Qi Li

Kecheng Liu
email: k.liu@henley.ac.uk

Interactive Data Visualisation: Facilitate the Accountability Disclosure through the Lens of Organisational Semiotics

Keywords: Interactive data visualisation, Organisational Semiotics, Pragmatics and visualisation

, is to propose a framework leading the design of interactive data visualisation procedure on the scope of charity reporting practice. It firstly interprets the impact of 'display and interaction' of data visualisation on both semantic and pragmatic levels of data signification, based on the theory of semiosis triangle and semiotic ladder. It then develops a visualisation framework for dividing the information into different degrees of detail and linking each information points to a user's intention. Finally, the developed framework will be applied to a visualisation prototype relating to Age UK.

Introduction

Data visualisation, a set of activities to present data or information with the aid of visual representatives, such as a table, map, diagram and chart, allows data to become sense-making in front of various readers [START_REF] Lindquist | Surveying the World of Visualization[END_REF]. Strecker [START_REF] Strecker | Data Visualization in Review: Summary[END_REF] points out that data visualisation looms as a tool which offers opportunities to transform and display data and information, for the purpose of 'capturing and addressing' complexity of dataset. Segel and Heel [START_REF] Segel | Narrative Visualization: Telling Stories with Data[END_REF] point out that an interactivity on data opposed to the traditional static visual display enables readers to filter information according to their reflective interpretation. With the development of cloud computing techniques and data intelligence, interactive data visualisation can be accessed through multi-devices, where the reader can customise content and layout of data dashboard in terms of ad-hoc information requests. To some extent, to compare with traditional 'visual display', the 'interactive' of data visualisation not only enables data to be sense-making, but also fulfils a wide range of information demands from different readers.

Referring to the definition from the Charity Act [START_REF] Ellington | Using multiple imputation to estimate missing data in meta-regression[END_REF], charity can be defined as the organisation which is established for a charitable purpose and serve the social public with charitable activities. There are 165,231 registered charities in England and Wales, which earn the income of 69.49 billion pound in 2015 [START_REF]Charity Commission: Charity Overview[END_REF]. However, Charities have been suffering from low transparency because of insufficient disclosure, since the traditional charity annual report cannot fulfil the information needs from multiple stakeholders was spotted since 1990s [START_REF] Hyndman | Contributors to charities -A comparison of their information needs and the perceptions of such by the providers of information[END_REF][START_REF] Hyndman | Charity Accounting -An Empirical Study of The Information Needs of Contributors to UK Fund Raising Charities[END_REF]. Connolly [START_REF] Connolly | The Accountability Mechanisms and Needs of External Charity Stakeholders[END_REF][START_REF] Connolly | Accounting Narratives : The Reporting Practices of British Charities[END_REF] argues that because of the inadequate efficiency on the charity reporting practice, charities tend to be increasingly 'business-like' -over-emphasise the financial information, such as 'annual income', instead of describing the performance and impact of charitable activities. Influenced by the scandals that happened in the charities section around 2000s, some scholars further discuss that without efficient and understandable reporting practice, it is likely for the social public to gradually lose their 'trust' on charities [START_REF] Dhanani | Accountability of UK charities[END_REF]. Although a series of empirical studies proved that the reporting practice tends to be improved over a period years, since the pressure of regulation and public supervision, lack of awareness, skills and resources still prevent the information delivery of UK charities [START_REF] Hyndman | Charity Accounting -An Empirical Study of The Information Needs of Contributors to UK Fund Raising Charities[END_REF][START_REF] Connolly | UK charity accounting: An exercise in widening stakeholder engagement[END_REF][START_REF] Dhanani | Non-governmental Organizational Accountability: Talking the Talk and Walking the Walk?[END_REF]. Hyndman and McConville [14] and Charity Commission [START_REF]Charity Commission: Transparency and Accountability[END_REF] criticise that the transparency of charity operation would be constrained since the reporting practice cannot enable the social public to understand 'where did a charity raise money' and 'how did they use money'.

