A CUSTOMER COMMUNICATION APPLICATION FOR ORGANISATIONS Shumani Netangaheni 762831 A business venture proposal submitted to the Faculty of Commerce, Law and Management, University of the Witwatersrand, in partial fulfilment of the requirements for the degree of Master of Business Administration Johannesburg, 2022 Protocol number: WBS/BA762831/557 August 2022 DECLARATION I, Shumani Netangaheni, declare that this business venture proposal is my own work except as indicated in the references and acknowledgements. It is submitted in partial fulfilment of the requirements for the degree of Master of Business Administration in the Graduate School of Business Administration, University of the Witwatersrand, Johannesburg. It has not been submitted before for any degree or examination in this or any other university. Shumani Netangaheni Signed at Johannesburg On the 25th day of August 2022 DEDICATION This thesis is dedicated to my partner Sipho Mthimunye who brainstormed this idea with me during initiation stage. He also gave me the time needed to focus on my research. I would also like to dedicate this thesis to my children Sbusiso, Azania and Lwazi who were forever understanding that I needed to work on this. ACKNOWLEDGEMENTS I would like to firstly thank my supervisor Sylvester Senyo Horvey who came in last minute when I was stranded and helped shaped this paper. It is unfortunate that we did not have enough time together, otherwise we would have produced magic. All things considered, we have tried our best. I would also like to thank Dr Thabo Mosala for temporarily assisting and help shape the thinking. I would also like to thank Dr Rendani Mamphiswana who helped explain the principles of research and approach. I would also like to thank Dr Siphamandla Zondi who did the initial explanation of the structure of research. I would also like to thank my partner and children for allowing me to lock myself in the study to focus on my thesis. I would also like to thank my sisters who took the kids for the weekend to give me an opportunity to focus. SUPPLEMENTARY INFORMATION Supervisor: Sylvester Senyo Horvey Word count :22 934 Supplementary files: Distributed Questionnaire_762831 Qualtrics Output_762831 SPSS Output - Statistical Analysis_762831 Ethics Certificate Shumani – Netangaheni Submission checklist and supervisor evaluation form: March 2022 Consolidated submission forms_762831 ABSTRACT The aim of this study was to investigate the need for an integrated communication application dedicated to service communication for organisations as well as to determine factors that users consider important for the adoption of a new communication mobile application system. This study is important to the building of an application where resources are constrained and need to be traded off. The growth of social media has seen a rise in the need for consumers to instantly want access to information that is of importance to them. It is for this reason that communication has become a commodity that organisations invest in to build stronger relationships with their customers. However, it is not always easy for organisations to get it right as the communication methods they rely on may not be efficient or cost effective. It is for this reason that theoretical work was consulted to determine a need for a central communication application which various organisations can plug into to publish messages when they have service failure or recovery. The literature advised of a need but for a different problem. Primary data was collected from respondents using a survey where respondents were asked if they would use an integrated mobile application dedicated to communication when their service providers have service failure and when the service has recovered. TABLE OF CONTENTS DEDICATION .................................................................................. 3 ACKNOWLEDGEMENTS ................................................................ 4 SUPPLEMENTARY INFORMATION ............................................... 5 ABSTRACT ..................................................................................... 6 1 CHAPTER 1. INTRODUCTION ............................................ 12 1.1 MOTIVATION FOR THE STUDY ............................................................ 12 1.2 RESEARCH PROBLEM ....................................................................... 14 1.3 RESEARCH OBJECTIVES .................................................................... 16 1.4 SIGNIFICANCE OF THE STUDY ............................................................ 16 1.5 CONCEPTUALISATION OF CENTRAL RESEARCH THEMES ....................... 16 2 CHAPTER 2. LITERATURE REVIEW .................................. 18 2.1 INTRODUCTION ................................................................................ 18 2.2 CUSTOMER COMMUNICATION THEORY ............................................... 19 2.3 CUSTOMER COMMUNICATION DURING SERVICE FAILURE ...................... 20 2.3.1 HOW SHOULD ORGANISATIONS ORGANISE THEMSELVES FOR AGILE COMMUNICATION 20 2.3.2 FORMS OF CUSTOMER COMMUNICATION ................................................................ 21 2.4 PLATFORM BUSINESS STRUCTURE ..................................................... 24 2.5 PLATFORM USER ADOPTION .............................................................. 27 2.5.1 FACTORS INFLUENCING NEW TECHNOLOGY ADOPTION ............................................ 27 2.5.2 RANKING OF FACTORS INFLUENCING NEW TECHNOLOGY ADOPTION ......................... 30 2.6 HYPOTHESIS.................................................................................... 31 2.6.1 RESEARCH MODEL ............................................................................................... 31 2.6.2 HYPOTHESIS ........................................................................................................ 31 2.7 SUMMARY ....................................................................................... 33 3 CHATPER 3. RESEARCH METHODOLOGY ...................... 34 3.1 INTRODUCTION ................................................................................ 34 3.2 RESEARCH APPROACH ..................................................................... 34 3.3 RESEARCH DESIGN .......................................................................... 36 3.4 DATA COLLECTION METHOD AND INSTRUMENT .................................... 36 3.5 POPULATION AND SAMPLE................................................................. 37 3.5.1 POPULATION ........................................................................................................ 37 3.5.1 SAMPLING METHOD AND SIZE ................................................................................ 38 3.6 DATA COLLECTION PROCESS ............................................................. 40 3.6.1 INTRODUCTION ..................................................................................................... 40 3.6.2 DATA COLLECTED ................................................................................................. 41 3.6.3 PILOT STUDY ........................................................................................................ 43 3.6.4 DATA COLLECTION TOOLS AND TIMEFRAME ............................................................ 43 3.7 DATA ANALYSIS ................................................................................ 43 3.7.1 INTRODUCTION ..................................................................................................... 43 3.7.2 DATA ANALYSIS TECHNIQUE .................................................................................. 44 3.7.3 DATA ANALYSIS TOOLS ......................................................................................... 44 3.8 LIMITATIONS .................................................................................... 44 3.9 VALIDITY AND RELIABILITY ................................................................. 45 3.9.1 VALIDITY .............................................................................................................. 45 3.9.1 RELIABILITY ......................................................................................................... 45 3.10 RESEARCH ETHICS ........................................................................... 46 3.11 SUMMARY ....................................................................................... 47 4 CHAPTER 4. PRESENTATION OF FINDINGS .................... 48 4.1 INTRODUCTION ................................................................................ 48 4.2 SAMPLE SIZE AND DEMOGRAPHICS .................................................... 48 4.3 NEED FOR AN INTEGRATED COMMUNICATION APPLICATION ................... 51 4.4 FACTORS INFLUENCING APPLICATION ADOPTION ................................. 54 4.4.1 PRODUCT RELATED FACTORS ................................................................................ 54 4.4.2 SECURITY RELATED FACTORS ............................................................................... 55 4.4.3 SOCIAL RELATED FACTORS ................................................................................... 55 4.5 STATISTICAL TESTING ....................................................................... 56 4.5.1 VALIDITY AND RELIABILITY .................................................................................... 56 4.6 SUMMARY ....................................................................................... 59 5 CHAPTER 5. DISCUSSION OF FINDINGS .......................... 60 5.1 INTRODUCTION ................................................................................ 60 5.2 NEED FOR AN INTEGRATED COMMUNICATION APPLICATION ................... 60 5.2.1 INTRODUCTION ..................................................................................................... 60 5.2.2 INTEGRATED APP USAGE ...................................................................................... 60 5.3 FACTORS INFLUENCING APPLICATION ADOPTION ................................. 63 5.3.1 INTRODUCTION ..................................................................................................... 63 5.3.2 PRODUCT RELATED FACTORS ................................................................................ 63 5.3.1 SOCIAL INFLUENCE RELATED FACTORS .................................................................. 65 5.3.2 SECURITY RELATED FACTORS ............................................................................... 67 5.4 SUMMARY OF FINDINGS .................................................................... 68 6 CHAPTER 6. BUSINESS PLAN ........................................... 70 6.1 DESCRIPTION OF THE OPPORTUNITY .................................................. 70 6.2 INDUSTRY AND MARKET ANALYSIS ...................................................... 72 6.2.1 INDUSTRY FACTORS ............................................................................................. 72 6.2.2 CORE COMPETENCIES .......................................................................................... 73 6.3 MARKETING PLAN ............................................................................. 74 6.4 FINANCIAL PLAN ............................................................................... 75 6.4.1 PROJECTED INITIAL COSTS .................................................................................... 75 6.4.2 PROJECTED FINANCIALS ....................................................................................... 75 6.4.3 PROJECTED INCOME STATEMENT .......................................................................... 76 6.4.4 PROJECTED BALANCE SHEET ................................................................................ 77 6.4.5 PROJECTED RETURN ON INVESTMENT .................................................................... 77 6.5 HOW THE APP WILL WORK ................................................................. 