Peter M Bednar
email: peter.bednar@port.ac.uk

Christine Welch
email: christine.welch@port.ac.uk

Sustainability in IS: the case for an open systems approach

Keywords: Contextual Analysis, Open Systems Approach, Contextual Dependency, Socio-Technical Design, Complex Information Systems

Common sense tells us that cost cutting leads to saving, and spending should therefore be minimized. However, a little reflection tells us that this sometimes leads to false economies. In an organizational context, these can lead on to a downward spiral of organizational 'suicide'. Examples of false economies may include: saving on maintenance; saving on research and development expenditure; saving on margins (waste or just-in-time management); and saving on 'how' we do things, as opposed to 'what' we do. Common sense cost cutting makes 'how' invisible, and only recognizes 'what'. It is vital that we also remember to consider 'why' activities are undertaken. Professional competence implies not only skill/knowledge in a particular field, but also desire to apply that knowledge in accordance with certain values, and engagement with the context of application so that learning through reflection may take place. Professional work therefore includes scope for extra-role behaviour, such as suggesting innovative methods or identifying and developing new opportunities (Bednar and Welch, 2010). We suggest that a naïve pursuit of 'efficiency' is likely to constrict and curtail possibilities for extra-role behaviour, with disastrous consequences for the development and growth of the business. Creation of systems experienced as sustainable therefore requires us to focus attention on perceived usefulness, rather than efficiency.

BACKGROUND

It is possible to conceive of an organization as a particular instance of a purposeful human activity system (Checkland, 1981). A precise agreement about the nature of that system would be difficult to achieve since individual experiences of the same phenomenon vary widely. Boundaries drawn by a person in conceiving of a human activity system will depend upon her changing perspectives over time, which are unlikely to concur precisely with those of others. Organisations subsist as complex, open systems that are continually co-created and recreated through the interactions between their individual members. Open systems that we experience as useful involve a certain ambiguity -a tolerance for variations and imperfections. Maintenance of a perfect equilibrium at all times would be both impossible, in the light of individual, contextually-dependent interpretations of system boundaries. This is, in essence, the reason why Vickers (1972), for instance, preferred a model of relationship maintaining to one of goal seeking when he reflected upon the nature of organizational management. ICTs are deployed in a purposive, contextuallydependent way, i.e. relevant to some particular members of the organization who expect to engage with them in their work. The information needs of those individuals will be recreated continuously over time in the context of activity. Use of the data system will therefore need to be adaptive to these needs. Attempts to design perfect equilibria in such systems would be likely therefore to have an adverse effect on usefulness in practice.

Recently an insurance company perceived a problem in their order processing system, resulting in a significant decline in customer satisfaction. In response, a new IT system was developed early in 2010 intended to improve productivity by enabling each operator to answer four calls simultaneously, together with on-line ordering and a facility for customers to look up product information for themselves. Statistics covering the following year showed an increase in customer inquiries answered. However, the number of actual orders placed remained remarkably constant throughout the period of the project. The impact of the new system was marginal on productivity, but customer satisfaction continued to decline drastically throughout the period measured. However, the exponents of the new system continued to assert its vaunted benefits -these were, they implied, still hidden in undiscovered and unspecified 'qualitative' data. This appears to be an instance of a solution looking for a problem -an assumption that whatever difficulties the order processing system was experiencing, the answer must surely be a new piece of software. The lack of any evidence of progress was then disqualified by the evaluators themselves with the suggestion that they simply had not looked for it in the right place. This case reminds us of work by [START_REF] Schoderbek | Management Systems: conceptual Considerations[END_REF] reporting research by the IT Governance Institute into 1600 projects in UK businesses. More than half of these projects in organizations were seen to deliver only marginal benefits, but in approximately one third of cases projects actually destroyed organizational value. He also puts forward evidence to suggest that managers continue to support these projects beyond the point where they already know that this will happen.

Why this apparent paradox? We suggest that it is the undue focus on the 'what' (e.g. "functional requirements") of system developments, as opposed to the 'how' (e.g. "non-functional requirements"). Managers simply say to themselves that the initiative must deliver value (according to common sense logic). We suggest that this phenomenon is caused by a fragmented view of the development process, brought about by a lack of a sound and holistic socio-technical approach to systems analysis.

Although analysts may recognise the importance of a socio-technical stance, taking into account context, this is frequently limited to local context. Analysis is then restricted by a closed systems perspective (focusing on 'what' a system is intended to do) but sustainability in IS depends upon an open systems perspective (including 'how' and why a system may be experienced as useful by someone). Managing effective transfer and diffusion of technologies requires consideration of the wider environment within which a company is operating and not just its own, internal technical systems. [START_REF] Langefors | Theoretical Analysis of Information Systems[END_REF] pointed out that those engaged in managing an organization need to know about the behaviour and condition of all its component elements, and the wider environment in which it operates at any given time. It is possible to develop a data system to support managers in their tasks, which becomes an information system for any given individual through direct and interpretive participation. Langefors originally considered that the purpose of an information system was to promote attainment of organizational goals. However, he soon realised that expression of any such goals was itself a problematic task, itself requiring a supporting information system. A reflexive relationship can therefore be seen between these defined purposes of IS: promoting the attainment of organizational goals and also support for goal setting. Viewed as a human activity system, the elements of an organization are all interrelated -operational units, sub-systems to monitor their operations and a managing sub-system interpreting data from them, in order to support operations with appropriate resources and directions. Since these interrelated elements are coordinated through interconnected information-generating units, it may be preferable to view the organization and its information system as different views of the same phenomenon (Langefors, 1995, p53).

PROBLEM SPACE

Consideration of sustainability in information systems requires us to pay further attention to the nature of organisations. A system may be described as autopoietic (self re-creating) if its component parts interact with each other so as to continually [re] produce and maintain that set of components and the relationships between them (Maturana and Varela (1980, pp. 78-79). There must be sub-systems perceived to be allopoietic, i.e. that have a purpose other than continuation of their own integrity. Luhmann (1990in Midgley, 2003 p.67)) has suggested by analogy that social systems, such as organisations, can be seen as autopoietic within a given boundary, i.e .a homeostatic, self-referential system whose critical variable is its own existencenot, of course, living, conscious beings. It is not 'life' that is continually [re]produced but 'meaning'. Such a system constantly creates and recreates itself within its autopoietic space in the context of interactions with its environment. Any structural element of the system may change radically over time, but the existence of the system is maintained. Within the context of an organisation, the elements of which it is comprised may be observed as allopoietic sub-systems, i.e. their interactions make up inputs and outputs to organisational processes and are therefore purposeful in that organisational context. An organisation is part of the wider environment with which the individual system must interact in maintaining its ontological integrity -the sole ultimate goal of an autopoietic system. It is interesting to reflect upon this in comparison with Vickers' idea of organisational management systems as relationship maintaining, rather than goal seeking. If the theory of Autopoiesis is accepted, then the goals of the disparate elements cannot be identical, or congruent with the expressed goals of the wider organizational system, at any given time.

There have been examples of companies which thrive in the initial stages of marketing a new product, while the market is expanding. However, once the market nears saturation point, the initial success is not sustained because the company is too product-oriented (and confuses usability with usefulness). Customers do not necessarily choose products on their technical specifications alone, but on a whole range of 'qualities' influenced by convenience, fashion and availability. An example can be found in the experience of Nokia during 2010 leading to total restructuring of its mobile business 2011 [START_REF] Orlowski | Why Nokia failed: Wasted 2000 man years' on UIs that didn't work[END_REF]. The need for an ecological approach is illustrated by Capra (1996) who discusses the example of a bicycle perceived as a system. An ecological awareness goes beyond perceptions of the cycle as personal transport, to consider its natural/social environments. This incorporates awareness of the materials from which it is made; the sources of those materials and the processes by which they were derived; how and where the bicycle was designed, manufactured and marketed; what potential riders are seeking for in their use of the bicycle; its impact on the environment in which it is ridden and the society in which the riders live, and so on.