For the purpose of facilitating the information delivery of charities and enhancing the transparency, the concept of accountability was proposed in the 1970s, which indicates 'be awarded of its conducts and stakeholder's information needs in terms of what has done, is doing and plans to do' [START_REF] Hyndman | Contributors to charities -A comparison of their information needs and the perceptions of such by the providers of information[END_REF][START_REF] Dhanani | Accountability of UK charities[END_REF][START_REF] Stewart | The role of information in public accountability[END_REF]. In contrast to the research above, this paper will improve the accountability disclosure in the charity reporting practice with the aid of technical support -interactive data visualisation, for the purpose of enhancing the performance of charity reporting practice in terms of understanding, accessibility and interactivity (Section 2). Organisational semiotics, a theory revealing the procedure where information transfers from one party to another on the context of business, will be applied to lead the design of interactive data visualisation (Section 3). Instead of purely exhibiting data into 'fancy pictures' [START_REF] Strecker | Data Visualization in Review: Summary[END_REF], this paper will follow the principle of organisational semiotics, regarding data visualisation as a 'procedure' where data should be categorised and displayed in different layers in terms of 'degree of detail' (Section 4). Age UK, one of the leading charities, will be selected as a case study to demonstrate how the framework aided their business reporting in Section 5. In the final section, a brief summary and a few suggestions will be provided to guide the following studies to progress the research further.

Interactive Data Visualisation: Basic Concept and Principle

Information-overload intensifies the complexity of datasets, which causes the static data visualisation to no longer able to fulfil the diversified information demands from different stakeholders [START_REF] Few | Now You See it: Simple Visualization Techniques for Quantitative Analysis[END_REF]. The function of 'interaction', where readers can customise the content and layout of dashboards, and interpret data from their ad-hoc perspective. The development of cloud computing enhances the interaction of data visualisation where readers can drill down the dataset into a detailed extension. In addition, the presentation of data visualisation can be assessed by multi-devices, including PC, tablet and even smart phone [START_REF] Segel | Narrative Visualization: Telling Stories with Data[END_REF]. Aligned with business intelligence, interactive data visualisation can address the complexity of dataset and answer the ad-hoc requests from different stakeholders [START_REF] Rouhani | Review Study: Business Intelligence Concepts and Approaches[END_REF], which will be utilised for assisting the accountability disclosure in charity reporting practice. However, without a proper design and clear definition, data visualisation cannot automatically signify data and reveal the meaning and story behind it. Few [START_REF] Few | Now You See it: Simple Visualization Techniques for Quantitative Analysis[END_REF] indicates that data visualisation is more than just simply displaying the raw data with visual representatives, but a certain procedure which enables data to be sense-making and to fulfil the information needs from various readers. Schoffelen et al. [START_REF] Schoffelen | Visualising things: Perspectives on how to make things public through visualisation[END_REF] emphases that compared with displaying data in numeric format, the means of data visualisation to enhance the readability of dataset, where visual representatives attract people's attention with different colours and shapes, enabling sense-making in different layers of detail, and facilitating the reflecting interpretation with interactive functionalities. Ware [START_REF] Ware | Information Visualization: Perception for Design[END_REF] categorises two methods for visualising data, including 'button-up' which perceives the information through observing the patterns shown in the visualisation, and 'top-down' which checks over the data visualisation with pre-defined requests. Two approaches of designing data visualisation procedure is mentioned in Segel and Heer [START_REF] Segel | Narrative Visualization: Telling Stories with Data[END_REF], 'author-driven' where the authors predefine the content, format and layout of information presentation and 'reader-driven' where the readers can explore the whole dataset based on the ad-hoc information needs. In 2015, stemmed by the theory of organisational semiotics, Liu and Tan [START_REF] Liu | Semiotics in Visualisation[END_REF] suggest to think of data visualisation as a procedure of 'abduction', where users can search for explanation, generate hypothesis, testify hypothesis, instruct their following behaviour based on the analysis result. This research focuses on display and interact with data which enables users to perform information processing capabilities.

This research puts a focus on the 'author-driven' approach to improve the interoperation between authors and readers towards data visualisation. This approach advances the research outcome from Liu and Tan [START_REF] Liu | Semiotics in Visualisation[END_REF] in terms of building interactive capabilities around data while they are presented.

Organisational Semiotics to Data Visualisation

Semiotics is a study of sign which carries information from one party to another.

Organisational semiotics shows that information delivery with an organisational and social system should not rely on technology alone and the impacts of procedure and interaction should not be neglected [START_REF] Stamper | Information, Organisation and Technology: Studies in Organisational Semiotics[END_REF]. Liu and Tan [START_REF] Liu | Semiotics in Visualisation[END_REF] suggest under theory of organisational semiotics, data visualisation should be defined as a procedure which signifies data on both semantic and pragmatic levels, covering collecting data, transforming data, mapping visual representatives and displaying (interaction). Instead of discussing displaying visual representatives, this paper will discover how interactive data visualisation signifies data on the semantic level, and enables readers to address their ad-hoc requests and to interpret the author's intentions from different perspectives. Thus, in this section, two theories in organisational semiotics, Semiosis and Semiotic Ladder, will be discussed to inspire the following findings with theoretical supports.

