Mobile is one of the most famous communication infrastructures in the world. A very large percentage of the world’s population has access to mobile phones. The access to mobile phones is not restricted to developed countries only; it is even possible to the most remote areas of Latin America and Asia, and other remote regions. Hence, most countries aspire to deliver safe, effective and affordable health solutions to their citizens using mobile phones. When the usage of mHealth is considered, it usually includes the patient, application, and the healthcare provider which are interconnected to each other through a simple electronic device.For instance, mHealth is used in Remote Health Monitoring (RMH), in order to provide communication between physician and patients. As per survey report, approximately 15% of healthcare utilisation cost could be saved through remote health monitoring (Dahiya &Kakkar, 2018). We found one case study, which effectively uses mobile devices with IoT and Big Data to prevent the deadly disease named Ebola.
This study was a small-scale study conducted in Africa. A team of US researchers decided to conduct a dummy project to prevent the spread of Ebola. Hence a software application is designed and installed in the mobile phones of individuals, since it was a small-scaleproject; only 200 individuals installed it in their phones. The people with the application were supposed to fill some details and the physical conditions using their phones. The researchers were collecting their data in a database along with their location. In this way researchers were getting an idea about the affected area. Hence, when an individual enters in such area, he received a message saying this area is at higher risk of Ebola and these are the precautions which should be taken (Dahiya &Kakkar, 2018).
Several mHealth apps are helping healthcare workers in collecting and transmitting crucial data in real time. mHealth technologies are also used in educating people regarding their health and lifestyle. For instance, in Senegal a large-scale public campaign was initiated in which nearly 4 million messages were sent to public regarding the self-management of diabetes (Dahiya &Kakkar, 2018).
After selecting the articles for our literature review, we started analysing the articles, in order to do so; we first created a table which reflects what is presented in each article. This table helped us to understand the article as a whole, and helped us to locate different types of CSF’s, as in some paper CSFs were repetitive, while in others, CSFs were quite different.
|Article No.||Summary of the articles|
|1||It measures the feasibility and adaptability of the mHealth system by frontline health employees. In this paper Agarwal determine various factors which represent the efficiency and feasibility of mobile based health descriptions which includes advantages as well as limitations of the currently available mHealth devices. Therefore, overall this paper act as a great source of CSFs. (Agarwal et al., 2015)|
|2||This article conducts a systematic literature review and evaluates the case studies of mHealth to determine the pros and cons factors for using mHealth systems. The paper recognizes and highlights the CSFs in the area of mHealth. (Akter & Ray, 2010)|
|3||This describes and outlines the various features of Quality of Service (QoS) which helps to make a valid scale. By this scale they measure the Quality of Service of mHealth from a user perspective and also help in recognizing the CSFs relevant to usability and client satisfaction. (Akter et al., 2013)|
|4||This article recognizes and finds out the IoT application areas, their main goal and scope, their objectives and features, and limitations. They also evaluate the IoT technology to figure out the different evolutionary stages in its growth and progress. Their findings and discussion highlight the CSFs which are very useful for us.(Atzori et al., 2017)|
|5||Augustson et al. (2017) conduct a case study in China to measure the feasibility, user suitability, and efficiency of smoking termination program by sending text messages to users. They found that it can be positively delivered and easily accepted by users. Their study and results allow us to extract CSFs criteria and measurements (Augustson et al., 2017).|
|6||This article identifies the factors that limit the usage of personal details related to health in research. To do so they conducted a survey of clients, researchers, and dealers of healthcare devices and applications in order to identify the CSFs. (Bietz et al., 2016)|
|7||In this article researchers conducted a case study to check out the results of Parkinson’s disease and discussed the growth of IoT area in the health sector. This paper represents the most important challenges and positive factors of growth that can be used to identify CSFs (Cohen et al., 2016).|
|8||It recognizes the most important challenges and positive factors for these applications to figure out the experiences of users on mobile apps that support health changes. The explanation of clear important challenges and positive factors delivers a clear source to recognize CSFs.(Dennison et al., 2013)|