When a business wishes to deal with losses or to increase profits, there are two alternatives: reduce costs or increase revenue streams. The first alternative is clearly a good idea if there is a lot of wasteful inefficiency in the firm, e.g. a lot of wastage in the production process or poor management of administrative functions. However, great care is necessary because cuts to essential services can be counter-productive. For example, suppose the Board of a company look around for areas to cut costs and see the R&D department as a drain on resources. They may choose to cut the research budget, with the result that the firm does not develop or apply relevant technology, notice a new development in technologies, or a change in customer tastes. The rival firm in the next town, which still has its R&D department fully functioning, may well notice these trends and respond to them effectively, thus taking away some of the cost cutters' market share. This will lead to a fall in that firm's existing revenue streamsi.e. reduction in profit. In effect, the business is shrinking. It is possible to view this in systemic terms as an instance of a positive feedback loop having a destructive effect on system behaviour. The action taken in the firm to bring output in line with expectations has actually had the opposite effect and the discrepancy between planned and actual output increases (See Schoderbek, et al 1990, pp112-113). In the 1970's and 1980's, when 'new technologies' were first given serious consideration in business organizations, much emphasis was on cost savings. This is quite logical: one word processor operator could achieve the same work output as several typists. Similarly, if a production line can be 'manned' by software controlled robots, there are savings in wage costs -particularly as robots do not take sick leave or need holidays. Thus, a dominant idea grew that ICTs are a means of saving on costs. Of course, considerable investment in new systems was needed in order to attain these desirable savings. The difference between costs and investment is an important one and must always be borne in mind by those spending organizational budgets. The Law of Diminishing Returns in classical economics tells us that, as investment in capital increases, so the marginal increase in revenue diminishes [START_REF] Samuelson | Economics[END_REF]. In a similar way, the extent to which investment in ICTs can deliver efficiency gains will be limited. Especially if the necessary investment in the organisational development, behaviour and change-process is ignored, underestimated or not understood. The emphasis on efficiency gains also ignores the important role of ICT investment in improving effectiveness. Often, these systems are enablers of progress (or sometimes just keeping up with the on-going needs of users for enhanced utility).

CONCLUSION

Developments in ICTs have not focused just upon efficiency. We have faster machines and smarter software systems than those of the 1980's. No bricks-andmortar bookshop of the 1980's could have carried out the kind of analytics on customer purchasing behaviour that Amazon.com is able to do today. Profits can be increased not through efficiencies but through change of organizational behaviour and thus generation of enhanced and new revenue streams. However sustainable effectiveness is a sociotechnical phenomenon requiring a focus not on technology dissemination, usability and potential use -but on contextually relevant application and usefulness. Traditional socio-technical approaches (e.g. Mumford, 1983) do not go far enough in promoting systems experienced as useable in context. Methods are required that are based in phenomenology, to address complex open systems by providing support for inquiry into multiple levels of contextual dependencies (e.g. Bednar, 2000). Sustainability in business requires understanding of the (complex) relationship between investment, cost control and profitability. Leaders and "visionaries" often engage in rhetoric suggesting that their policies will simultaneously achieve cost savings and improvements in quality. It is necessary to be sceptical about such claims however and challenge paradoxical thinking which leads to creation of IS that are not only not perceived as useful by organizational actors, but which can actually destroy value for the business.

:

  Bednar, P. (2000). A Contextual Integration of Individual and Organizational Learning Perspectives as Part of IS Analysis. Informing Science. 3(3) 145 -156. Bednar, P. and Welch, C. (2010). " Professional desire, competence and engagement in IS context" In D'Atri A. and deMarco M. (eds.) Management in the Interconnected World: Exploring the connection between organizations and technology. Springer 359-366. Capra, F. 1996. The Web of Life: A new scientific understanding of living systems. NY: Anchor Books.

AUTHOR BIOGRAPHIES:

Peter Bednar is originally from an engineering background and has several years of industrial experience in systems analysis and development. He is currently working as a Senior Lecturer in Information Systems at the University of Portsmouth, UK. He is also affiliated with the Department of Informatics at Lund University, Sweden.

Christine Welch is a Principal Lecturer in the Department of Strategy & Business Systems at the University of Portsmouth, UK. Her research interests include critical systemic thinking and contextual analysis. She is currently Vice-President of the UK Systems Society.


Jan Pries-Heje

Ann-Dorte Fladkjaer Nielsen

Innovative Project Idea Maturation: An important part of Governance

Keywords: Governance, project idea, design science research, project management

Project Governance includes the elicitation of ideas for projects, choosing the best ideas, initiating projects, assigning resources, and nurturing the projects to success. In the third largest bank in Denmark -Jyske Bank -IT project managers often became uncertain when they were assigned new projects; their descriptions were either to vague or broad or sometimes even both. There was a need for better project scoping and improved idea descriptions. In this paper we describe how we committed ourselves to solve this problem. Based on a literature study on innovation and using an existing creative space (room) called the "land of opportunity", we used a design science approach to formulate a new idea maturation concept. Here two years later, we have developed and evaluated our concept on several project ideas. The uncertain project managers are now very enthusiastic; they really feel that they have a much clearer scope and idea description as well as an early prototype of the idea. After four design iterations, the concept stabilized. In this paper we present the idea maturation concept and the organizational design science research process we have used.

INTRODUCTION

Peter Weill & Jeanne Ross (2004) talk about five key IT decisions in IT governance: (1) IT principles decisions, (2) IT architecture decisions, (3) IT infrastructure decisions, (4) Business application needs, and (5) IT investment and prioritization decisions. As part of the last decision area project governance is a major part. Project Governance includes the elicitation of ideas for projects, choosing the best ideas, initiating projects, assigning resources, and nurturing the projects to success. Only then can the investment represented by the projects be ´harvested'.

Jyske Bank is Denmark's third largest bank, formed through a merger of smaller banks in 1967 and headquartered in Silkeborg. There is still a long way to the two largest banks in Denmark, so Jyske Bank is very conscious of differentiating themselves. "Make a Difference" is their mantra. On their homepage Jyske Bank presents themselves as the (social) climber in the banking industry who "always treads new paths." Innovation has, for many years, been an important parameter to differentiate Jyske Bank from others. In order to differentiate oneself and be innovative, one must constantly come up with new ideas. In Jyske Bank, the effort towards this has been very conscious. Among others, they have designed a large room on the ground floor in the Silkeborg headquarters, dubbed "the land of opportunity." The room is furnished as a fantasy world based on Norse mythology, with a large painting in one end of the room and a wide canvas in the other. Colours, soft shapes and flexible partitioning is part of the design. Even the coffee mugs and the cutlery are "different". The room's purpose is to promote the creative process, by bringing humans into other environments than those they are usually surrounded by in their work.

Many good ideas arise around Jyske Bank, but they are not always presented or described well enough. In autumn 2007 an interview survey among project leaders revealed that the individuals often felt that the project descriptions they were given were too vague and superficially described, so they felt uncertain about the projects they were responsible for. The survey led to the creation of a new project, with the definition that Jyske Bank did not need more ideas, but better descriptions of the ideas. Focus was thus the transformation from draft proposals to better project descriptions. We formulated the central question as thus: What does a concept for better project descriptions look like?

How is the remainder of the paper organized?

The remainder of the paper is structured as follows. First, we talk about the theoretical anchorage of the concept, both in innovation theory, and in situational theory. Then we briefly describe our approach as a mixture of action research and design science. Thereafter follows a long section where we describe the concept in detail. This is followed by a series of selected examples of the use of the concept. Finally, we summarize the points of the article and discuss further research.

CREATIVITY AND INNOVATION

"Creative" comes from the Latin Creare, which means to create. As a starting point, "Creative" is a positive word, (something everybody looks positively on). A usable definition of creativity is: "A systematic to break the habit of thinking". Innovation is the deployment of an idea or an approach that relates to a product, service, device, system, policy or program that is new to the organization deploying it [START_REF] Damanpour | The dynamics of the adoption of product and process innovations in organizations[END_REF]. Coming up with new ideas is creative. To implement it is innovation -when it is experienced as new and creates something of value.

Edward de [START_REF] De Bono | New think; the use of lateral thinking in the generation of new ideas[END_REF][START_REF] De Bono | The mechanism of mind[END_REF][START_REF] De Bono | Lateral thinking -creativity step by step[END_REF]) did research on how people think, be it innovative and/or creative. De Bono notes that all self-organizing systems form patterns -habits, we call it often. This means that one is forced to move across these patterns instead of just following them, if one wants to think thoughts that are different. De Bono calls this "Lateral Thinking".

If one is to think laterally -to the side -then it is necessary to put oneself in a situation that changes ones starting point and perception of how things link, to actually force one to think in a fundamentally different way.