Semiosis

Theory of semiosis was derived from the work of Peirce in the 1930s, which demonstrates the procedure of information delivery among different parties into a triangle framework [START_REF] Liu | Organisational Semiotics for Business Informatics[END_REF]. Sign, plays the primary role in this framework which carries information through a physical token, such as written words, graphics and oral language. Object indicates the meaning and information which a sign indicates or links to. In order to link a sign to an object, every individual needs to go through the process of 'interpret', which is more than interpreting the information carried by sign, but also the author's intensions and reflections/impacts on the reader's behaviour. Therefore, both the quality of sign and principles of interpret would influence the information delivery and interpretation. Data visualisation, as a procedure of information signification and interpretation, can also be demonstrated into the framework of semiosis (Table 1). Visual representatives can be regarded as signs which carries information. On the context of business, the financial or operational performance always counts as object which should be revealed to stakeholders. Different from the previous research which stress design of visual representatives and algorithm, this paper will focus more on the procedure of interactive data visualisation where not only the information would be revealed with the help of a visual representative, but readers will also be empowered to interact with data on the function of interaction. In summary, the semiosis triangle demonstrates the procedure of information delivery as an information pathway going through three points, including sign, interpret and object. Moreover it leads this paper to focus on the procedure of interpretant, where the authors and readers can achieve communications in terms of information and intentions. In the following part the process of sense-making 'interpretant' will be discussed further, within the body of the semiotic ladder.

Perception towards Information

Following the semiosis triangle from Peirce, Stamper [START_REF] Stamper | Semiotic Theory of Information and Information Systems[END_REF] further interprets the procedure of 'interpretant' in semiotic ladder, for the purpose of identifying and removing the barrier of signifying different signs. The procedure of 'interpretant' covers more than just interpretation, but a set of activities, from perceiving the sign from a physical aspect to instructing the following behaviour. Inheriting the research from the traditional semiotics where divisions of syntactic, semantic and pragmatic to indicate grammar, meaning and usage of sign, semiotic ladder extends the basic levels of 'physical world' and 'empiric' to measure the quality and functions of sign, and the premium level of 'social world' to imply the influence of social context [START_REF] Stamper | Information, Organisation and Technology: Studies in Organisational Semiotics[END_REF]. Table 2 provides all six levels and their detailed explanations.

Table 2. Explanations of Semiotic Ladder [22]

The functions of 'sign' are mainly related to the semantic, pragmatic and social levels. Following the study of Liu and Tan [START_REF] Liu | Semiotics in Visualisation[END_REF] where data visualisation should be regarded as a procedure to reveal the meaning of data and intentions from authors, this research will enhance the information further, to interpret the procedure of data visualisation on semantic and pragmatic levels, and develop a framework to enhance the communication in terms of the meaning of data and even information needs from readers. On the semantic level, compared with the traditional annual report of charities, data visualisation signifies data with visual representatives. On the pragmatic level, the function of 'interactive' allows readers to explore the data based on their information demands.

In charity reporting practice, although previous research points out that the traditional annual report cannot fulfil the information needs, they have never tried to solve the problem with technical solutions. In this research, interactive data visualisation will be utilised as a tool to enhance the performance of charity reporting and to enable the content to be understandable for all stakeholders. Also, it supports the communication between authors and readers on the level of pragmatic. On the one side, all report content will be divided into different levels in terms of the degree of detail and all information can be linked to the whole picture which demonstrates the overall performance of charity operation. On the

Levels Explanation Physical element

The durability and stability of sign Empirical element

The reliability of channel used to deliver the sign Syntactic element

Whether the language can be understood by both information senders and receivers Semantic element Whether the receiver can figure out the relationship between the sign and object Pragmatic element

Whether the intention attached to the sign can be perceived Social element

Whether the interpretation of sign can be linked to some certain social norms other side, readers can request a certain theme they are particularly interested in, instead of following the predefined routine. In the following section, a newdeveloped framework will be introduced for the purpose of leading the design of interactive data visualisation. On the semantic level, it enables data to be sensemaking; on the pragmatic level, it allows readers to follow the author's routine and reflect their ad-hoc request at the same time.