|9||In this study scholars conducted a systematic literature review and present work to figure out the different aspects of IoT and Big Data enabled health systems. This paper has very accurate information regarding our domain and helps to find out CSFs (Dimitrov, 2016).|
|10||Researchers review literature and current developments to recognize different problems raised by IoT technology such as social and policy issues. Additionally, this paper also highlights the various hindering problems; for example, policy and social issues which helps us to find out the organizational CSFs.|
|11||In this paper Felipe Fernandez describe some essential engineering frameworks viewpoints to break down the comparing multiplex decision space. This paper increases the inspection of the IoT space by observing some specific models and particular points of interest, which compare to significant alternatives of the included measurements. (Fernandez and Pallis, 2014)|
|12||This study came up with a technology model which is established on the basis ofan IoT acknowledgment model to determine which factors define IoT user acceptance. This article provides findings about the factors which plays a vital role in user acceptance and allow us to identify which of those factors should be considered CSFs (Gao & Bai, 2014).|
|13||This article evaluated IoT and healthcare to design a structure for an IoT enabled healthcare system to reduce the matter of device interoperability. This article provided a framework for a highly interoperable mHealth system (Gelogo et al., 2016).|
|14||It depicts the formation of mHealth system for cancer patients. This study further elaborated the critical success factors for the success of the mentioned mHealth system (Girgis et al., 2016).|
|15||Hartzler et al. (2016) attempt to discover the ease of use and acceptability of an mHealth application stimulating health behaviour change.|
|16||Hassanalieragh et al. (2015) audit current and future outcomes for mHealth applications utilizing wearable gadgets, IoT intelligence, cloud computing, and Big Data to recognize the assets and shortcomings. This article also highlights and describe our domain including few new big data issues which we may consider as CSFs.|
|17||This study is a literature review which covers the various IoT application areas to recognize and explore how terminologies, definitions, and ideas have developed which they utilized to recognize similar things and differences between areas. In this article we can recognize the restrictions and specifications ordinary to all IoT zones which we can apply as CSFs (Ibarra et al., 2017).|
|18||This paper presents an innovative thought of finding and anticipating an individual status and careful usage of induction method in sensor hubs to interface IoT networks. This paper determines the problems by presenting a surmising interference framework in view of a short and long-haul wellbeing status forecast and may act as source of CSFs (Jin Kang et al., 2015).|
|19||Mauriello et al. (2016) pursue an experiment on mHealth system which focus on how to develop the performance in providing care before birth including smoking termination, how to manage pressure and tension, and eating fresh healthy foods instead of packed and processed food. After experimenting they found that the mHealth system was able to develop the changes in behaviour as well as performance also and this paper helps to find out CSFs through its feasibility learning which leads to the success benchmark of this system.|
|20||This study is a well-structured literature review to decide whether the interventions using mobile phone are functional and efficient in refining the outputs for cardiovascular disease. When they recognize the outputs, reviews, and discussion, it gives CSFs which are very useful for us (Park et al., 2016).|
|21||This article covered a detailed investigation and analysis of undergraduate students to examine the approval of IoT using the technology acceptance model. The results and findings pinpoint the various corresponding factors which are very helpful to find out the CSFs (Prayoga & Abraham, 2016).|
|22||Raghu et al. (2015) used a cardiovascular disease (CVD) prediction tool to estimate the risks. They also utilized a management tool to create an mHealth framework for CVD screening, directing, and managing with a goal to focus on the chosen groups and healthcare contributors. From this article we get information about the technical characteristics and acceptance of tool by the users as well as a short-term discussion of company acknowledgement which make it useful as a foundation of CSFs.|
|23||Günter Schreier survey that if and how the Internet of Things for modified wellbeing (IoT4pH) can encourage the 4P healthcare insurance and talks about related difficulties and openings. This is an extremely relevant article that gives CSFs from system plan requirements and the difficulties that went up against (Schreier, 2014).|
|24||The scholars of this article administrated a study to signify whether patients will frequently use numerous mHealth devices to grasp their patient data. This survey was acceptable as feasibility criteria are a good source of CSFs(Shaw et al., 2016).|