Another source of inspiration is IDEO [START_REF] Kelley | The Art of Innovation: Lessons in Creativity from IDEO, America's Leading Design Firm[END_REF][START_REF] Kelley | The ten faces of innovation[END_REF]) that helps companies innovate. They create strategies for innovation and they design products, spaces, services and experiences. Key to their success as a design and innovation firm are the insights they derive from understanding people and their experiences, behaviours, perceptions and needs. Donaldson (2001, p.5) defines contingency theory in the following way: "At the most abstract level, the contingency approach says that the effect of one variable on another depends on some third variable ..." The core of the situational theory, therefore, is that organizations that want to work optimally need to take account of the situation; it is the factors of the situation that determine what the best approach would be. Contingent models have, for example, been applied in vague and ambiguous situations, where the information available could be interpreted in many ways depending on perspective [START_REF] Galegher | Computer-mediated communication for intellectual teamwork: An experiment in group writing[END_REF]. Contingency factors have included leadership style [START_REF] Hersey | Life Cycle Theory Of Leadership[END_REF][START_REF] Hersey | So You Want to Know Your Leadership Style?[END_REF], communication support, process structuring and information processing [START_REF] Zigurs | A Theory of Task/Technology Fit and Group Support System Effectiveness[END_REF][START_REF] Zigurs | A test of Task/Technology Fit Theory for Group Support Systems[END_REF], as well as task complexity and whether the technology is appropriate for the task (Van de [START_REF] Van De Ven | The Concept of Fit in Contingency Theory[END_REF].

Contingency theory has been criticized for having an excessively static view of the relationship between structure and technology [START_REF] Barley | The Alignment of Technology and Structure Through Roles and Networks[END_REF]. Nevertheless, we found characteristic features in the situation, related to the idea maturation context, that we then used to develop a situation specific concept. Obviously we can not rule out that it is a very static picture we've seen, that would be are outstripped by the dynamics of reality; but it has simply not occurred here more than a year after we identified the main situation specific factors.

RESEARCH METHOD

Herbert A. [START_REF] Simon | The Science of the Artificial[END_REF] defined in the book "The Sciences of the Artificial", a science that focuses on design/construction of artefacts or human systems with the "desired" properties. Simon defined design science as the counterpart to the (natural) sciences that study given things (in nature or society) and how they work. [START_REF] Hevner | Design Science In Information Systems Research[END_REF] presents a definition of Design Science Research (DSR) and outline three key processes. First there is iteration between building and evaluation. Second is the relevance-process, i.e. once the design has gone through a number of iterations in the middle, it turns back and is attempted to be used to meet the needs or solve the problem. Third, we find the research process ("rigor"), where the input from the existing research knowledge base is obtained, and it is reported what contributions to scientific knowledge the design allows. Whether the knowledge is in the form of theory, model, method, framework or something similar, it is the researchers job to take existing knowledge into account, and "stand on the shoulders of it" i.e. to build on the existing. Thus DSR relates to the needs and problems with focus on satisfying the needs or solving the problems. Design science is prescriptive, or perhaps even normative. Thereby, design science differs from analytically oriented sciences, where the objective is to understand and describe.

We decided to use DSR as our approach for designing and building a solution to the problem at hand in Jyske Bank, for testing it, learning from it and becoming wiser, built an improved concept, test it, learn from it and become wiser, etc. The main reasons for choosing DSR was the close engagement with practice, the iterative nature of DSR that ensures that learning can be captured and used. Further the 'scientific' cycle of DSR ensures that what is designed is done on a sound scientific state-of-theart foundation.

The first version of the idea maturation concept was developed after studying the problem and its scope closely (relevance process). We interviewed around 10 projectmanagers in the organization and dived into the old project descriptions and business cases to get insight into the content and quality of it. It quickly became clear that many immature ideas were launched. This was documented in a presentation that spawned our work on creating a concept that could improve project descriptions.

It was with these inputs, as well as our own experience and the knowledge that the literature on innovation and creativity has given us (the research process) that the first version of the concept saw the light of day.

Alongside studying projects from their own organization, we read what others had written about for example innovation. The literature gave us an overview and inspired us to create a concept that could actually be used to improve and mature ideas.

We worked with the concept in four design iterations. Our first version of the concept was ready in June 2008. In each iteration, we have received feedback from users, and their comments for each step in the process gave rise to (many) adjustments.

In general we have chosen a naturalistic examination [START_REF] Venable | A Framework for Design Science Research Activities[END_REF] in a real organization -Jyske Bank -with a perceived problem. The first evaluation trial took place in June 2008. This evaluation gave rise to many changes in the first version of the concept. The same happened after the second and third evaluation of the concept.

THE IDEA MATURATION CONCEPT

Finally in 2009 we ended up with the concept presented here with four steps:

• Step 1: A discussion of: How clear is your idea? • Step 2:
Determining what idea maturation process would be best suited for that specific business idea? • Step 3: Arrange idea maturation workshop • Step 4: Secure results of idea maturation process including relevant reviews

The purpose of step 1 -How clear is your idea? -is to uncover a focused phrase that describes the idea. Experience from Jyske Bank shows that it is often wasted work to pursue an idea if the wording is unclear. That is similar to having been through a phase with divergent thinking, but without closing and deciding, similar to the convergence phase (Jakobsen & Rebsdorf, 2003).

To be sure that the idea owner has the right starting point for further idea maturation, we have selected some questions from (inspired by [START_REF] Pries-Heje | Laer at implementere: Software procesforbedring der virker i praksis[END_REF], which gives a clear indication of whether the idea is sharp (= clear wording of idea -convergent phase completed) or open (= unclear wording of idea -convergent phase not completed).

The questions we use in the concept to be answered by the idea owner is shown in Figure 1. It takes approximately an hour to give and receive answers to the questions in Figure 1. We note the idea owner's responses as they are obtained. There are 3 possible outcomes of Step 1:

1. The idea owner has a concrete answer to the questions (Figure 2) and is therefore ready for step 2 of the concept. 2. The idea owner has been unable to respond to one or more of the questions and should therefore continue working to concretize the idea before it the idea is pursued. (New meeting is planned) 3. Idea owner has found out that the idea is not sustainable enough and the work on the idea ends here.

The knowledge that is the result of questions and answers session can be used directly in various sections of the project description. As a result, the idea owner is no longer left standing with just a blank and empty description. The purpose of Step 2 is to get a characterization of the idea, so that we can find the idea-process just sufficient enough to mature the idea. The idea-owner will be asked to decide which group of statements from Figure 2 best matches his idea.

Fig. 2. The four innovation types included in step 2 of the idea maturation concept

The type with the largest amount of fitting statements is the type that forms the basis for further work on the idea. Each grouping has a corresponding idea maturation process, a number of techniques, proposition of participants, etc. The four types represent the contingency factors that we found central in idea maturation at Jyske Bank. Radical innovation connotes groundbreaking innovation: the surprisingly new, that in a radically new concept links technology and value from use. Incremental innovation connotes innovation in small steps where existing concepts are linked in a new way. Often, it is a question of continuous improvements and adaptation of existing products. We could have done far more than four types, but to ensure its practical applicability we ended up restricting ourselves to four main groups. Together, the four types cover the entire innovative spectrum, from radical to incremental innovation [START_REF] Dewar | The adoption of radical and incremental innovations: an empirical analysis[END_REF]

THE FOUR INNOVATION TYPES IN THE IDEA MATURATION CONCEPT

In Figure 3 we have shown the four innovation processes that are implicit in the four innovation types covered by our concept. The result of a workshop is always a prototype that visually describes the ideal solution or solution elements.

Radical innovation often has a very divergent and convergent phase. In Figure 3, the divergent part is illustrated as a lateral "V", which opens from left to right.

Similarly, the convergent part is illustrated as an interim "V", which closes from left to right. The difficulty of an idea maturation workshop for radical innovation is that the uncertainty is very high. Some workshop participants won't be comfortable with very high uncertainty. It may also be that the participants at the workshop are okay with it, but it is as much about the organization's expectations: "Will we get some answers to our questions soon?" rather than just generate more questions. Therefore, it has to be a very 'strong' idea-owner to be responsible for an idea of this nature. Otherwise, you risk the process being closed too rapidly -it travels too fast from divergent to convergent -and then the result won't be radical innovation anyhow. Fig. 3. How the process runs dependent on innovation type 360 degrees innovation, corresponding to drawing 2 in Figure 3, is special because each of the divergent and convergent phases needs to be able to handle several different opinions and angles. There might, for example be several target groups that have to be evaluated at the same time. Or it could be several sources of inspiration that are used in parallel. Idea maturation workshops of this nature are often more complex, and require more advanced techniques and external contributions. We spend from 1-2 days on workshops of this type.