78 7 CHAPTER 7. LIMITATION AND CONCLUSION .................. 79 7.1 LIMITATION ...................................................................................... 79 7.2 CONCLUSION ................................................................................... 79 REFERENCES .............................................................................. 80 8 APPENDIX A: QUESTIONNAIRE AND CONSENT FORM .. 89 9 APPENDIX B: DATA OUTPUT ............................................ 93 List of Tables Table 1: Description of hypothesis for the proposed research model for adoption ......................................................................................................................... 32 Table 2: Demographic and mobile experience ................................................. 51 Table 3: Number of Apps downloaded ............................................................. 52 Table 4: App type preference ........................................................................... 52 Table 5: App type ranking ................................................................................ 53 Table 6: Communication App need .................................................................. 53 Table 7: App characteristics ranking ................................................................ 55 Table 8: App security characteristics ranking ................................................... 55 Table 9: Social characteristics ranking ............................................................. 56 Table 10: Reliability testing .............................................................................. 57 Table 11: Reliability testing – with deleted items .............................................. 57 Table 12: Correlations (mobile experience, number of Apps, usage ranking, age, education) ........................................................................................................ 58 Table 13: ANOVA (number of downloaded Apps and communication App) .... 58 Table 14: Regression analysis ......................................................................... 59 Table 15: Cost structure ................................................................................... 75 Table 16: Expected volume and revenue ......................................................... 76 Table 17: Projected income statement ............................................................. 76 Table 18: Projected financial position ............................................................... 77 Table 19: Performance ratios ........................................................................... 77 List of Figures Figure 1: Core processes of service dominant logic ......................................... 18 Figure 2: Communication Technology Evolution .............................................. 22 Figure 3: Platform Business Structure .............................................................. 25 Figure 4: Proposed Research Model for Adoption ........................................... 31 Figure 5: Sampling Techniques Diagram ......................................................... 39 Figure 6: Planning and Collection Process for Primary Data Collection Techniques....................................................................................................... 40 Figure 7: Cronbach's Alpha Rule of Thumb ..................................................... 46 Figure 8: Market Analysis and Environment Analysis ...................................... 72 Figure 9: Core Competence Decision .............................................................. 73 Figure 10: How communication App works ...................................................... 78 1 CHAPTER 1. INTRODUCTION 1.1 Motivation for the study Organisations reach out to their customers to communicate different messages, ranging from campaigns, service failure or service changes. They use various communication medium like short message service (SMS), eMail, organisation website or social media. The introduction of ‘Treating Customer Fairly (TCF)’ by the Financial Services Conduct Authority (FSCA), which TCF Outcome 3 states that “Customers are given clear information and are kept appropriately informed before, during and after the time of contracting”, has elevated the need for organisations to communicate to their customers when they are unable to meet the promised service (Financial Services Board, 2014). Social media has also increased the need for organisations to communicate more frequently to their customers than they had done in the past. However, the use of social media has mainly focused on organisation branding, advertising, promotion, and handling customer queries (Farzana et al., 2015). Organisations have decided to continue communicating service failure mainly by sending email or SMS, while social media is usually reserved for positive engagements. This, however, is contradictory as service failure usually spread on social media through word of mouth giving opportunity for social media users to spread misinformation or negative information (Grégoire et al., 2015). Organisations do use social media for post crisis management to apologise or to set the record straight and rebuild relationship with its customers (Triantafillidou & Yannas, 2020). Social media is used for both Person to Person (P2P) and Business to Customer (B2P) communication, therefore introduces a dilemma of clutter. Customers must surf through information they are interested in, and at times such information of interest is not available. This then moves the communication to contact centre, where customers call in to find information they are looking for (Ukpabi et al., 2019). The challenge with contact centre is that it is customer service personnel dependent which is both cost intensive and may lead to customer dissatisfaction if the personnel is not well trained (Ukpabi et al., 2019). To improve customer communication, there is a need for a communication channel designated to customer communication where there is no information overload, and it is costs effective (Cai et al., 2020). Organisations website and mobile apps would have served this purpose well, however, to respond faster, organisations would need to make real time updates on their websites or mobile apps. This also does not provide the centrality provided by the likes of Twitter and Facebook as each organisation would need to manage their own website and mobile app content real time (Dwivedi, et al., 2021). Studies have been conducted by Cai et al (2020) and others to determine the need for central communication systems as well as studies by Rehncrona (2022) and others on factors that influence technology adoption using technology adoption model (TAM). However, studies conducted by Rehncrona (2022) and others focused on specific industries other than customer failure communication. This research aimed to bridge this gap in the body of knowledge by investigating a need for a cost-effective central communication system that can be used by all willing organisations to update their consumers of service failure real time. The study also leveraged technology adoption model to give users opportuning to rank factors they consider important on a service failure communication App to ensure high adoption rate. 1.2 Research Problem There are two main research problems which are to investigate from consumers the need for a central service failure communication system and determining characteristics of the applications users find more important for adoption. When there is service failure, organisations would like to communicate to their customers using communication mediums that are efficient and effective (Zhang et al., 2018). Organisations have a choice to send communication through SMS, eMail, organisation website, in App messages or social media platforms like Facebook and Twitter (Claeys & Coombs, 2020). SMSes and eMails are reliant on the accuracy of customer data and could be cost intensive for organisation with high number of customers, while social media is a shared platform used by businesses as well as personal use (Chen & Aklikokou, 2020). eMails and SMSes also increase the risk of fraud as they are susceptible to phishing (Moore & Clayton, 2007). Organisations that fail to communicate when there is service failure risk having customers calling in to find the information. This could increase the cost of doing business as it is customer service personnel dependent and customer satisfaction is dependent on the level of training and the experience of the personnel (Schwager & Meyer, 2007). Social media platforms like Facebook and Twitter have proven there is a desire for people to access information instantly and frequently, which most organisations have tried to respond by registering their organisations into these platforms to communicate with their customers and potential customers (Chen & Aklikokou, 2020). However, the use of social media has mainly focused on marketing and public relations rather than customer service (Sutherland et al., 2020). Users of social media also do not go to social media platforms to see if their organisations have messages for them but to socialise with family and friends (Kowal et al., 2020). To overcome the challenges of social media clutter while making sure customer is communicated to instantly during service failure, the business venture aims to build a central mobile application (App) that various organisations can utilise to communicate to their customers in an event of service failure. Jayarathna & Hettige (2013), argued the need for a multi-agent technology for communication among related persons. The argument by Jayarathna & Hettige (2013) for multi- agent communication technology was raised for agricultural sector only indicating a gap for other sectors . A study was conducted to investigate the need for an integrated communication App dedicated to service communication for organisations. The study revealed there is a need for a single App that can be used by various organisations to communicate to their consumers when they are unable to meet the service level agreement. This business venture will be developing this App to respond to the need identified. The shared mobile application will be accessible to all organisations that provide services and would like to post messages to their consumers. Consumers of services will also have access to the mobile application to view messages from their service providers. The challenge with building a new mobile application or technology is getting users to utilise the application. The other challenge with building a new system is not knowing how to utilise resources to focus on technological factors users of technology consider important. To overcome this challenge, building a new technology while navigating resource constraints requires an organisation to do trade-offs and put more resources on factors that users consider more important. A study done by Luo et al. (2013) on technology adoption trade-offs ranked perceived usefulness number one and risk was ranked last at number eight. This study was generic, not specific technology focused. This study gave users an opportunity to rank factors they consider important for technology adoption to ensure factors with high ranking are given high priority when developing the new communication mobile application (App), and most respondents indicated they want the technology to work, and it must be free of charge. When this business venture develops this communication App, the highly ranked technology characteristics will be given high priority. 