient resources in workplaces and mental health counseling may help to mitigate the burden of the psychological symptoms identified among the respondents..

Psychological burden of the COVID-19 pandemic and its associated factors among frontline doctors of Bangladesh: a cross-sectional study

Background: Frontline doctors are the most vulnerable and high-risk population to get the novel coronavirus disease 2019 (COVID-19) infection. Hence, we aimed to evaluate the anxiety, depression, sleep disturbance and fear of COVID-19 among frontline doctors of Bangladesh during the pandemic, and the associated factors for these psychological symptoms. Methods: In total, 370 frontline doctors who were involved in the treatment of suspected or confirmed COVID-19 patients during the pandemic took part in an online cross-sectional study. Recruitment was completed using convenience sampling and the data were collected after the start of community transmission of COVID-19 in the country. Anxiety and depression, sleep disturbance, and fear of COVID-19 were assessed by the Patient Health Questionnaire-4, two-item version of the Sleep Condition Indicator, and the Fear of Coronavirus-19 scale, respectively. Socio-demographic information, health service-related information, co-morbidity, and smoking history were collected for evaluating risk factors. The proportion of psychological symptoms were presented using descriptive statistics and the associated factors were identified using multinomial logistic regression analysis. Results: Of the doctors, 36.5% had anxiety, 38.4% had depression, 18.6% had insomnia, and 31.9% had fear of COVID-19. In multinomial logistic regression, inadequate resources in the workplace were found as the single most significant predictor for all psychological outcomes: anxiety and/or depression (severe, OR 3.0, p=0.01; moderate, OR 5.3, p=0.000; mild, OR 2.3, p=0.003), sleep disturbance (moderate, OR 1.9, p=0.02), and fear of COVID-19 (severe, OR 1.9, p=0.03; moderate, OR 1.8, p=0.03). Conclusions: The study demonstrated a high burden of psychological symptoms among frontline doctors of Bangladesh during the COVID-19 pandemic situation. Inadequate resources are contributing to the poor mental health of Bangladeshi doctors. The supply of sufficient resources in workplaces and mental health counseling may help to mitigate the burden of the psychological symptoms identified among the respondents..

Empirical insights on technology use for navigating human services

A considerable portion of the US population still lacks access to technology, which causes challenges for marginalized communities to access information and services. Research on the digital divide exists in various contexts, but few have examined it in the context of human services. This study examines the impact of socioeconomic status on the methods of communication used when searching for service-related information. We analyzed both quantitative and qualitative data collected from 63 low-income and/or current human service users in Albany, New York. Education showed positive associations with smartphone ownership and personal computer use. Income was found only significant for tablet use. Non-whites were more likely to use mobile apps to web browsers compared to whites. Qualitative analysis revealed three key themes (i.e., availability, ease of use, and usefulness) as influencers of individual preference of methods. Our findings suggest that the digital divide is not merely about the income level but also educational background and culture. Human service professionals need to consider multiple channels to reach targeted populations for service delivery. Particularly, the collaboration between service providers and public libraries is worth examining to ensure the physical access and skills training for those who experience the digital divide at multiple levels.

Incidence of Pelvic Floor Disorders in US Army Female Soldiers

To determine the incidence of pelvic floor disorders (PFD) among active-duty US Army female soldiers. We studied 102,015 women for incident PFD using the Stanford Military Data Repository, which comprises medical, demographic and service-related information on all soldiers on active duty in the US Army during 2011-2014. Cox proportional hazards estimated adjusted associations with PFD diagnoses. In the adjusted models, military-specific characteristics and fitness were evaluated alongside known PFD predictors. Among 102,015 subjects at risk there was a cumulative incidence of 6.4% over a mean of 27 months (median 29, range 1-42). In adjusted models, obese soldiers were more likely to have a PFD compared to those of normal weight (hazard ratio [HR] 1.23, confidence interval [CI] 1.14-1.34, P <0.001) and those with recent weight gain were more likely to have a PFD compared to those without (HR 1.32, CI 1.24-1.40, P <0.05). Women with the lowest physical fitness scores were more likely to have a PFD (HR 1.14, CI 1.04-1.25) compared to those with the highest scores. Over a median follow-up time of 29 months, 1 in 15 women in this active-duty cohort was diagnosed with a PFD. Optimizing risk factors including body mass index and physical fitness may benefit the pelvic health of female soldiers, independent of age, children, and years of service.

Reaping the Benefits of Virality in Advertising - A Study of the Indian Scenario

With the rapid and unprecedented growth and advancement of digital technology, the present world is experiencing several changes and innovations in every field. In this world of change, innovations in marketing and advertising are definitely in the front row. Actually, the global cut-throat competition has impelled sellers, advertisers and marketers to use alternative techniques and compliment their traditional techniques with newer ones to reach their prospective customers. In simple words, advertising and marketing now takes place digitally in addition to the traditional modes. One such popular technique of marketing products and services through digital media is Viral Marketing. It refers to the spreading of product and service related information to a massive audience at a very fast speed through the use of the internet based services like blogs, tweets, e-mails, social networking sites, company websites, mobile messages etc. As these viral messages are transmitted digitally, they carry the benefit of being low cost or no-cost, speedy and high–impact mode of disseminating product and service related information to the prospects. India, being a country with the second highest population in the world and blessed with a huge internet using population base is one of the best Viral Marketing Destinations. The authors in this study have tried to show the effectiveness of Viral Marketing campaigns in India with the help of selected viral advertisements in recent times. The term Viral Marketing has also been known by terms like electronic Word of Mouth (e-WOM), Network enhanced word of mouth marketing, Stealth Marketing etc.

Information needs and information seeking behaviour of people with dementia and their non-professional caregivers: a scoping review

BackgroundPeople with dementia often require full-time caregivers especially in the later stages of their condition. People with dementia and caregivers’ access to reliable information on dementia is essential as it may have an important impact on patient care and quality of life. This study aims to provide an overview of the information needs and information seeking behaviour of people with dementia and their non-professional caregivers.MethodsWe conducted a scoping review of the literature and searched four electronic databases for eligible studies published up to August 2018. Two reviewers independently screened studies and extracted data. Information needs were classified according to emerging themes in the literature, and information seeking behaviour was categorized using Wilson’s model of information behaviour.ResultsTwenty studies with a total of 4140 participants, were included in this review. Reported information needs focused on: (i) disease; (ii) patient care provision; (iii) healthcare services; and (iv) caregiver self-care. The most commonly reported information need was on healthcare service-related information. Characteristics found to influence information needs were the severity of dementia as well as patient and caregiver status. People with dementia and non-professional caregivers mainly displayed active searching, information seeking behaviour and preferred using electronic sources to obtain health information.ConclusionCurrent dementia information sources available in English are extensive in the information they offer, but more emphasis needs to be placed on healthcare service-related information. All studies originated from high income countries and focused on information needs of non-professional caregivers only. The only variables found to be associated to information needs were severity of dementia condition as well as patient/caregiver status. The information needs identified in this review can be used to inform development and design of future dementia resources for people with dementia and their non-professional caregivers.