Experimental innovation, equivalent to drawing 3 in Figure 3, has a lot of small and rapid divergent and convergent phases. This workshop is about developing some ideas and getting them translated into a prototype that can be assessed and improved. The improvements should in turn be incorporated in the prototype and evaluated, etc. This process is repeated until the user accepts the prototype. Often we use 1 day on the idea maturation workshop.

Quick innovation (drawing 4 in Figure 3) is a single small process, where we get through both a divergent and a convergent phase in a very short time span. We use simple techniques that are known to many in the organization. An idea maturation workshop of this type often takes 2-4 hours. To get the participants in a workshop to identify with the activities related to the situation-specific group, we decided to use metaphors. Something similar has been done with very good results in systems development [START_REF] Kendall | Metaphors and Methodologies: Living beyond the Systems Machine[END_REF]& 1994).

More specifically, in the workshop we have chosen to use a travel metaphor corresponding to the four types as follows:

Type 1 is 'The journey into space' this means that we are going to face the completely unknown. It is essential to think in a totally new way. We need to create something that no one has seen before Type 2 is a trip 'Around the world' this means that we need to examine varied sources of inspiration, look elsewhere, try to work in a different way, etc., to conceive completely new ideas into the existing situation. We know what the job is, but can it be done in another way? Type 3 is a 'fieldtrip to a science lab in Silicon Valley' this means that we must experiment our way to a solution. We need to work with prototypes, assessments, improvements. We steal with arms and legs! We need not reinvent the wheel again.

Type 4 is a 'Shopping trip to London' this means that what we are looking for are easy, small and fast methods to get a lot of ideas out in the open and then select the best -one or more. We're short on time, but we would like to spend some of it getting the good ideas out. Maybe a slightly more modern product will cover more needs. Often adds bits to something existing.

Travel metaphors have proven to work really well at giving participants a sense of how much innovation is required for idea maturation. Moreover, the travel metaphor gives inspiration to the staging of the workshop. We aim to adjust the staging to fit the idea. If we are to mature an idea that falls into group 4: 'Shopping trip to London' we aim to create a workshop environment with shops, for the right age group and market segment.

As part of the concept, step 2, we co-operate with the idea-owner to asses who should participate in the workshop. The purpose of step 3 -arranging the idea maturation workshop -is to get a group of well-chosen people to specify the idea and generate proposals for the first draft of the solution. The result of step 3 is a clear description of which solution proposals or solution elements to pass on to a development project -including a clear definition of what to include and what to exclude. Also step 3 results in a simple prototype that visualizes the product or process being generated by the idea.

Many different creativity increasing techniques can be found in the literature. We have tested many but have no final set of techniques to recommend. We can only say that it is contingent on the experience of the participants and the facilitator. We use the travel metaphor to plan the progression of the workshop. First we're going out and then were returning home -we must first open, then close the idea generating process -divergence and convergence.

The prototype developed at the workshop need not be a working functional prototype. It may be a simple drawing of a process, a sketch of a product or something similar. For the workshop we use "the land of opportunity". Figure 4 shows an example of the workshop setup from "a hike in the Alps"; the idea is to imagine being in a tent camp in the mountains. We have also found that things like appropriate catering, music and effects that the participants can touch and play with are important and help them live themselves into the fantasy.

At the beginning of a workshop we have found it to be a good idea to have an employee from the business area of the idea tells a little about how the new idea fits into the overall strategy of the company, the thoughts behind the idea, if the idea is part of a larger context etc. We have also had inspirational presentations from external specialists. If the purpose is, for example, to come up with ideas for the customer segment from 18 to 24 years, it might be appropriate to have a futurologist talk about current or upcoming trends for this segment.

iVenture & Radical innovation

Financial guide to a Jyske Bank advisor when he is in dialogue with customers. For risk assessment of clients' securities composition.

3

Budget zone in online banking & Quick innovation

New feature included in online banking as an application for private customers.

4

Secure buy&

Experimental innovation

A new banking product for securing corporate clients, when trading with foreign countries.

In Table 1 we have given four examples of using our idea maturation concept for four different types of innovation.

AN EXAMPLE OF AN IDEA MATURATION WORKSHOP

To convey the core of how the idea maturation workshop is held, we give an example from one of our evaluation. In the example (no. 1 in Table 1) the problem to address was the fact that many SMEs are required to present a strategic plan to Jyske Bank to obtain a loan or another Baking product. However, this requirement can sometimes be difficult to meet for SMEs because they do not have the expertise inhouse to develop a strategic plan. Thus the idea we started out from was that the bank could offer customers expertise to help them with their strategy process. This would create a win-win situation in that the companies would get assistance and the Bank would get insight into the company and thereby be able to offer customers the right banking products. Our focus in this example is on process rather than outcome (the product that was matured). In this example, we will discuss an idea from group 2, so we needed to build an idea workshop with 360-degrees innovation -a trip around the world.

The day began at a check-in counter at the airport. Participants were each provided with a travel suitcase, a boarding pass, a bread roll in a paper bag and a newspaper.

Then they were allowed to check in and go ahead with the workshop. This start-up was created so the participants could become aware that there would be something different going on -not the usual meeting, nor a normal meeting room.

In the room where the participants entered, we created four different "client countries", in the form of staging of 4 different types of businesses which participants would go to. For each country, participants were given the task to discover what the customer's thoughts about the product/service were. In the carpentry workshop, they literally sat amongst wooden boards, laths and machinery, and tried to imagine how a carpenter thought and felt about the particular subject.

The next process of the workshop was a presentation by an outside consultant. From what he told me and from the inputs the participants had gotten from their travels to different types of businesses, we produced the first draft of a prototype. A process, where the facilitator tried to assemble the many inputs into a drawing, they could all say yes to.

Thereafter, the first prototype had to be made marketable, so the next task was called "How do the advisers work with this model, so the customers experience Jyske Differences?" To get participants to think "differently", we chose to let them work in different cities. We made a scene setup with three different cities (Paris, London and New York). For this, we used images, sounds and props that characterized each of the big cities. Participants would then in turn go to the three cities and gather inspiration on how to market the product. With the combined associations they had created on their journeys, we developed the next version of a prototype. This prototype was then tested against the different types of customers; again in the form of a journey to the four "customer countries". The above is an example where we constantly "force" the participants to see the problem from different angles. We tried to bring them into realistic environments and environments which could hopefully create very different connotations than the usual ones. We believe that both parts were successful. Also, it should be emphasized that this is only one example. For other idea workshops, we will probably create quite different destinations, tailored to the specific idea. As the example shows, the organization of the idea workshop is contingent on the chosen innovation type. The trip is never a "standard trip".

The outcome related to the idea that has come from steps 1, 2 and 3 needs to be secured and described in a project description. There are several target groups for the project description, which is extremely important to keep in mind when the writing process starts. The target groups are decision makers in the organizations and the upcoming projects and project teams. These two groups have widely varying needs. The decision makers require a project description that describes the project idea in a clear and concise manner with just enough information to enable them to make the decision of whether the project idea is to be realized or rejected.

It is in the interest of the future project -manager and group to have the project presented in such a way, that they are not forced to start from scratch, but can draw on the information that has already been studied and described in the project description.

When the project description has been written -with all the details of steps 1, 2 and 3 -it is ready to be enriched. The idea-owner can have two needs of enrichment -they may need, some people who can contribute knowledge directly to the project description, or they may need to have internal experts review the resolution, to make sure it is sound and in accordance to Jyske Bank's values and strategies.

CONCLUSION AND LEARNING POINTS

We believe we have designed a successful idea maturation concept in four steps, which Jyske Bank has benefited considerably from. The largest benefit is when starting a project, as the project group gain a quicker start because the idea is better described and documented through an idea prototype. This means that the project group doesn't waste time getting acquainted with the task, but instead can 'dive' directly into its solution.

The process we designed ensures that you focus better and think the right thoughts before writing the project description. The contribution is the contingent grouping of business ideas, and the consistent use of the groups, as well as metaphors, to determine the actual idea maturation process.