1.3 Research objectives Objective 1: To investigate the need for an integrated communication application dedicated to service communication for organisations. Objective 2: To determine factors that users consider important for the adoption of a new communication mobile application system 1.4 Significance of the study Literature review revealed that there are enough studies on the adoption of technology, however, studies conducted focused on specific industries other than customer failure communication. Other studies looked at the need for a central communication, however, the targeted audience were not for consumer communication when an organisation has a service failure. This research aims to build on the technology adoption model and provide rankings of the technology characteristics users find important by asking users to directly rank the factors against each other. This study is important to the building of an application where resources are constrained and need to be traded off. 1.5 Conceptualisation of central research themes The initial focus was theoretical work with literature review (chapter two) on customer communication theory exploring why organisations see the need to communicate to their customers and the benefits they hope to derive from communicating. Customer communication will then be narrowed down to customer communication when there is service failure and the mediums used to communicate. The agility and the effectiveness of the available mediums will be reviewed. The focus will then shift to platform business analysing factors critical to the success of the platform business. Few examples will be analysed to see how those businesses made a success of platform business. The last part of the literature review will focus on what users are looking for in a technological application platform and what drive them to accept the new application. Literature review will be expanded to the ranking of factors that customers consider important to application adoption. Research methodology (chapter three) explores to determine best practice in designing research questions; method used to collect the data; tools used to analyse the data; and what how to ensure the data collected answers the research question by testing validity and reliability of the instrument used to collect the data. Chapter four presents the findings with the aim of finding out what the respondents said in relation to the questions asked to address the research problem identified. Chapter five discusses the results findings with the aim of finding out what the respondents said in relation to the questions asked to address the research problem identified. Results are analysed to determine the outcome of the study. Statistical test was also conducted to ensure validity and reliability of the internal factors. Chapter six discusses the business opportunity identified and how the business venture will be setup. This chapter also analyses industry forces that can help or hinder the success of the business. Marketing plan and venture financials are also provided. Chapter seven will discuss the limitations of the study and provide a conclusion of the study and as well as the opportunity of building a business venture. 2 CHAPTER 2. LITERATURE REVIEW 2.1 Introduction The existence of an organisation rests on its customers or those it provides services to (Fader, 2020). Organisations include those who provide service for profit, non-profit, public, or private sectors. Without its customers, organisations would not exist for long. It is for this reason; organisation would like to be perceived by its customers or potential new customers to be providing a good service to continue serving them or acquire (Edvardsson & Olsson, 1996). Every organisation that provides goods and services has an inherent risk of unplanned and possibly unforeseen service failure or planned interrupted service for maintenance or service upgrade. Depending on the nature of the relationship between the customer and the organisation, the customer understanding of the service failure will vary, therefore organisations should keep service interruptions to a minimal (Hess, Ganesan, & Klein, 2003). The diagram below depicts the service ecosystem showing how customer service influence economic exchange and value creation. Figure 1: Core processes of service dominant logic Source: (Fujita et al., 2018) The service dominant logic framework encompasses resource integrations and actors coming together to create service exchange facilitated by institutions, to create a service ecosystem. In an event an organisation is unable to meet the promised service level or customer’s needs, there could be negative consequences such as customer complaints, negative word of mouth and customer loss (Kruger et al., 2015). The growth of social media and search engines facilitated by the internet revolution has created new customer expectations. There is an expectation that information would be available instantly and customers can easily compare organisation responses in an event of a crisis. Organisations that are perceived to be doing better attract more customers (Barnes & Cumby, 2002). Products and services are also reviewed online before customer makes a purchasing decision, therefore organisations with negative or no digital footprint suffer the consequences of losing out a sale to their competitors (Kushwaha et al., 2020) This literature review will critically evaluate the literature in the context of customer communication; mediums used by organisations to communicate to their customers during service failure; analyse platform business model and factors that influence users to adopt new technological platform and their ranking. 2.2 Customer communication Theory Communication facilitates information sharing between parties (Lin, 2003). Communication has become a commodity that organisations invest in to build stronger relationships with their customers (Barnes & Cumby, 2002). Crisis management communication focuses on managing public perception than company strategy (Marsen, 2020). The rise of social media and platform business have seen more organisations pushing out messages to their customers than before (Wright et al., 2010). Communication channels are mainly used to sell or cross sell products, increasing brand visibility and occasionally used to manage service failure. Influenced by data science, organisations have access to a wide variety of customer data they can slice and dice and monetise. Organisations see marketing as a way of building relationship with their customers or customer relationship management where visibility is important (Kushwaha et al., 2020). When a customer takes up a product or service, organisations aim to keep that customer for life (Osterwalder et al., 2014). Organisations invest in customer lifetime value (Fader, 2020). The relationship is two-way, with customer spending their money and the organisation providing the service customer paid for to generate profit (this is for profit organisation). If the relationship is not working either the customer or the organisation will exit the relationship (Sashi, 2012). Looking at it from the customer’s side, if they are promised a service but service provided is substandard or not met, customer will seek similar service from competitors (Fader, 2020). The extensive use of data science for advertising and marketing should be extended to customer communication when there is service failure to improve the relationship between organisations and their customers. 2.3 Customer communication during service failure 2.3.1 How should organisations organise themselves for agile communication Organisations used to decide when and how they would like to engage their customers. The rise of digital media has, however, driven organisations to relook their process as customers have become co-creators of their value through interactions and positive word of mouth. If customer is not satisfied with a service from an organisation, the converse applies where they become a destroyer of value, brand, and reputation (Zhang et al., 2018). Digital media communication is instant and reaches a wider audience including those that may not be impacted by the communication at the time but could have interest in the organisation in future. Digital communication leaves permanent footprint and accessible to all including those who may want to enhance their purchasing decision process (Zhang et al., 2018). The agility of social media means information spreads instantly with or without the organisation in question aiding the discussion. In the absence of alternative narrative, social media users repeat information within their reach irrespective of the validity of the information. The reach makes it difficult for organisation to consider pursuing the defamation lawsuit. Either way, the damage would have been done (Bergh, 2019). To manage the social media narrative, there is a need for organisations to align themselves with the evolution of technology, which has consequently led their customers adapting their engagement need to instant information. The need for instant communication needs to be serviced and the information gap is best filled by the organisations themselves (Coombs, 2020). When there is service failure be it planned or unplanned, organisation would know about this before their customers do. Organisations that are agile set themselves up for a faster communication and not only to communicate service failure but service recovery as well. This way they become the leaders of the discussion and manage responses based on what they have shared with their customers and not what customers think might have transpired (del Río-Lanza et al., 2009). 2.3.2 Forms of customer communication Organisations use technology to facilitate communication between themselves and their customers (Lin, 2003). Communication technology has over the years evolved from pigeon to now digital media. Organisations have over the years tried to keep up with the different communication mediums. Figure 2: Communication Technology Evolution Source: (Liew et al., 2014) The communication technology evolution in figure 1 is based on data collected in Malaysia, however, the migration is similar globally. The study by Liew, Vaithilingam, & Nair (2014) shows that Facebook which is the media of the day is generally used for personal use with users looking to socialise or entertainment. 97% of the respondents surveyed showed they joined Facebook to socialise with family and friends, entertainment or to have fun. This study was supported by studies done by other researchers including a study done by Kowal et al. (2020), that included 46 countries and 60 authors, which concluded that most of the respondents visit Facebook to connect to friends and family. This means customers who visit social media are not necessarily trying to get in touch with organisations that provide them services or actively trying to find out what organisations are saying to their customers. They visit social media for personal use. When organisations are faced with a crisis, they are often faced with time pressure and information overload having to deal with a crisis and decide next steps like sending notifications to their customers. Organisations that may choose to communicate struggle to decide the on the effective medium (Claeys & Coombs, 2020). Organisations that may choose to communicate through SMS or eMail face the limitations of having to first identify impacted customers and depend on the quality of the contact details at their disposal, to reach their customers. Public organisations like electricity and water suppliers face the challenge of magnitude where they service the entire country. Use of email and SMS is not economical (Reason et al., 2015). The other challenge with SMS is the character limitation that organisations may be restricted to, limiting the content they may want to send out (Kisteneva et al., 2020). eMail also create a challenge of phishing, which has led organisations limiting its usage as a communication medium (Moore & Clayton, 2007). Organisations that may choose to communicate through social media risk their messages not being noticed as people visit social media sites for social and entertainment purposes, not necessarily to find out what organisations are saying (Liew et al., 2014). The other challenge is social media users have multiple products that it may not be practical for them to search their service providers one by one searching for what their providers are saying. A single platform that can be used by both private and public sector will solve the issue of clutter faced by other social media platforms and the issue of cost faced by communication through eMail and SMS. Studies have been done on the use of social media by organisations to communicate to their customers but there isn’t enough data on the use of a central shared communication platform that only focuses on organisations communicating to their customers in an event of planned or unplanned service interruptions. 