|25||The authors executed a literature review, deliberate, and audit to create a theoretical model to conclude what user experience and service quality factors are responsible for better user acceptance. The focus is on wearable devices and mobile apps which helps us to examine user experience and service quality CSFs for mHealth devices (Shin, 2017).|
|26||This article examined progress related to IoT in healthcare to expand a theoretical framework for aspect of experience in user acceptance. It focused on social and technical aspects and how they collaborate which provides us a key source of CSFs (Shin & Hwang, 2017).|
|27||This article performed a thorough analysis of state of the art mHealth apps and services, and the most expressive research works. It is beneficial to our research as it also counts discussion of open and serious issues which may be potential CSFs (Silva et al., 2015).|
|28||Suciu et al. (2015) deploy very famous ICT technologies named as IoT, Big Data and cloud computing, their interaction, and its relevant works to define their own e-health architecture. The article is beneficial as the authors refer to common constraints and design suggestions which provideCSFs.|
|29||Ullah, Shah & Zhang (2016) study literature denoting the best ways for usingIoT in healthcare efficiently and come up with a model for eHealth. As a comparative review of studies and architectures, this article is good for technical CSFs (Ullah et al., 2016)|
|30||Researchers describedhow Big Data analytics framework can be established. From this paper we also come to know that what benefits they will have on business value. In the findings they discussed phases of Big Data analytics and the potential profits of using Big Data in healthcare which will might act as beneficial CSFs(Wang &Hajli., 2017).|
|31||This study plans to investigate the background, design, and capabilities of Big Data analytics to find out the gains of usingBig Data analytics in healthcare. Their investigation shows the logical procedures to accept the technology and its profits, which helps finding various CSFs (Wang et al., 2016)|
|32||This study develops an eHealth system for home care using IoT and Big Data which identifies patterns in patient data for healthcare decisions. This provides CSFs from system design requirements and the challenges (Wong, et al., 2017).|
|33||By examining usability methods utilized in mHealth literature this article finds out the accuracy and consistency of research and significance of usability in developing an mHealth application. Ease of use and usability are measured which helps find out how they act as a useful source of CSFs (Zapata et al., 2015)|
Table 2-summary of the articles
After writing the summaries of all the articles, we tried to locate all the CSFs discussed in the articles. Altogether, we got 17 different potential CSFs, which were discussed in the papers we selected. Along with the CSFs we also mentioned three main technologies in the table, which our report wants to keep inside. These are Big Data, IoT, and mHealth. We included these technologies as we only want to analyse those papers, which were taking these technologies into account, as we didn’t want to deviate from our selected area of research. After developing this table, we got a clear picture about gaps in the literature. As it is clear in the table all the papers are dealing with mHealth, which is then followed by IoT and Big data, where Big Data is least discussed as compared to other two.
In order to find the CSFs, we followed a simple approach, we tried to locate any barriers, opportunities, issues, disadvantages,or requirements, and then we discussed whether we can consider them as CSFs or not.
For discussion, we selected those CSFs which have high number of citation count. Furthermore, the CSFs whose cite count is very less, we merged that into the nearest CSF’s based on our understanding. For instance, only two papers were discussing about the number of text messages, as in patients were complaining about getting large number of text messages regarding their health. Hence, “number of text messages” were merged with the “user acceptance”.
Following this approach, we grouped a few CSFs together, which will be discussed later in this report. We did this merging to help us to understand the concepts. At this point of time, we are not considering the fact that factors might be influencing each other, or which have the largest effect. They might or might not do so, however we kept the report scope simple by just sticking to our main topic, which is to find the relevant Critical Success Factors (CSF).
Table 3- Critical Success Factors (Continued)
Table 4- Critical Success Factors
After reading through the selected articles, we got many CSFs related to the use of IoT and Big Data in the mHealth industry. From the table listed in the literature review section, we tried to identify the closely related CSFs and we divided those CSFs into categories. According to our understanding and from the findings of the literature, we divided the CSFs into three categories such as user factors, organisational factors, and device factors.