One criticism that we have encountered is that all ideas "swell" with the use of the described concept. To counter this, we can only say that in practical use, the concept has proven not only to get ideas to "swell" up; it has also put ideas to death!

The main adjustments in the four design iterations are summarized in the following ten detailed learning points, structured by the four stages of the concept:

1. Questions should be good enough, to determine whether the idea is viable. The first time around, our questions (see Fig. 2) weren't broad enough and lacked focus on how the idea fit with the bank's overall strategy, the target group's expectations, etc. 2. Reduce contingent groups as much as possible to ensure clarity. We started with 6 contingency groupings with a corresponding travel metaphor, but it soon became apparent that it was too difficult to ask questions that accurately characterized the group; they overlapped to easily. 3. Metaphors may be a hindrance. We found that Innovation Types were easier to understand than the travel / journey metaphor. Innovation types proved to be more understandable for the idea owner. But the journey metaphor is still important to use, to bring about a framework and thereby the staging of the idea maturation workshop. 4. Design presentation style needs adaptation to targets group. We evaluated the production of the idea characteristics in three different ways. They were: (1) XYaxes with statements for given situation factors.

(2) Groupings of statements to cross off. (3) A matrix of key statements, which together made a room. As described earlier participants preferred (2), so this is the approach we have presented here. 5. Transformation processes are hard! Generally participating in a creative process such as our idea maturation workshop requires something completely new for the vast majority of our employees. You have to overcome some resistance.

6. It is very important to have the right people must participate in the workshop. In the beginning, we were not focused enough on getting the right people invited to the workshops. After the first two pilot tests we realized the importance; and it made a (positive) difference to have them joining. 7. Very difficult to escape the everyday life and challenges. We have learned that our participants have difficulty getting away from their daily life in the organization. In the first pilot we were not sufficiently aware that the choice of travel metaphor had to be placed in the participants' consciousness several times during the workshop. 8. People are different. Some are better than others at associating, brainstorming and living themselves into a (travel / journey) metaphor. The concept needs to allow for this difference. 9. Some people are very solution-oriented. Some participants find it very hard to work for period of time in the open phase. They immediately start evaluating ideas and discard some ideas as unrealistic. Thus you need facilitation to "keep" people in the creative mode. In the concrete we have meet four distinct types of people: 1. Those who just throw themselves into deep water and play along 2. Those who can participate well in the creative thinking games, but have a strong need for a facilitator to manage the process 3. Those who might feel as group two, but let's it depend on the rest of their group 4. Those who think stuff like "creative processes" is a complete waste of time Groups 2 and 3 have in this case been by far the largest groups and roughly equal in size. 10. Continuous adjustment of the project description template has been needed. Thus we have not only designed a new process for idea maturation but also the artefact coming out of idea maturation.

Finally, we have written this paper in the hope that others can use or be guided by our concept. We have emphasised details to an extent where it should be possible to learn from our example. Further, we have given the details of our design science research method thereby giving the academic reader a better insight into our work and giving the opportunity of judging the quality.

Fig. 1 .

 1 Fig. 1. Artefact from workshop used to give an overview of the questions in step 1 of the idea maturation concept

Fig. 4 .

 4 Fig. 4. Tent camp in the Alps; a travel metaphor implemented in the "land of opportunity"

Table 1 .

 1 Four examples of projects and innovation types

	No.	Project & Type	Result
	1	Strategic concept for	A strategic concept for corporate
		corporate customers &	customers. Many bank products require
		360 degree	that corporate customers can produce a
		innovation	strategic plan. However, many small and
			medium corporate customers do not have
			the ability to develop a proper strategic
			plan. Therefore Jyske Bank will offer to
			help with it expertise; a kind of add-on
			banking product offered to clients.


Yogesh K Dwivedi
email: ykdwivedi@gmail.com

Nripendra P Rana

Hsin Chen
email: hsin.chen@beds.ac.uk

Michael D Williams
email: m.d.williams@swansea.ac.uk

A Meta-Analysis of the Unified Theory of Acceptance and Use of Technology (UTAUT)

Keywords: Adoption, Diffusion, UTAUT, TAM, Meta-analysis, Information Systems

The originating article of the Unified Theory of Acceptance and Use of Technology (UTAUT) has been cited by a large number of studies. However, a detailed examination of such citations revealed that only small proportion (43 articles) of these citations actually utilized the theory or its constructs in their empirical research for examining IS/IT related issues. In order to examine whether the theory is performing consistently well across various studies, this research aims to undertake a statistical meta-analysis of findings reported in 43 published studies that have actually utilized UTAUT or its constructs in their empirical research. Findings reveal the underperformance of theory in subsequent studies in comparison to the performance of UTAUT reported in the originating article. The limitations experienced while conducting the metaanalysis, recommendations, and the future scope for the further research in this area have also been briefly explained in concluding section.

INTRODUCTION

UTAUT was proposed as a theoretical advancement over existing theories used to examine adoption and diffusion related research. [START_REF] Venkatesh | User acceptance of information technology: Toward a unified view[END_REF] reviewed, mapped and integrated constructs from following eight theories and models: theory of reasoned action (TRA), technology acceptance model (TAM), motivational model (MM), theory of planned behavior (TPB), a combined theory of planned behavior/technology acceptance model (C-TPB-TAM), model of PC utilization (MPCU), innovation diffusion theory (IDT), and social cognitive theory (SCT). By doing so the authors aimed to develop a unified view by eliminating redundancy and repetitions as several constructs in these theories were common.

Like its majority of predecessors' theories and models in the area of adoption and diffusion of IT/IS, UTAUT facilitates in examining user's intentions to use an information system and consequent usage behavior. The variance in intentions can be explained by measuring effect of four key independent constructs, namely, performance expectancy (PE), effort expectancy (EE), social influence (SI), and facilitating conditions (FC) as direct determinants of usage intention and behavior [START_REF] Venkatesh | User acceptance of information technology: Toward a unified view[END_REF]. The effect of independent variables on dependent variables is moderated by following four moderating variables: gender, age, experience, and voluntariness of use [START_REF] Venkatesh | User acceptance of information technology: Toward a unified view[END_REF].

Two of its constructs are similar to TAM constructs: PE can be mapped to perceived usefulness (PU) whereas EE can be mapped to perceived ease of use (PEOU). The remaining two constructs (SI and FC) are from TPB. Due to the similarity (in terms of constructs and relationships) of UTAUT with TAM and TPB, the current and future adoption and diffusion studies might be favouring use of UTAUT. This is particularly more likely as many scholars in the recent past have criticized over exploitation of TAM which ultimately affecting development of alternative theories and models in this area. However, it is difficult to demonstrate that if UTAUT is replacing TAM in empirical studies as there is no review of previous empirical studies that have utilized UTAUT. Also, there is no study that has surveyed or reviewed performance of UTAUT subsequently -so, there is a lack of information regarding reliability and consistency of performance of this theory in different situations.

Many literature reviews and meta-analyses have been conducted on UTAUT's popular precursor theories and models such as TAM and TPB. For example, use of TAM by a large number of studies caught researchers' attention to analyze trends, patterns of use, and the actual performance of the model through systematic review and meta-analysis technique. The successful efforts towards the systematic review were performed by [START_REF] Lee | The Technology Acceptance Model: Past, Present, and Future[END_REF], and [START_REF] Legris | Why do people use information technology? A critical review of the technology acceptance model[END_REF], whereas, the metaanalysis for measuring the performance of TAM was carried out by [START_REF] Deng | A multi-group analysis of structural invariance: an illustration using technology acceptance model[END_REF], [START_REF] King | A meta-analysis of the technology acceptance model[END_REF], and [START_REF] Ma | The technology acceptance model: a meta-analysis of empirical findings[END_REF]. The similar meta-analytic approach was also performed for TPB, and TRA by other previous studies [START_REF] Hausenblas | Application of the theories of reasoned action and planned behavior to exercise behavior: A meta-analysis[END_REF][START_REF] Sheeran | Predicting Intentions to Use Condoms: A Meta-Analysis and Comparison of the Theories of Reasoned Action and Planned Behavior[END_REF].