2.4 Platform business structure In the past ten years there has been exponential growth in platform business model with the likes of Uber, Air BnB, PayPay and Alipay being the most familiar to consumers all over the world. Platform businesses have disrupted some of the pipeline businesses like hotels that have been disrupted by Air BnB and private taxis that have been disrupted by Uber (Mody et al., 2020). The advantage of a platform business is that it does not necessarily need to own assets. For example, Amazon provides a platform for buyers and sellers to meet and exchange value. Amazon does not own a warehouse for the dispatch of goods. The assets are owned by the seller, who comes to the platform to meet potential buyers and buyers pay the delivery fee. Platform business consists of two-sided market and reliant on network effect with a strong ecosystem. Platform allows more than one stakeholder to participate where the supplier of service or product targets a particular consumer. Suppliers and consumers meet on the platform and exchange value (Kim, Platform adoption factors in the internet industry, 2018). This value could be content sharing and consumption or selling and buying of goods and services. Figure 3: Platform Business Structure Source: (Kim, Platform adoption factors in the internet industry, 2018) The platform provider facilitates the transactions between the supplier and consumer and charges a facilitation fee (Fu et al., 2021). Platform providers should build strong relationships with their stakeholders and understand their needs for them to continue providing content to the platform (Kim, 2014). Businesses that own platforms like Uber, AliPay, Paypal and Apple, have seen growth and high profits in past few years due to network effects. The more people use the platform, the more users find it valuable and willing to pay usage fee (Gregory et al., 2021). A platform business like Pay Pal has seen its number of transaction doubling between 2017 and 2020. Similar growth was seen with the Chinese platform business Alipay, which doubled number of users from 451 million to 900 million, between 2016 and 2019 (Rehncrona, 2022). Organisations that leverage platforms have seen the cost of transaction reduce while they have access to more consumers. This is because the platform functions as intermediary between the seller and the buyer who utilise the same platform to do business (Mody et al., 2020). Organisations that choose to continue to communicate using traditional methods like SMS and eMail risk being perceived as they do not value their customers and the need for agile communication when they cannot meet the agreed service level. Traditional communication method is also expensive, leading organisations passing the cost to the customer increasing the prices of the products they offer (Sebastian, et al., 2020). This may lead customers to switch their businesses to low-cost offerings. The role of the platform owner is to convince more consumers to join the platform, so organisations may derive value from the utilisation of the platform (Rehncrona, 2022). For example, for Uber to convince a driver to sign up and pay a fee, they must prove that the driver will receive customers who need transport and will use the service. Network effects alone may not be enough to keep users interested. To ensure users stay on the platform, the platform must be attractive to all users by providing quality content or goods and services (Hagiu & Rothman, 2016). Ali et al. (2020), critically evaluate businesses that may benefit from the network effect by quantifying network effects, which assists the platform owner in deciding which service provider to promote. Tucker (2019) analyses the negative impact of network effects where the technological platforms with high number of customers may suffer from customer data compromise and the impact it may have on the user trust. This means that though network effect has a positive impact on business revenue, it provides a higher impact when consumer data is compromised, leading consumers to have trust issues with technological platforms. Network effects are dependent on the decisions users make to adopt the platform. The platform owner is responsible for bringing the users on the two sides of the market together, therefore responsible for making sure the platform is adoptable (Belleflamme & Peitz, 2018). 2.5 Platform user adoption 2.5.1 Factors influencing new technology adoption The success of a platform is dependent on adoption by users (Rehncrona, 2022). Xie et al. (2021) identified social influence, facilitating conditions, perceived value and perceived risk as factors that influence customer’s intention to adopt a platform. Liang et al. (2021) highlighted transaction costs and perceived benefits as the drivers of perceived value. A study by Chen & Aklikokou (2020) identified perceived usefulness of the technology and perceived ease of use as critical factors that influence consumer adoption of the new technology. The study concluded that perceived usefulness and perceived ease of use integrate other factors like social influence, trust and other variables that lead to the outcome of intention to use. Most authors who have done studies on adoption of new technology or technological innovation acknowledge the social makeup of the usership influences the acceptance of new technology. A study that was done in Benin by Su (2013) analysed computer literacy and access to internet as a potential impediment to the development and adoption of technology in the country. When COVID-19 hit, South Africa was faced with a challenge of the need for learners to learn at home, and an article written by Hanekom (2020), who is a life sciences facilitator and ICT coordinator at the Centre for Pedagogy at Stellenbosch University, revealed only 37% of households have consistent access to internet. The cost of data was stated as one of the causes of the limited access to the internet. Study by Lin (2003) supports user cost being an impediment to technology access. A study conducted by Hamari et al. (2020) on freemium services concluded that the more users enjoy the service, the less willing they are to pay for it, but will highly likely adopt it. Example of this is social media like Twitter and Facebook who may likely lose usership if they introduce usership fee. Users, however, place social value high and are willing to pay for it when they have exhausted their free passes. Examples would be gamers who are willing to pay for the service. Jiang et al. (2021) found that consumer reviews, reputation of the developers and peer influence play a role in influencing perceived value and subsequently affect users intention to adopt the application. The implication of this is that platform owners should make the application easy to discover and should be differentiable from other applications. According to Zhu et al. (2017), study results indicated self-efficacy, social value, functional value, and emotional as key factors influencing perceived value and therefore intention to adopt the application. Implication is that platform owner should make the application easy to use without requiring a manual. On perceived usefulness, a study conducted by Kim (2018) focused on speed of finding content and the quality of the content with respondent showing that when users perceive the platform to be providing good quality information that is quick to find they are highly likely to use it. The study also focused on perceived ease of use with ease of finding information, ease of platform use, and ease of explaining the use of platform to other users with responses not showing significant relationship between perceived ease of use and adoption intention. However, a study conducted by Kamal et al. (2020) on telemedicine concluded that perceived ease of use is a critical factor to adoption. The study was based on rural patience. Another study conducted in India by Sharma (2020) concluded that users of telecommunication perceive the use of technology to acquire information being the most important, therefore meeting their needs. This means when the application meet consumer needs is considered useful. Platform users are also concerned with application security and privacy, therefore their perception on these items could hinder adoption. The study by Kim (2018) has explored user perception on security focusing on leaking of personal or transaction information and system hacks or virus attacks. The study concluded that if people perceive a system to be lacking security, they are highly likely not to adopt it. This outcome was supported by the telemedicine study conducted by Kamal et al. (2020). Security and privacy issues contribute critically on trust, where if users do not trust the system, they would highly likely not adopt it. Facilitating conditions focus on ability for organisation’s technological infrastructure to support user adoption of new technology. The success of adoption of communication application would be dependent on the presence of adequate technological infrastructure (Kamal et al., 2020). A study done by AlHogail (2018) identified trust as the driver of technological adoption. Factors were grouped into quantifiable and non-quantifiable factors which are social related factors, product related factors and security related factors with each factor having sub-factors that drive the behaviour. If a user trusts that product related factors and security related factors will perform as perceived, they are highly lucky to adopt a new technology. There is extensive research on factors impacting adoption of new technology or application, but the research is limited to other industries and focus on services like communication is lacking. Sharma (2020) also indicated for future consideration, ‘intention to adopt a new technology’ study could be expanded to sectors such as services sector. Studies that impact user adoption are also limited in South Africa. Most studies conducted have been done in the Asia region. This research will build on already existing theories and models of technological adoption, which will be expanded to service failure communication application focusing on ranking factors that influence new technological adoption. 2.5.2 Ranking of factors influencing new technology adoption Building a new technology while navigating resource constraints requires an organisation to do trade-offs and put more resources on factors that users consider more important. A study done by Luo et al. (2013) on technology adoption trade-offs ranked perceived usefulness number one and risk was ranked last at number eight. This study was generic, not specific technology focused. This study was supported by Ehrari et al. (2020) whose study concluded that perceived benefits of technology outweigh perceived risk. Literature on the ranking of factors where factors are measured against each other is limited. It is from this limitation that this research will be conducted to fill the gap. The research will leverage models that are already existence and build on those. 