Organisational factorsare a very important factorand is discussed by most of the studies we reviewed. There are many sub-factors under the category of organisational factor such as organisational policies, organisational culture, and management involvement.
As discussed in the user factor, many users were very concerned about their privacy; hence it is very important to set regulations for how the data collected from the mHealth devices will be used and how it will be shared. These regulations should be properly introduced and implemented (Akter & Ray, 2010). GünterSchreier (2014) further suggested that security and privacy of the mHealth are main two necessities of the mHealth systems, however, it is difficult to maintain the balance between the two necessities. Hence, there is no such solution in the market which offers both full security and privacy. The author further discussed that to provide legitimate access to user’s data for optimizing user’s health, they must also on the flip side create weak points allowing access of other third parties. Hence, the literature is emphasizing the implementation of standards and policies. Girgis et al. (2016) further added that sometimes the user’s data is also manipulated, hence, it is very important to maintain the standards to protect the accuracy and completeness of the data. Atzori, Iera & Morabito (2017) suggested that it is very important to implement the standards, in order to do so, it is very important to teach the users of mHealth technology the new standards.
Organisation culture also plays a very important role in the implementation of mHealth with IoT and Big Data. As we all know, when we implement a new solution or system, it is very important to have support from the organisation staff in order to make it a success, however, sometimes the staff feel reluctant to adapt to a new system. Hence, it is very important to provide sufficient training and education regarding the use of new system(Agarwal et al., 2015). For instance, the implementation of mHealth provides a shift from the paper-based systems to the technical systems, which cannot be achieved without the help of training and education as suggested by literature (Agarwal et al., 2015). In addition, the implementation of mHealth along with IoT and Big Data requires new business models as these may bring excessive work to the physicians or healthcare providers. (Wang, Kung & Byrd, 2016). Hence, in such scenarios it is very important to manage the resistance of the organization’s workers. Furthermore, when one is dealing with IoT and Big Data analytics, the analysis and the decision making process are very time consuming and require lots of effort, so if the results collected using Big Data and IoT are not analyzed properly then they will not make any sense and will not be useful. Hence, the organization culture is very important, which is offering the training and education regarding the use of mHealth tools.
The focus of this report is on use of IoT and Big Data in mHealth. All these three things need different expertise and various stakeholders. All the stakeholders from three different fields need to work together, and then only the system can be implemented successfully. Agarwal (2015), further supported the above argument by saying that, it is very important to have an effective involvement of all the stakeholders. Users and the community are also described as important stakeholders in the implementation of IoT and Big Data in mHealth (Raghu et al., 2015). Literature further added that patients can be considered as testers and they can provide effective feedback regarding the features, functionalities of the mHealth devices. Hence, the user experience factor can be improved by engaging the users in the implementation.
Figure3 mHealth ecosystem
User factor in the implementation of mHealth using IoT and Big data is one of the most discussed areas in almost all the papers we selected. There are many CSFs discussed under this category such as user acceptance, ease of use, affordability, user awareness, and user centric.
During our research we found plenty of studies which were focussing on the user acceptance, ease of use, and user centric as a contributing factor in the continuous use of IoT and Big Data in mHealth. The use of IoT and Big Data in mHealth depends on the fact that, whether it is liked by the users or not (Atzori et al., 2016) (Akter et al., 2013) (Cohen et al., 2016) (Hartzler et al., 2016).
The above-mentioned argument completely falls in line with the TAM Technology Acceptance Model). As per TAM theory, perceived usefulness and user acceptance can have an impact on the behaviour intention of any consumer to use a particular technology(Gao & Bai, 2014).
Ease of use
This term implies the trust and confidence the users have in the system (Girgis et al., 2016). Shin & Hwang (2017) added that the people will only use such technologies if these are easy to use, meaningful, and trustworthy. Hence for a technology to be loved by people, it is very important for it to be meaningful and fun for users. If the technology is making people uneasy, then they will definitely tend to avoid it. For instance, a study conducted by Dennison et. al (2013) found that people using mHealth services were annoyed with large number of messages they are receiving regarding their health condition, they said it doesn’t feel good to read large amounts of messages.