A large citation counts for UTAUT's originating article, its use in many empirical studies, an inconsistent performance of the theory, and a lack of reviews and meta-analysis related to it necessitate determining the past and current trends of its use by conducting systematic reviews and meta-analysis of articles that have either cited or utilized it as theoretical basis in their empirical research. Considering above discussions, this study aims to conduct a review and meta-analysis of articles that have cited the originating article (i.e. [START_REF] Venkatesh | User acceptance of information technology: Toward a unified view[END_REF] and have utilized UTAUT (or its constructs) for undertaking empirical research on adoption and diffusion of IT/IS. According to King and He (2006, p. 741) "Meta-analysis allows various results to be combined, taking account of the relative sample and effect sizes, thereby allowing both insignificant and significant effects to be analyzed. The overall result is then undoubtedly more accurate and more credible because of the overarching span of the analysis." This outlines contribution of this paper by conducting meta-analysis which might present more accurate and credible performance of the UTAUT theory.

The remaining paper is organized as follows: The next section will provide an overview of the research method utilized. The findings will then be presented and discussed in subsequent sections. The last section of this paper will outline conclusions, limitations and future research directions.

RESEARCH METHOD

As the aim of this research is to analyze and synthesize existing findings on use of the UTAUT theory, a combination of profiling review and meta-analysis methods [START_REF] Deng | A multi-group analysis of structural invariance: an illustration using technology acceptance model[END_REF][START_REF] King | A meta-analysis of the technology acceptance model[END_REF][START_REF] Lee | The Technology Acceptance Model: Past, Present, and Future[END_REF][START_REF] Legris | Why do people use information technology? A critical review of the technology acceptance model[END_REF][START_REF] Ma | The technology acceptance model: a meta-analysis of empirical findings[END_REF] was considered as the most appropriate one for this purpose. This research utilized data collected from studies that cited UTAUT's originating article [START_REF] Venkatesh | User acceptance of information technology: Toward a unified view[END_REF]. These citations were identified by employing Web of Science @ database. The demographic data (such as year of publications, and source of publications) related to all cited studies were first collected from Web of Science @ database. The citations for fully available articles were downloaded for the purpose of extracting further details from the cited articles.

There were total of 870 studies that cited UTAUT, out of which 450 studies were available to be downloaded as full articles. A detailed examination of 450 available studies led to identify 43 studies that used UTAUT (or its constructs) in their empirical studies. The remaining 407 studies just cited the originating article on UTAUT [START_REF] Venkatesh | User acceptance of information technology: Toward a unified view[END_REF] and did make full or partial use of the theory in their empirical research. In order to observe modifications, adaptations and integration of external variables with UTAUT by adopting an approach from the research of [START_REF] Legris | Why do people use information technology? A critical review of the technology acceptance model[END_REF], this study also analyzed 43 studies to identify the external variables, and external theories they used along with UTAUT.

A further and more detailed analysis of 43 studies (that have used UTAUT) was conducted which revealed that only 27 studies used the quantitative research method (similar to the originating article) and therefore subjected to the meta-analysis. The remaining 16 studies used UTAUT with some different research methods (for example qualitative or other statistical measurement technique) and hence was not considered for meta-analysis. Although 27 studies seem relatively fewer in counts, it was considered adequate in number for conducting the meta-analysis. A similar number of studies were also utilized in previous meta-analyses research [START_REF] Deng | A multi-group analysis of structural invariance: an illustration using technology acceptance model[END_REF][START_REF] Legris | Why do people use information technology? A critical review of the technology acceptance model[END_REF][START_REF] Ma | The technology acceptance model: a meta-analysis of empirical findings[END_REF]. For example, [START_REF] Legris | Why do people use information technology? A critical review of the technology acceptance model[END_REF] successfully conducted meta-analysis on TAM by extracting statistical data from three studies. [START_REF] Ma | The technology acceptance model: a meta-analysis of empirical findings[END_REF] employed data from 26 empirical papers and [START_REF] Deng | A multi-group analysis of structural invariance: an illustration using technology acceptance model[END_REF] collected data from 21 studies. Since these studies are published in respected peer reviewed journal, we considered 27 studies as an appropriate number for this research.

Adopting approach from previous meta-analysis studies [START_REF] Deng | A multi-group analysis of structural invariance: an illustration using technology acceptance model[END_REF][START_REF] King | A meta-analysis of the technology acceptance model[END_REF][START_REF] Legris | Why do people use information technology? A critical review of the technology acceptance model[END_REF][START_REF] Ma | The technology acceptance model: a meta-analysis of empirical findings[END_REF], the following types of data were collected from 27 studies for the purpose of meta-analysis: reliability of the constructs (Chronbach's α), sample size, correlation coefficient, and overall variance explained (or adjusted R2).

Meta-analysis is a statistical method by which information from individual studies is assimilated [START_REF] Field | Meta-analysis of correlation coefficients: a Monte Carlo comparison of fixed-and random-effects methods[END_REF]. Aforementioned summary data from each study applied to calculate an effect size for the study. An effect size is a number that exhibits the extent of the affiliation between two variables. The p-value is often used as a surrogate for the effect size, with a considerable p-value taken to entail a significant effect and a non-significant p-value taken to imply a marginal effect (Borenstein cited in Cooper et al. 2009). Once the mean effect size has been computed, it can be articulated in terms of standard normal deviations (a Z score) by dividing it by the standard error of the mean. A significance value (i.e. the probability, p, of obtaining a Z score of such magnitude by chance) can then be calculated. Alternatively, the magnitude of the average effect size can be deduced from the boundaries of a confidence interval constructed around the mean effect size [START_REF] Field | Meta-analysis of correlation coefficients: a Monte Carlo comparison of fixed-and random-effects methods[END_REF]. Metaanalysis is used as a way of trying to establish the true effect sizes (i.e. effect sizes in a population) by combining effect sizes from independent studies. There are two ways to conceptualise this process: fixed effect and random effect models. In reality, the random effect model is probably more realistic than the fixed effect on widely held occasions especially when the findings are not restricted only to those studies included in the meta-analysis but used to make general conclusions about the research domain [START_REF] Field | Meta-analysis of correlation coefficients: a Monte Carlo comparison of fixed-and random-effects methods[END_REF]). Considering the above facts, this study has also made use of random effect model for the meta-analysis.

FINDINGS

Demography of Citations

Citations by Source . MIS Quarterly emerged as the leading journal with the largest number of citations (C=36) followed by Lecture Notes on Computer Science as the second most published outlets with 30 citations, this followed by other leading journals such as: Information & Management (28 citations), Computer in Human Behavior (27 citations), European

Theories, Models and External Variables Used with UTAUT .

The aim of this aspect of our analysis is to identify external variables, external theories, and the relationship of external variables with the independent and dependent constructs of UTAUT for all 43 studies which have used UTAUT.

Use of External Theories .

Table 1 lists the seven out of the 43 UTAUT-based studies that used external theories in their research model analyses. Our analysis reveals that TAM is the most frequently used theory alongside UTAUT -being utilized on four occasions, followed by Task Technology Fit (TTF) twice, and one instances each of IDT, and SCT. The findings from our external variables analysis reveal that only 22 out of 43 studies have used external variables in their investigations. The remaining 21 used only the original constructs of UTAUT. Although age, gender, experience, and voluntariness of use are moderating variables in the original UTAUT [START_REF] Venkatesh | User acceptance of information technology: Toward a unified view[END_REF], these moderators are treated as external constructs in some of the studies. Attitude, anxiety, trust, self-efficacy, PEOU, PU, perceived risk, and perceived credibility are some of the most common external variables employed. Studies which did not use external variables indicated that they were applying the original theory without altering it to achieve their objectives. Task Technology Fit

Table 1. Summary of External Theories

Reference

The analysis of relationship amongst UTAUT constructs and external constructs (as listed in Table 2) reveals that attainment value, utility value, trust, attitude, perceived ease of use, perceived usefulness, computer self-efficacy, gender, perceived risk, income, and experience have a significant impact on behavioral intention (BI). However, anxiety, training, age, perceived credibility, and social isolation do not have a significant impact and self-efficacy, subjective norm, and objective norm have mixed influence. Furthermore, trust, belief and credibility have a significant and mixed impact on performance expectancy (PE). Similarly, computer anxiety, computer self-efficacy, resistance to change, and relevance have positive impact while credibility has a non-significant impact on effort expectancy (EE). Conversely, social influence (SI) is negatively impacted by credibility. Nevertheless, IT knowledge has a positive impact on facilitating conditions (FC). As far as intention to use (IU) or usage (U) is concerned, it is impacted positively by variables from tasktechnology fit models, and experience but impacted insignificantly by trust, and internet experience. Apart from these external constructs, income has been shown as a moderating variable on BI.