2.6 Hypothesis 2.6.1 Research Model Figure 4: Proposed Research Model for Adoption Source: (AlHogail, 2018) 2.6.2 Hypothesis Factor Hypothesis Usage (H0) H0a: There is a positive relationship between number of Apps downloaded and integrated App usage H0b: There is a negative relationship between number of Apps downloaded and integrated App usage Product related factors (H1) H1a: functionality and reliability are the most important factors for the communication app. H1b: helpfulness is the most important factor for the communication app. H1c: Ease of use is the most important factor for the communication app. H1d: perceived usefulness use is the most important factor for the communication app. Social influence related factors (H2) H2a: Intention to use communication app will be positively influenced by interest in trying out new technology. H2b: Intention to use communication app will be positively influenced by social network. Security related factors (H3) H3a: product security is the most important factor for the communication app H3b: perceived risk is the most important factor for the communication app Table 1: Description of hypothesis for the proposed research model for adoption Source: (AlHogail, 2018) 2.7 Summary Organisation that provides goods and services has an inherent risk of unplanned and possibly unforeseen service failure or planned interrupted service for maintenance or service upgrade. The growth of social media and search engines facilitated by the internet revolution has created new customer expectations where customer expects agile communication from their service provider. This dilemma was worsened by the introduction of ‘Treating Customer Fairly (TCF)’ by the Financial Services Conduct Authority (FSCA), which TCF Outcome 3 states that “Customers are given clear information and are kept appropriately informed before, during and after the time of contracting”. When organisations are faced with a crisis, they are often faced with time pressure and information overload having to deal with a crisis and decide next steps like sending notifications to their customers. Literature was interrogated to see if there is a solution to this problem. A single platform that can be used by both private and public sector was identified as the solution. Platform business consists of two-sided market and reliant on network effect with a strong ecosystem. The platform provider facilitates the transactions between the supplier and consumer and charges a facilitation fee. Network effects are dependent on the decisions users make to adopt the platform. These decisions include facilitating conditions, perceived value, perceived risk and social influence. 3 CHATPER 3. RESEARCH METHODOLOGY 3.1 Introduction Data collection is a process of gathering information, which is then measured to determine answers to questions the researcher may be interested in. The data collection leads the collector to new information that would not necessarily be discovered (Lalehzari, 2021). This chapter focuses on the research methodology followed to answer the research objectives and the hypothesis listed in the previous chapter. Research approach and design will be outlined as well as data collection method and instrument. Population and sample size as well as data analysis and limitations will be discussed. Approach to ensure validity and reliability of the data will also be discussed. In conclusion the ethical question around the research will be addressed as well as the cost of conducting the research. 3.2 Research approach The aim of this study was to answer the question if there is a need for an integrated communication application that can be used by multiple organisations to communicate to their consumers when they are faced with service failure or recovery. Though the application can be expanded to other use cases like advertisements by organisation, the immediate use case to be fulfilled was communication during service failure. Jayarathna & Hettige (2013) argued for a need to have an integrated communication platform, though their study was limited to agriculture sector. Jayarathna & Hettige (2013) conducted quantitative research to test system user satisfaction focusing on the usefulness of the system and efficiency. The study wanted to test how the system should be designed to meet the needs of the users. This study will also conduct quantitative research to determine factors that users consider important for the adoption of a new communication mobile application system. This is to ensure factors with high ranking are given high priority when developing the new communication mobile application, taking into consideration resource constraints. Quantitative method was chosen considering the time constraint and the size of the sample to be consulted, which aimed to reach many potential App users. The use of quantitative method during time constraint and larger sample is supported by Rahman (2020). The other advantage of quantitative data collection method is that it allows for randomisation of respondents (Faems, 2020). A survey was used to investigate the need for a communication hub and the information users of the platform would like to see in the communication App. System factors like ease of use and perceived usefulness together with security and privacy were queried to determine factors that may give App adoption uplift and factors that may hinder the adoption. This was to find out how best to position the platform for adoption. According to Groves, et al. (2011), surveys have been used for the past 60-80 years with the purpose of gathering information from a sample to construct quantitative descriptors that represent a larger population. Surveys can be used to analyse the whole population, however, for this study only a sample will be measured. Surveys are widely used to test theories of behaviours. In surveys, information is gathered from a subset of a population by asking a selected number of people questions, which answers aim to represent the entire population. This study focused on gathering information from a sample of the population, which will be used to represent the whole population. 3.3 Research design The aim of this study was to determine if there is a need for integrated communication platform and the characteristics that influence a user to adopt a new application (system). To answer this, descriptive study designs will be used. According to Omair (2015), descriptive study has ability to generalise a representative sample finding into a larger target population. Descriptive design does not seek to compare a sample with another group. Quantitative method with descriptive design is dependent on numbers and calculations to derive an answer. It involves the calculation of mean, median, mode, correlation etc. Quantitative method produces numbers to arrive at a conclusion (Lalehzari, 2021). According to Groves, et al (2011), descriptive statistics are used to measure the spread and size of various attributes in a population and how some attributes are related by measuring how the movement of one attribute impacts the movement of the related attribute. Kaur, Stoltzfus, & Yellapu (2018), argued that descriptive statistics describe the relationship between variables in a sample population. Data is condensed into a summary to allow for a specific population to be manageable (Sarka, 2021). Descriptive statistics makes the presentation of data easier to understand and helps to identify patterns (Groves, et al., 2011).Descriptive design was chosen for this study because it fits the outcome of the research, which is to determine characteristics of the system that are important to the users of the system in a manageable manner. 3.4 Data collection method and instrument Primary data was collected from respondents using a survey where respondents were asked if they would use an integrated mobile application dedicated to communication when their service providers have service failure and when the service has recovered. Respondents were also asked to rank mobile application factors by order of importance to measure factors that must be prioritised when the mobile application is developed. Respondents were sent a questionnaire through social media and Wits university student portal with pre-populated options. Questionnaire is classified under primary data collection as it allows for data to be collected from the primary source (the respondent) (Hox & Boeije, 2005). Questionnaire is customisable and allows for a wider reach with little effort. The use of online questionnaire has reduced the cost of data collection as there is no longer a need to go onsite and do the capturing after the collection (Lalehzari, 2021). This method is chosen considering the time constraint and the size of the sample to be consulted, which aims to have a larger reach. The use of quantitative during these conditions is supported by Rahman (2017). The other advantage of quantitative data collection method is that it allows for randomisation ( (Faems, 2020). Since quantitative survey is now conducted online, there is risk of the method being abused where the researcher can write computer algorism to generate responses (Moises Jr, 2020). To mitigate this risk, Qualtrics which is a reputable survey tool endorsed by Wits University was used to collect data for this study, to ensure the integrity of data collected is maintained. Respondents were only given one opportunity to complete the survey to ensure data is only collected once from a respondent. 3.5 Population and sample 3.5.1 Population Population is a whole group the researcher is trying to find answers from to draw a conclusion (Nardi, 2018). The population for this study is smartphone and web browser users from around the world. The survey was shared via social media (Facebook, Linkedin and WhatsApp) and Wits students via Wits email distribution list. Though the survey was shared online with people from different countries, the assumption is that majority of the respondents are from South Africa. The use of online tools like Qualtrics survey tool to conduct survey is the new normal, more especially since the Covid-19 pandemic started, which prevented face to face meetings (Moises Jr, 2020). 3.5.1 Sampling method and size Sampling method Sample is a subset of a population which is chosen to represent the larger population since due to time, cost, and resource constraints, it is difficult to consult the whole population for a conclusion. Sampling methodology, sample size and response rate have an impact of the representativeness of the sample (Acharya, et al, 2013). Using probability sampling, the researcher can generalise the finding from a sample to reflect the views of the target population. Probability sampling gives everyone in the population an opportunity of being selected for a study to represent the population (Singh, 2003). Probability sampling requires minimal knowledge of the population, and data is easy to analyse with high internal and external validity (Acharya, et al, 2013). Probability sampling has further classification which includes multiphase sampling (forms part of cluster sampling) that allows the researcher to collect information from a whole sample and another part from a sub-sample (Etikan & Bala, 2017). For example, only users that select ‘yes’ in the previous question are asked the next set of questions. This sampling method is purposeful and increases response rate (Acharya, et al, 2013). Highlighted in red below is the visual explanation of the sampling method used for this study. Figure 5: Sampling Techniques Diagram Source: (Mmbengwa, et al, 2021) This study investigates the need for integrated communication application for organisations. If a respondent selects ‘yes’ or ‘maybe’ indicating there is a need for a single service failure communication App, they are then asked to rank the features of the application to ensure the important characteristics of the application are prioritised first. If the respondent selects ‘no’, they are not asked further questions since they are not seen as the target market. Respondents are first asked number of years of using a smartphone before being presented with a question that asks about a need for a single communication App. If the respondent has never owned a smartphone, they were not asked the next questions as the target market is only smartphone users. This method of sampling is called probability multistage sampling. Sample size Data was collected from a total of 717 respondents. 