Affordability is considered a major factor, which contributes towards the use of mHealth services by users. According to various studies, mHealth services are quite cheap. As mobile devices are cheap and are used worldwide, it helps large number of people to connect with each other through messages or apps including the patients and the physicians (Augustson et al. 2017) (Cohen et al., 2016) (Gelogo et al., 2016) (Dennison et al, 2013).
However, according to Cohen, Bataille & Martig (2016), in IoT enabled mHealth, sensors and other expensive instruments are also being used which might not be as affordable to the users as the mobile devices are. They further argued that the medical devices such as accelerometer, sensors, and gyroscope provide much better and accurate results as compared to smart phones and smart watches. Hence, accuracy will be compromised if we consider the money factor.
User centric nature of the devices used in mHealth is also considered as one of the main factor which can lead to the prolonged use of the mHealth services incorporated with IoT and Big Data (Gao & Bai, 2014)(Hartzler et al., 2016). Hartzler et al.(2016) further argued that the user acceptability of any device depends on the user’s perspective on its flexibility and usability. They further added that if the device is meeting the needs of the users, and the user can have the general sense of control on the device, then the users will tend to use it more.
A study was conducted in order to check the perceived utility of the mHealth device using a personalized coach. The study results show that the users feel connected with the virtual coach, it was giving them more flexibility, and users can also set personalized goals. Hence users found utility when the device comes along with flexibility and accountability (Hartzler et al., 2016). Atzori et al (2016) found that users are very concerned when they found that their security can be shared with the third parties. As per survey results, 68% of the users of mHealth services will only share their data if their privacy is assured (Bietz et al. 2016). The study further stated that the informed consent of the user is very much required before sharing the user’s data. This argument is further supported by Dennison et al. (2013), who found that the users were concerned about what their devices can do with their data.
Akter et. Al (2013) emphasised on the user awareness as an important factor as far as the use of mHealth services are concerned. The argument is further backed up by various other authors. Agarwal et al. (2015) suggested that, it is not only the users who lack awareness regarding the use of mHealth services, sometimes the service providers such as physician also lacks the awareness regarding the use and functions of mHealth devices. Agarwal et al., (2015)further added that sometimes the physicians or the clinicians are very busy with their usual routine, and they do not get time to learn and adapt to the new skills regarding the use of mHealth devices.
Bietz et al.(2016) suggested that sometimes the mHealth devices might not be secured, sometimes the user feels reluctant to use the mHealth devices as they might don’t trust the devices, or they might do not know how the devices work. Hence, they further argued that it is very important to provide further education and training to the staff such as clinicians or physicians. Most of the time, the mHealth apps dealing with IoT and BigData, are considered as complex and undesirable to the society (Dennison et al., 2013).
Another important factor listed by the literature is that the mHealth apps may collect large amounts of data but it is very important to critically analyze and interpret the result (Akter & Ray, 2010). Hence, it is very important to educate the patients and the clinicians or other people who will be using the apps, so that the best results can be achieved.
Device Factors represents the factors which belongs to technological abilities of hardware and IoT devices used in the proposed solution. mHealth will need a wide variety of devices from various manufacturers which are enabled with bothBig Data and IoT and there are many challenging components that needed to be included (Dimitrov, 2016).
Interoperability in mHealth refers to the ability of device’s information system and software to exchange information. Therefore, interoperability will be a noteworthy challenge while executing IoT enabled mHealth projects. For the completion of this task they will need the coordination of various devices which execute many various tasks and store that information in different formats (Akter & Ray, 2010) (Gelogo, Hwang & Kim, 2015). A wide variety of gadgets can throw the reliability and availability of information into uncertainty, making it complex for many IoT network gadgets to be included, excluded, or modified through a lifecycle which makes the data analysis more difficult (Bietz et al., 2016)(Hassanalieragh et al., 2015).