Meta-Analysis

This section aims to investigate the corresponding relationships between constructs, measure their average reliability, the combined correlations between the constructs and their significance, and the major limitations of the studies. The findings from detailed analyses are presented in the following sub-sections.

Sample Size.

Table 3 illustrates the sample sizes from 27 studies that used UTAUT. Two studies by [START_REF] Yogesh | Monitoring the PACS Implementation Process in Large University Hospital-Discrepancies between Radiologists and Physicians[END_REF] and [START_REF] Laumer | Do as your parents say?-Analyzing IT adoption influencing factors for full and under age applicants[END_REF] have used more than one representative sample to present and compare cases in different scenarios. Chiu and [START_REF] Chiu | Understanding Web-based learning continuance intention: The role of subjective task value[END_REF] 286

Relationships between UTAUT Constructs. Table 4 represents the relationships between UTAUT constructs in terms of significant, non-significant, and not applicable categories. The category is specified as not applicable when the relationship between the constructs are not talked off at all may be because of the obvious reason of the study being qualitative or partially discussing the correlation where the relationship in question is not taken into consideration in that particular study. A number of studies have fallen in the 'not applicable' category because correlations between the constructs were not specified in such studies. Moreover, there is relatively a very few number of relationships categorised within non-significant category. Performance expectancy shows the highest number of significant relations with behavioral intention, followed by social influence, effort expectancy, and facilitating conditions.

Although relatively a larger numbers of studies have shown significant impacts of facilitating conditions on usage (as per the original model of UTAUT), there are still some studies which have analyzed impact of facilitating conditions on behavioral intention as well. Moreover, only eight out of 43 studies have shown the relationship between behavioral intention and usage. Reliability of UTAUT Constructs.

Table 5 lists Chronbach's alpha (α) values for indicating reliability of UTAUT constructs across all such studies (18 studies) which have used this theory. Two of the studies have got more than one set of reliabilities because they have been applied to two different set of samples. The remaining nine studies [START_REF] Alapetite | Acceptance of speech recognition by physicians: A survey of expectations, experiences, and social influence[END_REF][START_REF] Yogesh | Monitoring the PACS Implementation Process in Large University Hospital-Discrepancies between Radiologists and Physicians[END_REF][START_REF] Hung | User Acceptance of E-Government Services[END_REF][START_REF] Jong | Student Acceptance of Web-based Learning System[END_REF][START_REF] Lin | Learning online social support: An investigation of network information technology based on UTAUT[END_REF][START_REF] Sapio | Building scenarios of digital television adoption: a pilot study[END_REF][START_REF] Shin | Towards an understanding of the consumer acceptance of mobile wallet[END_REF][START_REF] Tsai | Understanding learning behavior using location and prior performance as moderators[END_REF][START_REF] Van Biljon | Cultural Factors in a Mobile Phone Adoption and Usage Model[END_REF] did not provide Chronbach's alpha (α) values for any of its constructs. As per Santos (1999), Chronbach's (α) determines the internal uniformity or average correlation of items in a survey mechanism to measure its reliability. Alpha coefficient varies in the value from zero to one and may be employed to explain the reliability of factors obtained from dichotomous and/or multi-point designed opinion or dimension. The higher the value, the more trustworthy the created dimension is [START_REF] Santos | Chronbach's Alpha: A Tool for Assessing the Reliability of Scales[END_REF]. [START_REF] Nunnaly | Psychometric theory[END_REF] pointed out 0.7 to be a standard reliability coefficient. So, the average reliability for each construct more than 0.7 (as illustrated in table 5) indicates that all of them are falling under the acceptable reliability levels. Figure 1 shows the original UTAUT theory with all its key constructs and their corresponding relationships. The combined correlation of the constructs has been shown in the figure with respect to the individual -values of the constructs. This combined effect of correlation has been calculated using the comprehensive metaanalysis software. The inputs given to the application for each construct were their individual βvalues and sample size from the specific studies. The combined zero-order correlations for each pair of constructs indicate the significant relationship between them. The R2-value for BI and Usage has been calculated taking the average of R2values of all the studies and found to be significant as well. Hence, the general concept of the relationship between the constructs is consistent with the original theory of UTAUT [START_REF] Venkatesh | User acceptance of information technology: Toward a unified view[END_REF].

Table 6 shows the correlation results for the five relationships for all its constructs [START_REF] King | A meta-analysis of the technology acceptance model[END_REF]) calculated through comprehensive meta-analysis software. The result shows that PE-BI and BI-U are relatively strongly correlated than the other relationships. The p-values across all the relationships indicate that the correlations between constructs are significant and hence consistent with the original theory of UTAUT. The 95% confidence interval also supports the correlation values and likelihood of these values to fall in the given interval.

Major Limitations of Studies that have Utilized UTAUT

Based on some of the common limitations encountered frequently across most of the studies, Table 7 has categorized limitations from individual studies into nine broader categories. Out of 43 studies, there are nine such papers which have not listed any limitations and Table 7 has listed them in a separate category called 'No Limitations Specified'. A considerable number of the studies have mentioned single IS, single subject, or cross-sectional studies as their major limitations. However, there are some studies which have referenced their limitations as: small number of samples, no application of actual usage, self-selection bias, and specialized single task difficult to generalize.

DISCUSSIONS

The findings regarding the relationships between the constructs of UTAUT in Table 8 signify that most of the relationships exist between the constructs are consistently significant. These findings are in accordance with the findings of [START_REF] Venkatesh | User acceptance of information technology: Toward a unified view[END_REF] in the original paper of UTAUT. However, there are still few relationships which are non-significant in nature and require further attention. Moreover, the relationship between FC and BI needs a further research consideration as most of the studies find this relationship as significant which is in disparity with original composition of the UTAUT [START_REF] Venkatesh | User acceptance of information technology: Toward a unified view[END_REF].

But, the findings of the combined effect of the overall relationship between the constructs demonstrated in Table 6 and Figure 2 measured through the meta-analysis software were found to be positive and significant. The comparison of construct correlations from the original theory and the combined effect is presented in Figure 2 below. The comparison of the correlation values between the constructs from the original theory and the combined effect shows that the direct effects of PE and the direct effects and interaction terms from EE, SI, FC, and BI have got the larger values in original model than the model obtained from the meta-analysis. Although, all relationships appear to be significant in meta-analysis, a lower correlation coefficient (β) suggests less consistent performance of UTAUT in various contexts. This may be due to relatively less number of studies used in for the meta-analysis and the absence of moderators in majority of studies that have utilized the UTAUT model. Any future research wishes to apply UTAUT should apply it in its original form that means effect of moderating variables must not be excluded from the model.

The combined R 2 -values are computed as an average of all the studies used for meta-analysis as the comprehensive meta-analysis tool does not provide the option for such input and it is found that the R 2 -values for both BI and U fall between the original theory's [START_REF] Venkatesh | User acceptance of information technology: Toward a unified view[END_REF] value range of 'direct effects only' and 'direct effects and interaction terms values' and hence acceptable.

The findings for the average combined reliability through Chronbach's (α) in Table 5 represent that all the constructs except FC (with acceptable reliability of 0.735) are greater than or equal to 0.8, and therefore, highly reliable and these findings are similar to TAM meta-analysis findings measured by [START_REF] King | A meta-analysis of the technology acceptance model[END_REF] across the various studies where they also considered the reliability of 0.8 as highly reliable. The findings regarding the limitations for the studies specified in Table 7 are in line with one used in [START_REF] Lee | The Technology Acceptance Model: Past, Present, and Future[END_REF] for TAM studies' limitations and open up a further scope of future research in those areas. Most of the studies are analyzed either along single IS, single subject, or single task category where they are applicable only to a specific area of research. Hence, a serious research agenda is to make them compatible with the generic scenarios. Although most of the studies are cross-sectional in nature, it becomes a potential limitation for them sometimes as they do not provide a clear picture of adoption of the technology. [START_REF] Lee | The Technology Acceptance Model: Past, Present, and Future[END_REF] argue that users' intent and opinion may change over the period of time and hence it is required to assess the adoption behavior at various points over longer period of time [START_REF] Lee | The Technology Acceptance Model: Past, Present, and Future[END_REF].