139 of the 717 respondents started the survey but never completed the survey, therefore incomplete data was deleted, and only 582 responses were analysed. 576 indicated they have used a smartphone before, therefore could continue to the next question. When asked if there is a need for an integrated communication App, 522 respondents indicated there is a need and therefore could continue to answer communication App related questions. 3.6 Data collection process 3.6.1 Introduction Data collection is a process of getting information from the respondents to measure variables that answer the research question and test hypotheses. Information on variables of interest is gathered and measured in a systematic manner to evaluate outcomes (Granello & Wheaton, 2004). Below diagram shows end to end data collection process. Figure 6: Planning and Collection Process for Primary Data Collection Techniques Source: (Aljohani & Thompson, 2020) Data collection process for this study ensured each question is responded to before the respondent could proceed to the next question. This was to ensure high data quality where the respondent might skip a question rushing to finish the survey or skip if they find a question difficult without first applying themselves. 3.6.2 Data collected Sample size and demographics To test hypothesis H0, it was determined that to establish if there is a need for an integrated communication application respondents who use smartphones will have to be interviewed as they are the target market of the finished product. If they agree there is a need, they will then be asked to rank application features to ensure their needs are catered for in order of importance taking into consideration resource constraints when building a new system application. Respondents were first asked how long they have been using smartphone as per below. If a respondent selects that they have never used a smartphone the survey ends as the respondent is not the target market. Of the 582 respondents that answered this question, 576 respondents were able to continue with the survey as they use a smartphone. Respondents were then asked to provide demographic information. Demographic information collected was age, gender, and education. Demographic information was collected to determine if people of the same age group or gender or level of education have the same needs. This was to check if responses are influenced by demographic information. Hypothesis H0 – App usage Respondents were asked number of Apps they have downloaded. This is number of Apps that did not come embedded on the phone. The assumption is that high number of Apps indicate that a respondent generally likes trying out new Apps and low number of Apps indicate the respondent would appreciate an integrated App where they do not have to download too many Apps to find out what their service providers are saying. Respondents were also asked the types of Apps they mostly use to determine the kind of Apps they are interested in and understand the kind of Apps the integrated App will be competing with for attention. Four Apps from the previous question were then selected for respondents to rank in order of usage. This was to determine which Apps are used the most daily. This question was designed to help decide how best to position the new integrated communication App. Majority of respondents (58%) indicated they mainly use social media Apps, therefore even though this is a service App, it will be positioned to potential users as a social media App with service failure information, to give it a competitive advantage. Considering the many Apps users must consult to find out why they are unable to access services from their service providers, respondents were asked if there is a need for an integrated App that notifies them when their service provider has service failure. If respondent selects ‘no’ the survey ends as the respondent is not the target market. Of the 576 respondents that answered this question, 522 respondents were able to continue with the survey as they have responded with a yes (372) there is a need for an integrated communication App, or they have responded that maybe (150) there is a need. Hypothesis H1 – Product related factors Respondents were asked to rank characteristics of the App they consider important. Respondents had to rank 6 App characteristics with 1 being the highest and 6 being the lowest. These 6 characteristics are grouped under product features and when the App is developed resources will be prioritised to the highest ranked features. Options included were that the App must work; the App must be reliable; the App must be helpful; the App must be easy to use; the App must be useful; and the App must be free of charge to the end user. Respondents could only select a number once to ensure they do not repeat the same ranking on other option. This means true ranking was applied. Hypothesis H2 – Security related factors Respondents were also asked to rank security related features to understand between protection against virus and to have their data protected, which they consider more important. This was to measure where resources are needed the most for user protection. Both factors will, however, be given high priority. Protection of financial details is one of the features that could have been added under security ranking, however, this App will not be holding end user financial data. This can be considered at a later stage when users are already entrenched in the system. Hypothesis H3 – Social related factors Lastly, respondents were asked social related questions to check if they are people who influence others to use a new App or if they are influenced by family and friends or influencers. This question is to help with marketing positioning of the new communication App. This will also help determine the cost of marketing depending on approach. 3.6.3 Pilot study Pilot study allows for the researcher to collect data at a small scale to determine if the study will yield the intended results (Malmqvist, et al, 2019). The questionnaire was shared with 5 respondents to test the viability of the study. The questionnaire was then refined before sharing with the rest of the group. However, statistical testing was not done during the pilot study. The pilot was done to ensure respondents can understand the questions. 3.6.4 Data collection tools and timeframe Respondents were sent Qualtrics questionnaire through social media (Facebook, LinkedIn, and WhatsApp) and Wits university student portal with pre-populated options. Respondents were given a month to complete the survey. If a respondent starts the survey but does not complete it, records were deleted after a week of incompletion. 3.7 Data analysis 3.7.1 Introduction Data analysis is process of cleaning, transforming, and modelling the information collected to support the decision-making process and suggest conclusion. There are different types of data analysis which include exploratory data analysis (EDA), descriptive statistics and confirmatory data analysis (CDA) (Pal, 2017). According to Ho (2006) there are two primary considerations to be made when choosing the appropriate statistical test, which are “(1) the nature of the hypothesis and (2) the levels of measurement of the variables to be tested”. A determination should be made if the intention of the hypothesis is to measure the mean score differences between groups or testing relationships. Depending on the aim of the research, the appropriate statistical test must be selected. Level of measurements must also be considered together with nature of the hypothesis to be tested (Ho, 2006). This study has adopted descriptive statistics with frequency distribution. 3.7.2 Data analysis technique Descriptive analysis allows data to be presented in a quantitative form that is easily accessible, and the rationale associated with the quantification is established. Observations are converted into numerical figures (Pal, 2017). Raw data collected was exported from Qualtrics to Excel spreadsheet where summaries and charts were created to give a visual output of the data to answer the research question. Correlations and scale reliability were also used to analyse the data for viability and reliability. 3.7.3 Data analysis tools Statistical Package for the Social Sciences (SPSS) data analysis tool was used to run statistical procedures. SPSS has been in existence since 1968 and widely used in educational research. Though software like Microsoft Excel can be used to run some statistical data, SPSS has ability to run complex data analysis, which makes it a preferred tool. 3.8 Limitations In quantitative studies, surveys tend to be the most preferred due to their low- cost nature and high representativeness of the population. However, the reliability of the study is dependent on the structure of the questions and the accuracy of the responses given by the respondents (Queirós et al., 2017). The concern of the representativeness was also raised by Hox & Boieje (2005). For this study, sample questions were tested with potential respondents before distributing to larger respondents. Questions were then refined to remove ambiguity. The tool used to construct questions, however, had limitations in terms of how some questions could be structured to ensure the right responses are collected. Some questions could not be structured correctly due to the limitation of the tool used, which increased number of incomplete responses. Incomplete responses make up 19% of the responses. Though the survey was considered difficult to complete by some of the respondents, conclusion could not be drawn that the entire 19% dropout is due to the level of difficulty of the of the survey. 3.9 Validity and reliability 3.9.1 Validity Quantitative research using surveys is vulnerable to errors where the questions asked may lead to the deviation from the desired outcome of the survey. Type of questions asked, who answers the questions and how the data is collected can compromise the quality of the results (Groves, et al., 2011). Heale and Twycross (2015) define validity as the accuracy of the outcome of the quantitative study. They divide validity into three categories, which are, content validity that measures the instrument if it covers all the content of the variable; construct validity that checks if inferences can be drawn from the concept being studied; and criterion validity which is an instrument that measures same variable and checks the extent of the relationship between measures derived from the survey and other external criteria which external criteria can be concurrent or predictive. This study measured criterion validity with the focus on concurrent validity. Known-group validity was used to test the categorical external criteria. 