The Visualisation Framework

Data visualisation is more than displaying raw data with visual representatives, but rather a procedure to enable data to be more sense-making and empower the reader to reflectively interpret data and author's intentions through the function of 'interaction'. Following the principles of organisational semiotics, a design of interactive data visualisation should sufficiently address issues from both semantic and pragmatic levels. The visualisation framework is depicted in Fig. 1. This framework was inspired by a common challenge when a reporting system offers a presentation of data in a specific structure, in non-profit organisaitons [START_REF] Hyndman | Transparency in Reporting on Charities' Efficiency: A Framework for Analysis[END_REF]. Such challenge often affects users to easily discover business issues from the data. The framework intends to empower users to have sense making on the data through their pragmatic abilities.

Fig. 1. Framework of Visualisation Pyramid

On the semantic level, it divides the procedure into three layers in terms of the degree of detail, where authors can present all information from a low to a high degree of detail. Visual dashboard at the front layer is an interactive user interface which presents information to allow users quickly generating facts and views based on meaningful signs through colour, size and shapes of visual representations [START_REF] Ware | Information Visualization: Perception for Design[END_REF]. On the pragmatic level, interactive visualisation empowers all readers to address their different information requests based on their preference. Other than capturing the pattern of dataset from the dashboard, readers can drill down certain information themes they are particularly interested in and look for further explanations.

Table 1 .

 1 Explanation of Semiosis[START_REF] Liu | Organisational Semiotics for Business Informatics[END_REF]

	Roles	Explanation	Data Visualisation
	Sign	The physical carrier of information,	Visual representatives: chart, diagram, table
		or the raw material where the	and map
		information comes from
	Object	The meaning of sign, which will	Financial and operational performance:
		influence the receiver's	cost-efficiency, customer satisfaction,
		understanding and even behaviours	market growth
	Interpretant A mechanism or a set of activities	Understand the definition of each variable
		to process the raw material to the	in the visual presentation; Link different
		information which fits the demand	variables to the performance measurement;
		of information users	Capture the author's intentions through
			reflecting ad-hoc requests

Age UK: a Case Study and Prototype of Interactive Data Visualisation

This study has chosen Age UK, a leading charity providing services to senior citizens, as a case study. With a reference to Hyndman and McConville [START_REF] Hyndman | Transparency in Reporting on Charities' Efficiency: A Framework for Analysis[END_REF] and Connolly and Hyndman [START_REF] Connolly | UK charity accounting: An exercise in widening stakeholder engagement[END_REF], two themes from Age UK's annual report, such as income and expenditure, are considered to be mostly interested by the readers (Table 3). Based on such requirements, this study employed the visualisation framework to design an information structure (Fig. 2) which transformed the reporting practices. It feeds the data into the defined business views and governs interactive behaviour for reflective interpretations. For example, a user (i.e. reader) can compare income with expenditure on the top level dashboard, and then look into the cost structure within the theme of expenditure, in order to find out which type of charitable activities Age UK spent money on. Furthermore, the user can choose to view more narrative contents of performance which describe how Age UK invested money to help beneficiaries in terms of their welfare.

Fig. 2. Information Structure for Visualisation

Compared with the traditional annual report, the prototype of interactive data visualisation can be utilised as a complimentary tool of charity reporting practice in Age UK. Firstly, instead of showing all contents in one report, this prototype is more flexible for readers to choose the information their interested in and get insight of the overall performance. Secondly, readers are able to drill down the information points they are interested in to a desired level of detail, rather than reading through all contents, which can fulfil their ad-hoc requests in an efficient way.

Fig. 3 exhibits all three layers of information points based on the information structure. Themes, setting and visual dashboard, present the front layer of information, which delivers a 'big picture' of financial status. On the middle layer, Definition(s), KPIs and News, provides the complimentary information, which helps readers to understand the concepts and relevant indicators. On the back layer, case studies with extensive information of beneficiary stories, can be accessed through hyperlink, which can trace back to the website and demonstrate the most detailed information in a particular expenditure category that the reader is specifically interested in.

Conclusion and Future Work

This paper identifies the research gap where UK charities always suffer from insufficient accountability disclosure since the traditional charity reporting method cannot enable all stakeholders to understand the report content and provide little flexibility for them to address their ad-hoc requests. Instructed by the theory of organisational semiotics, this paper developed the visualisaiton framework which implemented the pragmatic ability in making sense of the data via the interactive data visualization tool. This framework was experimented on the Age UK. This tool enables every single information point to be link in a holistic viewpoint of the business. The functions of the interaction meet the uses' specific information demands and view the content from different perspectives.