CONCLUSIONS

• The following significant points emerged from the findings and discussions presented in this study are: • A number of empirical studies that utilized UTAUT were based on relatively very small sample size.

• There is an increasing trend of using external variables and external theories together with the UTAUT.

• Relationships between external variables along with UTAUT constructs and behavioral intention were generally reported as significant or mixed significant.

• Reliability of UTAUT's survey instrument was found consistent in all citations that have utilized it.

• The overall effect of zero-order correlations between independent and dependent constructs of UTAUT was found significant.

The findings from this study also have relevance for technology adoption and diffusion research. The refined model of UTAUT based on meta-analysis that presented within this report may contribute to the area of IS/IT adoption and diffusion research as it raises many vital points related to the original model. For example, those studies that are utilizing UTAUT model generally ignore the effect of moderating variables which might be distorting the actual performance of the theory. It also highlights issue of integrating external variables without giving strong and logical justifications, for example, some studies have utilized both usefulness and performance expectancy and others have utilized both effort expectancy and ease of use. These constructs are essentially similar in nature and should not be employed in same study to avoid repetitions and redundancy.

Limitations and Further Research Directions

The first limitation is that some studies could not be taken into consideration because of the lack of privileged access rights for some journals such as European Journal of Information Systems, and Journal of Information Technology. The future researchers can look for the access more such UTAUT related studies in order to more accurately explore the meta-analysis. Secondly, the study does not take into consideration the analysis of moderating variables and their impacts on the constructs. The researchers can more elaborately analyze the effect of moderators such as age, gender, experience, and voluntariness of use on the relationships of the constructs to get more effective outcome. Thirdly, the combined R 2 -value for BI and U has not been computed using the meta-analysis tool as they do not have any such option for generating the combined effect for the same and hence represented just by taking the average of individual R2-values. The future researchers need to explore some more appropriate method to compute the combined R 2 -value through some specific software tool or relevant statistical measurements. Fourthly, the study has not considered the structural relationship between UTAUT constructs where majority of researchers have been more fascinated about it as it helps them explicate individuals' adoption of new technologies, than in the zero-order correlations [START_REF] King | A meta-analysis of the technology acceptance model[END_REF]. This research could have been made more generalized by incorporating the structural relationships between the constructs where most of the researchers are more interested about. Lastly, this study has not considered qualitative studies as a potential candidate for meta-analysis as [START_REF] King | A meta-analysis of the technology acceptance model[END_REF] have done in the meta-analysis of TAM studies. The future research can also explore this kind of meta-analysis to be included along with quantitative one.

  : Innovation Diffusion Theory; SCT: Social Cognitive Theory; TAM: Technology Acceptance Model; TAM2: Extended TAM; TTF: Task Technology Fit Use of External Variables .

Table 5 .

 5 UTAUT constructs with reliabilities [*NS=Non-Significant] (adapted from King and He 2006) and Pearson (2009) 0.929 0.905 0.821 0.0825 0.895 -------Aggelidis and Chatzoglou (2009) ---------------------0.890 ------------------------0.870 ----------------------[START_REF] Zhou | Integrating TTF and UTAUT to explain mobile banking user adoption[END_REF] 0.866 0.864 0.846 0.833 --------0

Fig. 1 .

 1 Fig. 1. UTAUT Constructs Combined Correlation with *p<0.05 (model adapted from Venkatesh et al. 2003)

Fig. 2 .

 2 Fig. 2. Construct correlations and R2 from original theory and combined effect (Model adapted from Venkatesh et al. 2003; Data Source: Venkatesh et al. 2003;Comprehensive Meta-Analysis Software)

Table 2 .

 2 Table 2 lists only those studies which used external variables. Summary of External Variables

	Reference	External Variables
	Abu-Shanab and Pearson (2009)	Self-Efficacy, Anxiety, Perceived Trust, Perceived Risk, Personal Innovativeness, Locus of Control
	Aggelidis and Chatzoglou (2009)	Attitude, Self-efficacy, anxiety, Perceived usefulness, Ease of use, Training
	Chiu et al. (2010)	Trust, Past Transactions, Gender, Age, Internet Experience
		Computer Self-Efficacy, Attainment Value, Utility
	Chiu and Wang (2008)	Value, Intrinsic Value (Playfulness), Social Isolation, Anxiety, Delay in Responses, Risk of Arbitrary
		Learning
	Curtis et al. (2010)	Voluntariness of Use, Anxiety, self-efficacy
	Dadayan and Ferro (2005)	Compatibility, Computer Anxiety, Computer Attitude, Acceptance Motivation, Organizational Facilitation
	He et al. (2007)	Individual Innovativeness, Compatibility, Task Technology Fit
	Jong and Wang (2009)	Attitude, Self-efficacy, Anxiety
	Kijsanayotin et al. (2009)	Voluntariness, Experience, Knowledge
	Laumer et al. (2010)	Subjective Norm, Objective Norm
	Lin and Anol (2008)	Online Support Expectancy, Online Social Support
	Loo et al. (2009)	Perceived Credibility, Anxiety
	Luo et al. (2010)	Trust Belief, Perceived Risk, Self-Efficacy, Disposition to Trust
		Result Demonstrability, Computer Self-Efficacy,
	Nov and Ye (2009)	Computer Anxiety, Resistance to Change, Screen
		Design, Relevance, Terminology
	Schaupp et al. (2010)	Optimism Bias, Trust of e-file system, Perceived Risk
	Shin (2009)	Trust, Self Efficacy, Perceived Security

Table 3 .

 3 Sample Sizes

	Reference	Sample Size Reference	Sample Size
	van Dijk et al. (2008)	1225	Nov and Ye (2009)	271
	Kijsanayotin et al. (2009) 1187	Schaupp et al. (2010)	260
	Abu-Shanab and Pearson	878	Laumer et al. (2010)	255
	(2009)
	Tsai (2009)	759	Zhou et al. (2010)	250
	Al-Gahtani et al. (2007)	722	Wang and Shih (2009)	244
	Jong and Wang (2009)	606	Hung et al. (2007)	233
	Chiu et al. (2010)	412	Sapio et al. (2010)	181
	Curtis et al. (2010)	409	Chang et al. (2007)	140
	Duyck et al. (2010)	362	Luo et al. (2010)	122
	Aggelidis and Chatzoglou	341	Gupta et al. (2008)	102
	(2009)
	Lin and Anol (2008)	317	van Biljon and Kotze (2008) 57
	Ye et al. (2008)	306	Alapetite et al. (2009)	39
	Shin (2009)	296	Huser et al. (2010)	18

Table 4 .

 4 Relationships with UTAUT constructs (approach adapted from[START_REF] Lee | The Technology Acceptance Model: Past, Present, and Future[END_REF]

	Relationship Type	PE BI	EE BI	SI BI FC BI	FC U BI U
	Significant	25	19	22	9	14	8
	Non-Significant	0	5	3	2	2	0
	Not Applicable	18	19	18	32	27	35
	Total	43	43	43	43	43	43

Table 6 .

 6 Summary of Zero-Order correlations between UTAUT constructs (approach adapted from King and He 2006; Source: Comprehensive Meta-Analysis Software 1 )

	Statistical Measurement PE BI	EE BI SI BI FC U BI U
	Number of Studies	8	8	10	8	3
	Total Sample Size	4170	4170	4453	1846	1990
	Average (β)	0.343	0.140	0.231	0.165	0.405
	Z-value	21.699	2.201	4.945	7.103	4.097
	p (Effect Size)	0.000	0.028	0.000	0.000	0.000
	95% Low (β)	0.231	0.015	0.141	0.120	0.221
	95% High (β)	0.446	0.261	0.317	0.209	0.562

Table 7 .

 7 Summary of limitations of UTAUT studies (approach adapted from[START_REF] Lee | The Technology Acceptance Model: Past, Present, and Future[END_REF]

	Limitation	Papers (#)	Explanation	Examples
	Self-Reported Usage	4	Does not assess the actual usage	Hung et al. (2007)

Comprehensive Meta-Analysis Software: http://www.meta-analysis.com

AUTHOR BIOGRAPHIES

Yogesh K. Dwivedi is a Senior Lecturer in Information Systems and E-Business at the School of Business and Economics, Swansea University, Wales, UK. He obtained his PhD and MSc in Information Systems from Brunel University, UK. He has coauthored several papers which have appeared in international referred journals such as