3.9.1 Reliability According to Heale and Twycross (2015), reliability is the accuracy of the instrument used in a quantitative study to determine the outcome of the study. If the instrument is used consistently in the same situation repeatedly, it is expected to produce same results for it to be considered reliable. Heale and Twycross (2015) divide reliability into three categories, which are homogeneity, stability, and equivalence. Homogeneity is assessed by checking if one construct can be measured using all items in a scale. It is recommended that Cronbach's α is used to determine consistency of the instrument. If the Cronbach's score is 0.7 and above, then the instrument is considered reliable. The source of acceptability of Cronbach's score of 0.7 and above is the original study done by Cronbach in 1951. This study adopted Cronbach's score of 0.7. Figure 7: Cronbach's Alpha Rule of Thumb Source: (Chan, 2020) Stability is determined by doing repeated testing and check if the results generated are consistent. An instrument that is stable has a high correlation between scores each time the test is completed. An instrument that is considered stable has a correlation coefficient of 0.5. Correlation of 0.3 to 0.5 is considered moderate and anything less than 0.3 is considered weak. 3.10 Research ethics Respondents took a survey online and were not requested to disclose personal information and responses cannot be tied to a particular individual. Explanation was given that confidentially will be maintained and anonymity will be retained in the data. There are no additional researchers that conducted or have access to view the data. No privileged information was requested from the respondents. Sensitive information or information that falls within vulnerable category was not requested from the respondents. No formal permission was required, and the research was conducted online through online questionnaire. Only demographic information like gender, age and income was collected to determine the target market. Digitally collected and stored data is easy to access for future, however, the storage of the data should be done in compliance with ethical requirements reference (Golinelli, et al., 2020). Data collected for this study will be stored in a password protected computer and on online database that is accessed using a password. The password for the computer and the database changes every three months. Data will be purged after five years from the online database. 3.11 Summary The aim of this study was to answer the question if there is a need for an integrated communication application that can be used by multiple organisations to communicate to their consumers when they are faced with service failure or recovery. Quantitative method was chosen considering the time constraint and the size of the sample to be consulted, which aimed to reach many potential App users. A survey was used to investigate the need for a communication hub and the information users of the platform would like to see in the communication App. Data was collected from a total of 717 respondents and 522 respondents indicated there is a need for a communication hub. 4 CHAPTER 4. PRESENTATION OF FINDINGS 4.1 Introduction The aim of this study was to determine if there is a need for integrated communication platform and the characteristics that influence a user to adopt a new application (system). To answer this, respondents were asked a set of questions, which the responses will be presented in this chapter. Hypothesis were made before data was collected. Analysis of the data will reveal if the hypothesises were met or not. Statistical test was also performed to determine validity and reliability of the study, which results of the test will also be presented in this chapter. The study attracted over 700 respondents whom only 582 completed the survey. The rest started the survey but never completed, therefore the responses were discarded including those that were already 95% complete. Some respondents indicated the survey was difficult on questions that required a ranking scale. This feedback was used as a lesson that firstly, technology users do not necessarily read instructions and secondly, when one is building a technological system, they must consider the lowest common user to make the technology easy to use, which increases adoption. Technology adoption is something this study is trying to solve for; therefore, the difficulty of the survey was considered in the outcome of the investigation. 4.2 Sample size and demographics The interest of this study was on respondents that use smartphones. To determine smartphone usage, respondents were asked how long they have been using a smartphone. Results below show the number of respondents that have used a smartphone who could be allowed to continue with the survey and form part of the study. Where a respondent selected that they have never used a smartphone the survey ended as the respondent was not the target market. Of the 582 respondents that answered this question, 575 (99%) respondents were able to continue with the survey as they have used a smartphone before. The 7 (1%) respondents who have never owned a smartphone were dropped from the sample. To see if people of the same age group or gender or level of education have the same needs, respondents were grouped into demographics, which was age, gender, and education. Below is a view of gender, age and level education combined to form a matrix. The demographic information below shows the type of people who completed the survey. Majority (30%) of those who completed the survey were between the age of 18 and 24, which majority of the respondents (55%) were female and had only a high school qualification (25%) or honours/postgraduate qualification (25%). The reason for majority of respondents being young (18-24) with no post high school qualification or respondents between ages of 35 and 44 (29%) is because some data was collected through the Wits university distribution list with only students targeted. This group of respondents was followed by those with post graduate qualification for similar reasons. This skewness of respondents was not seen as a concern because young people tend to adopt technology faster and get the adults in the family to also adopt as well. Responses below are 565 which is less than the question that follows, because initially demographic questions were at the end, then moved to the top to accommodate respondents that would be dropped off the survey earlier depending on their responses. Demographic was combined where age, gender and education level were grouped together to show full demographic of a respondent. Majority of the respondents (15%) are females between the age of 18 and 24 who are high school graduates followed by high school graduate males of the same age at 7%. The gap between male respondents and female respondents of the same age and qualification is almost double with females leading. Similar observation was made in the next age group set where more females responded to the survey and majority either had a 4-year degree or honours/postgraduate. As the ages progressed, more males than females completed the survey and tended to have higher qualifications like master’s and doctorate than females. Categories Items Frequency Percentage Gender Male 252 44.68% Female 308 54.61% Non-binary / third gender 2 0.35% Prefer not to say 2 0.35% Age 18-24 170 30.14% 25-34 142 25.18% 35-44 166 29.43% 45-54 66 11.70% 55-64 19 3.37% 65+ 1 0.18% Education Less than High School 0 0.00% High school graduate 143 25.35% College graduate with Certificate 24 4.26% Diploma Graduate 36 6.38% 3-year degree 84 14.89% 4-year degree 49 8.69% Honours/Postgraduate 142 25.18% Master’s degree 74 13.12% Doctorate 12 2.13% Mobile Experience Never owned one 1.20% 7 1-3 years 3.61% 21 4-7 years 17.35% 101 7-10 years 21.13% 123 10+ years 56.70% 330 Table 2: Demographic and mobile experience 4.3 Need for an integrated communication application Respondents were asked number of Apps they have downloaded, and majority (52%) indicated they have downloaded between 10 and 20 Apps. The finding below indicates people generally download lower number of Apps. Table 3: Number of Apps downloaded Respondents were asked the types of Apps they mostly use and were allowed to select multiple Apps. The most selected Apps were social networking Apps and service Apps with 16% and 15% respectively, then transport at 12%. Table 4: App type preference The above Apps were then trimmed to allow respondents to rank them in order of usage to determine which Apps respondents use daily. The four Apps chosen were randomly selected with the assumption that respondents will rank them highest in the previous question. Majority of respondents (58%) indicated they mainly use social media Apps followed by services App. # Answer % Count 1 Social networking (Facebook, Twitter, WhatsApp, etc) 15.90% 551 2 Services I consume (Banking, telecoms, water, electricity, etc) 14.72% 510 3 Entertainment (TV, radio, movies, etc) 10.53% 365 4 Games 7.22% 250 5 News 7.27% 252 6 Sports 4.13% 143 7 Educational 7.82% 271 8 Spiritual 6.87% 238 9 Food (Checkers, Pick n Pay, Mr D, UberEats, etc) 10.16% 352 10 Transport (Uber, Bolt, etc) 12.35% 428 11 Other 3.03% 105 Total 100% 3465 App preference - What type of Apps do you mostly have? You can select multiple Table 5: App type ranking Respondents were asked - If there is one App (service failure communication App) that combines all the services (Banking, telecoms, water, electricity, etc), to notify them when their service provider is having service failure, would they use it. 65% of respondents answered ‘yes’ they would use it and 26% answered ‘maybe’ they would use it. That made a total of 91% respondents that would probably use it. The 9% that answered ‘no’ they would not use it, were not presented with the next set of questions as they were not considered target market. Table 6: Communication App need 4.4 Factors influencing application adoption 4.4.1 Product related factors Respondents were asked to rank characteristics of the App they consider important. Respondents had to rank 6 App characteristics with 1 being the highest and 6 being the lowest. These 6 characteristics are grouped under product features. The characteristics included ‘work’, which meant the user requires the App to do what it is intended to do; ‘be reliable’ which means the App should be available all the time when expected to be available; ‘helpful’ which means the App should provide information that adds value to the users; ‘be easy to use’ which means the App must not be too complex and should be easy to navigate without the need of a manual; ‘be useful’ meaning the App must provide information that is useful to the users; and ‘free of charge’ which means users must not be charged to access the App. Free of charge had the highest ranking at number 1 with 25.91% followed my reliability at number 2 with 25.72%; then helpful followed at ranking number 3 with 23% which was then followed by easy to use at 20% and usefulness at number 5 with 24%. Free of charge seems to either be very important or not important at all as it scored highest on ranking 1 and highest on ranking 6. This means respondents felt strongly about being charged. Option 1 (work) ranked high on ranking 1 but free of charge ranked the highest in the same ranking, therefore that the App must work could not be said to have ranked the highest on ranking one though ranking 1 is option 1 highest ranking. Option one could not rank highest on other options either, therefore it would be placed as the last ranking 6 though ranking 6 is the lowest ranking of option 1 (work). It could be said respondents may have interpreted option 1 and option 2 as fulfilling the same need, therefore their raking is closely related in each ranking. Table 7: App characteristics ranking 4.4.2 Security related factors Security related questions were asked to the respondents where they had an option of protection against viruses or to have their data protected. 87% of the respondents chose to have their personal details protected showing that though people do not mind being online, they prefer anonymity. Table 8: App security characteristics ranking 4.4.3 Social related factors The last question of the survey dealt with social related questions where respondents were asked how they get to know about a new technology or application if they are the first ones to discover it and share with others; if they hear about a technology from family and friends and are highly likely to take advise from family and friends or if they listen to celebrities to use a new technology. This was to determine if respondents are people who influence others to use a new a new application or if they are influenced by family and friends or influencers. 