Security and Privacy
The usage of IoT technology comes with pros as well as cons to the privacy and security of the data, as there is a chance that the data will be leaked while sharing between the wireless devices. In addition, cloud technology used for storage or computing will may create many additional chances for privacy loss (Hassanalieragh et al., 2015). The mixture of varied sources of raw facts and the use of information sharing unlocks the chance that privacy can be lost by combining two or more sources of data (Bietz et al., 2016). Security and privacy are indispensable part from a part of user acceptance as most of persons will only share details about their personal health if their data is secured and privacy is guaranteed (Bietz et al., 2016).
Reliability and Availability
The next feature is the devices reliability and availability which refers to the quality of being trustworthy and the necessity of devices to sustain the connection to the network and to constantly perform well. This is particularly significant as mHealth solutions frequently focus on how to dealing with remote areas to send treatment as in remote areas don’t have internet or mobile reception (Agarwal et al., 2015) (Akter & Ray, 2010).
To prevent threats, bugs, and issues which destroy the reliability of the system and its quality of services, these projects must give guarantees that they are robust and durable to prevent failures in the future (Akter, D’Ambra & Ray, 2013) (Atzori, Iera & Morabito, 2017). There is a need to manage the devices carefully to confirm the availability and efficiency of a system, as well as data transfer between devices, and to diminish outages and maintenance costs (Dimitrov, 2016).
The devices and technologies that are involved in IoT enabled mHealth systems have various restrictions. IoT enabled mHealth devices and sensors both have disadvantages, for example, the size of display screen, processing power, memory, data transfer capacity, and the limits and consumption rates of batteries. These act as boundaries that lower effectiveness because they may cause incomplete or corrupted data (Akter & Ray, 2010) (Cohen, Bataille & Martig, 2016).
The restrictions need proper maintenance to be established and solved carefully because as limitations they can decrease the acceptance of users. For example, battery to be utilized in a system also means the need for users to do regular recharges (Atzori, Iera & Morabito, 2017) Cohen, Bataille & Martig, 2016). Many devices are not only small but wearable also which means some devices can be worn on,under, and top of the clothing,which also lowers the battery size and user mobility (Hassanalieragh et al., 2015). Technology issues can be made worse even further due to installation in resource poor places (Akter & Ray, 2010). Therefore, technical restrictions will evidently be a foremost challenge as balances must be made between technical requirements of a system, and with non-functional requirements of devices such as affordability or acceptability.
Scalability is an indispensable part for the usage of IoT technology. To include or to remove IoT devices from the system is very essential to complete the requirements for flexibility; adjusting the amount of devices to meet needs, particularly when we are aiming on providing Big Data analytics. (Dimitrov, 2016) (Dutton, 2014,). Achieving scalability and affordability as well as balancing the technical restrictions can be helped through the use of cloud storage and computing(Agarwal et al., 2015)(Hassanalieragh et al.,2015).
Efficient dataanalytics is the main purpose for accessing information from Big Data in an mHealth system. This system has three crucial segments. They are: Gathering data that includes ingestion, aggregation, or extraction;Processing data, also stated as analysis or transformation of data; and Visualizing data, which means the assessing and reporting of data. (Cohen, Bataille & Martig, 2016,)(Dimitrov, 2016,)(Ibarra-Esquer et al., 2017)(Wang &Hajli, 2017). The main objective of these segments is to transform the data into clinical measures, accurate values, and new bits of knowledge, including some that could not be attained without Big Data analytics (Cohen, Bataille & Martig, 2016)(Dimitrov, 2016)(Ibarra-Esquer et al., 2017).
Data security & Data type
Allocation and sharing of data or information between the organizations is not always secure. Therefore information sharing is frequently restricted by the lack of interoperability of systems; for example, utilizing trademarked protocols for each device, and legacy systems (Akter & Ray, 2010) (Dimitrov, 2016) (Wang, Kung & Byrd, 2016). Data gathering will be a great barrier as drawing effective data means extracting important data from various types of sources and devices which further means that the data gathered or extracted is extremely diverse. As each device and source has different reliability and scalability, various data formats, and diverse methods and frequencies to communicate, this acts as a great challenge for effective data analytics (Hassanalieragh et al., 2015) (Wang, Kung & Byrd, 2016).