42% in the first ranking indicated they discover the technology and tell others about it while in ranking 2 41% of the respondents indicated they would follow their friends and family, and lastly respondents indicated they would listen to celebrities and influencers. Table 9: Social characteristics ranking 4.5 Statistical testing 4.5.1 Validity and Reliability Internal reliability integrity scale was investigated using Cronbach's alpha. The results indicated that the alpha for the total scale on 7 items was equal to .501 which is considered poor as the acceptable result is 0.7 and higher. A total of 584 items were tested and 62, which is 10% were excluded from the analysis as they form part of respondents that could not continue with the survey based on the answers given in the previous question. The low Cronbach's alpha indicates low reliability on the scales used in the questionnaire. Cronbach's alpha can be improved by removing ‘usage ranking’ scale which improves it to 0.527. This result indicates that items are poorly homogenous, and they do not comprise exceptional variance to not be similar in form and relations with each other. Table 10: Reliability testing If some of the scales are deleted and only 3 scales, which are ‘mobile experience, ‘age’ and ‘education’ are used, Cronbach's alpha improves to 0.658 with a Cronbach's alpha on standardised items at 0.744. Table 11: Reliability testing – with deleted items Correlation test was also conducted, and it was observed that correlation between number of years respondent has been using a smartphone and number of Apps the respondent has downloaded are significantly correlated with a p- value = 0.01. Similar observation was made between the adoption of communication App and ranking of security features with a p-value = 0.05. This indicates that it is not by chance that number of years of experience drives number of Apps the respondent would download, and communication App adoption drives how the respondent would rank security features. Reliability Statistics Cronbach's Alpha Cronbach's Alpha Based on Standardized Items N of Items .501 .407 7 Reliability Statistics Cronbach's Alpha Cronbach's Alpha Based on Standardized Items N of Items .658 .744 3 Table 12: Correlations (mobile experience, number of Apps, usage ranking, age, education) One-way ANOVA was conducted at alpha level of 0.05 to determine if number of Apps influences new communication App adoption. There was no significance difference between number of Apps downloaded and App adoption with F (5,570) = 5.33, p=0.75, partial η2 = 0.005 and mean of 0.437. This means number of Apps downloaded does not influence communication App adoption. Table 13: ANOVA (number of downloaded Apps and communication App) Regression analysis was conducted testing if age, gender, number of years of mobile usage and number of Apps downloaded influence how respondents rank product features of a new technology, results showed that there was no Correlations Usage Ranking Age Education Mobile experience Pearson Correlation Usage Ranking 1.000 -.062 -.066 -.060 Age -.062 1.000 .559 .467 Education -.066 .559 1.000 .439 Mobile experience -.060 .467 .439 1.000 Number of Apps .025 .107 .137 .166 Sig. (1-tailed) Usage Ranking . .078 .067 .086 Age .078 . .000 .000 Education .067 .000 . .000 Mobile experience .086 .000 .000 . Number of Apps .287 .007 .001 .000 N Usage Ranking 522 522 522 522 Age 522 522 522 522 Education 522 522 522 522 Mobile experience 522 522 522 522 Number of Apps 522 522 522 522 ANOVA Communication App Sum of Squares df Mean Square F Sig. Between Groups 1.166 5 .233 .533 .751 Within Groups 249.271 570 .437 Total 250.437 575 relationship with a regression of 0.657 which is less than 0.7. This means predictors are not multilinear. Predictors are observed to be less than 0.3 which means there is a linear relationship. Residual is within 3 and -3 miscalculating by 0.007 points. Durbin-Watson is 1.981 which is below 2 therefore an indication that there is no auto correlation. The instrument is therefore not considered stable. Model Summaryb Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Change df1 1 .086a .007 .000 .412 .007 .966 4 Model Change Statistics Durbin-Watson df2 Sig. F Change 1 517 .426 1.981 Table 14: Regression analysis 4.6 Summary Over seven hundred (700) respondents took part in the survey. Respondents were asked - If there is one App (service failure communication App) that combines all the services (Banking, telecoms, water, electricity, etc), to notify them when their service provider is having service failure, would they use it. 65% of respondents answered ‘yes’. Respondents were then asked to rank characteristics of the App they consider important as per technology adoption model (TAM) and respondents ranked that the App should ‘work’ the highest. 5 CHAPTER 5. DISCUSSION OF FINDINGS 5.1 Introduction This chapter discusses the research findings in relation to the stated objectives and the hypothesis. Each objective and accompanied hypothesis are analysed. 5.2 Need for an integrated communication application 5.2.1 Introduction This section analyses the need for an integrated communication application dedicated to service failure for organisations. This section compares responses from the survey with the literature around integrated service applications and communication theory. 5.2.2 Integrated App Usage The literature consulted indicates that though organisations use social media to reach out to their clients, most organisations mainly use social media to market their products. In an event of service failure, organisations still largely rely on the use of eMail and SMSes to notify their clients. However, this has proven to be a challenge as it is both costly and not real time. Literature indicated there is a need to communicate to clients when an organisation cannot meet the promised service levels and the communication should be done real time to manage customer perceptions. Literature also indicated that some organisations use social media to communicate service failure, however, since most service consumers use social media to interact with family and friends, social media may not be an effective tool for organisations to rely on for service failure communication. Literature, therefore recommended an integrated application that organisations can use to focus on service failure communication to avoid relying on cluttered platforms like social media or usage of SMSes and eMail that may not be real time. Responding to the need identified in literature, a survey was conducted through questionnaire where respondents were first asked if they have used a smartphone before, number of Apps they have personally downloaded, Apps they use frequently and if they would use an integrated App if it was to be launched. Ninety nine percent (99%) of the respondents who took the survey indicated they have used a smartphone before which was expected as the survey was conducted online Of those who had a smartphone, eighty two percent (82%) had downloaded less than forty (40) Apps on their smartphones or tablets with fifty two percent (52%) of those having downloaded less than twenty (20) Apps, indicating people are not too fond of downloading too many Apps considering according to Apple (2022), App store has about 1.96 million active Apps while according to Buildfire (2022), Google play store has about 2.97 million active Apps available for download. Answering which Apps, they use the most, thirty one percent (31%) of the respondents indicated they use social media and services the most though social media was the highest ranked. When asked to rank in order of usage, fifty-eight (58%) of respondents ranked social media number one. This shows when people are on their phones they are mostly trying to connect with family and friends than concerning themselves with what their service providers are saying. Respondents were asked if they would use an integrated service failure App if it were to be made available and sixty five percent (65%) responded with a ‘yes’ while twenty six percent (26%) responded with a ‘maybe’. Together this makes ninety one percent (91%) of respondents that indicated the need for a single App that various organisations can use to communicate to their clients when they cannot meet the agreed service level. This shows smartphones users prefer a single point of contact to get information that is important to them. This need is currently being fulfilled through social media which literature has indicated is not ideal as most users use it to socialise and therefore cluttered with social material and marketing material from organisations. Single communication App that can be used by organisation becomes a necessity for consumers to get information real time that is focused on service failure and in an organised manner. Hypothesis (H0a) tested if there is a positive relationship between number of Apps downloaded and integrated communication App usage to see if users with low number of Apps would adopt the combined App as they are not fond of downloading too many Apps. The results showed there is no relationship between number of Apps downloaded and preference of combined App. This means the assumption that consumers with lower number of Apps downloaded will highly likely adopt the communication App was not met. This, however, just means any user irrespective of number of Apps they have is highly likely to adopt the new communication App. In conclusion, there is a need for an integrated App that can be used by organisations to communicate service failure to their service consumers real time. Schwager & Meyer (2007) highlighted the risk of having customers calling in to find information in an event service has been interrupted which could increase the cost of doing business as it is customer service personnel dependent. Ukpabi et al (2019) highlighted challenge with the use of contact centre that it is customer service personnel dependent and is both cost intensive and may lead to customer dissatisfaction if the personnel is not well trained. Moore & Clayton (2007) highlighted the risk of fraud and phishing with the use of eMails and SMSes for customer communication. To improve customer communication, Cai et al (2020) indicated there is a need for a communication channel designated to customer communication where there is no information overload, and it is costs effective. Therefore, this study supports the literature that there is a need for a designated communication channel. 5.3 Factors influencing application adoption 5.3.1 Introduction As it was seen above that both the App store and Google play store have millions of Apps, yet the eighty two percent (82%) of the respondents have indicated they have downloaded less than forty (40) Apps on their phones or tablet, indicating the App adoption remains an issue. This section analyses factors that are considered important for App adoption. These factors are grouped into three sections which the first one is product related factors that deals with the functionality of the App which include that the App must work, it must be reliable, helpful, easy to use, useful and must be available at no cost to the user. The second group deals with security related factors to determine security features most important to the App users, and these include that the users details must be protected and that the user device must be protected from viruses and malware. Financial details security feature was not included because the plan is not to request users’ financial details. The last group deals with social related factors explori