In our results, we have clearly identified our CSF’s into four major categories including organizational factors, user factors, device factors and other factors which included decision making and data analytics. All these factors were further divided into subcategories which highlighted the technological ideas about IoT, Big Data and mHealth. These categorizations of CSFs were then compared with the CSFs described by Pinto, J. K. (2015) in their Project Implementation Profile (PIP) to find which of these factors are relevant enough and how they compared to CSFs in another area.
Based on our research and understanding about the project, and this comparison, we were able to spot 5 different Critical Success Factors. Each of these factors was purely based on the 33 journal articles that we had chosen and referred in our research. Below are the listed CSFs and their explanation as to why we selected these Critical Success Factors:
1. Top Management Support
2. Technical tasks
3. Client consultation
4. Client acceptance
According to Jeffrey Pinto (2014), Following CSF’s were relevant to our research which are as follows:
Top Management Support: Some of our papers that were referenced by the team fulfils the Critical Success Factor ‘Top Management Support’ and is long been considered of great importance in distinguishing between ultimate success and failure. Managers and their teams not only are dependent upon top management for authority, direction and support but also are the conduit for implementing top management plans or goals for the organization. If the project has been developed from internal audience, the degree of management support will lead to significant variations in the degree of acceptance or resistance to that project or product. Top management support may include aspects like allocation of sufficient resources as well as project management’s confidence in support from top management in the event of crisis.
Technical Tasks: Some papers also referenced by our team fulfils the Critical Success Factor ‘Technical Tasks’ which basically refers to the necessity of having not only the required numbers of personnel for implementation team but also ensuring that they possess the technical skills, technology, and technical support needed to perform the tasks. It is also important that people managing the project understand the technology involved. In addition, adequate technology must exist to support the system. Without the necessary technology and technical skills, projects tend to quickly disintegrate into a series of miscues and technical errors.
Client Consultation: Some papers referenced also fulfil the Critical Success Factor ‘Client Consultation’ because it gives high levels of client acceptance in the project. A client is nothing but an end-user who will be using the product of that project either as a customer or as a part of a department of the organization. Nowadays, need for client consultation has been recognized as an important point of system implementation which means clients are personally involved in the process of implementation of that project which will directly co-relate their support for the project.
Client Acceptance: Most of the papers referenced by our project team fulfil the critical success factor ‘Client Acceptance’. A client is nothing but an end-user who will be using the product of that project either as a customer or as a part of a department of the organization. It basically refers to the final stage in the project development process, at which time the overall efficacy of the project must be determined. In addition to client consultation, at an earlier stage in the development process, it remains of ultimate importance to determine whether the user or client for whom the project is initiated will accept it.
Communication: Papers referenced by our project team also fulfils the Critical Success Factor ‘Communication’ because they typically involve feedback mechanisms from both clients and the rest of the organization with all the concerns of the project capabilities, goals of project, changes in any policies and procedures, status reports etc. Therefore, it includes better communication between project members and end users.
We also found that since the topic of mHealth was a narrowed down topic under healthcare sector, which was why we faced few challenges in searching case studies relevant to our research. Since limited case studies were present during the search, we opted for one case study that was relevant and spoke about the Ebola Outbreak in Africa.
Limitation and future research
During our research we only considered three databases and reviewed only 34 articles due to time constraints. As a result, we might have missed some of the relevant literature. Furthermore, we neglected the articles from other languages, there is a great possibility that we missed the chance to review the relevant literature of other languages which further can restrict our understanding of the domain of our study and findings. Lastly, the literature we reviewed was mostly only about client’s and user’s perspective. However, we missed the opportunity to analyse the organisational perspective in detail such as project budget, HR, and project schedule, which are very crucial success factors as suggested by Pinto, J. K. (2015). Further research can be conducted to find the role of HR, project schedule, project budget, and other management aspects in the mHealth sector to generate business value.