In addition, there are a few points where the following studies could be discussed further. Firstly, other than information of income and expenditure, more content can be fulfilled through the framework of visualisation pyramid. Especially, the study from Hyndman and McConville [START_REF] Hyndman | Transparency in Reporting on Charities' Efficiency: A Framework for Analysis[END_REF], where the information reflecting performance of charitable activities can be addressed by data visualisation, which helps identify the 'high-profile' charity in terms of their performance. Secondly, this paper interprets the procedure of data visualisation on both semantic and pragmatic levels. Referring to the framework of the semiotic ladder, further research is suggested to focus on the level of the 'social world' of interactive data visualisation, where the meanings of data (semantic level) and intentions (pragmatic level) might influence the following behaviour of readers.


Buddhi Pathak
email: b.n.pathak@pgr.reading.ac.uk

Service Innovation in Business Ecosystem: The Role of Enablers and Formation Cycle

Keywords: Service innovation, business ecosystem, S-D logic

Service innovation in business ecosystem is attracting more research interest; however, there is less insight into how the formation of ecosystem enables service innovation. The limited prior research on the formation of an ecosystem is characterized by different phases and activities associated with lifecycle of an ecosystem, however it is not connected to the service innovation processes. This work addresses this gap by proposing formation cycle of an ecosystem for service innovation.

Introduction

Service innovation in business ecosystem (SIBE) refers to the process of innovating services through combining ecosystem partners (business partner, customer and other stakeholder) and their resources (e.g., knowledge and capabilities) that not only collaborate relationship amongst ecosystem but also appropriately configures value proposition for ecosystem partners.

Various studies in service innovation (SI) and ecosystem research stress the role of resources for successful innovation [START_REF] Mention | Co-operation and co-opetition as open innovation practices in the service sector: which influence on innovation novelty?[END_REF][START_REF] Rusanen | Accessing resources for service innovation-the critical role of network relationships[END_REF][START_REF] Akaka | An exploration of networks in value co-creation: A service-ecosystems view[END_REF]. These studies show that resources are key element of successful ecosystem. Integration of resources is seen as key component of service innovation [START_REF] Gummesson | Marketing as value co-creation through network interaction and resource integration[END_REF]. Regardless of acknowledgement of the significance of resource in the formation of ecosystem for service innovation, existing research has little to say about how firms can essentially develop ecosystem with regards to service innovation.

Therefore, research opportunity arises since little knowledge is known about formation of business ecosystem for service innovation [START_REF] Thomas | Emergent equifinality: An empirical analysis of ecosystem creation processes[END_REF], their resources configuration, actor interaction, network facilitation and its management [START_REF] Rusanen | Accessing resources for service innovation-the critical role of network relationships[END_REF][START_REF] Edvardsson | A new conceptualisation of service innovation grounded in S-D logic and service systems[END_REF]. To address this research gap, this work aims to examine how firms can use resources in the formation of business ecosystem and linked that with service innovation. More precisely the paper;

 identifies the service innovation enablers; factors that support service innovation in ecosystem  suggests the formation cycle of ecosystem consisting the role of resources that is used for crating ecosystem for service innovation purpose This aim is achieved by drawing on an extensive literature on service innovation, ecosystem and resources from mainly resource based view (RBV) and Servicedominant logic (SDL) and Service Science theoretical lenses. The first Section presents literature on service innovation in business ecosystem. Service innovation enablers in ecosystem are identified in Section 3. Having suggested ecosystem formation cycle, the final Section presents further research agenda on the topic.

Service Innovation in Business Ecosystem

The service industry makes more than three quarters of major economies of the world and is seen as a key for growth and competitiveness [START_REF]OECD: Growth in Services -Fostering Employment, Productivity and Innovation[END_REF][START_REF] Ostrom | Moving forward and making a difference: Research priorities for the science of service[END_REF]. The functions and significance of SI is defined as "it introduces something new into the way of life, organization, timing and placement of what can generally be described as the individual and collective processes that relate to consumers" [START_REF] Barcet | Innovation in services: A new paradigm and innovation model[END_REF]. Additionally, specifying the types of SI, IfM and IBM [START_REF]Succeeding Through Service Innovation: A Service Perspective for Education[END_REF] defines service innovation as the combination of technology innovation, business model innovation, socioorganizational innovation, and demand innovation to improve existing or create new service value propositions (offerings or experiences) and service systems. Service Science and SDL define innovation differently than traditional approach of manufacturing. It considers the role of customer as co-creator of value, where they use resources to co-create value [START_REF] Vargo | Advancing service science with service-dominant logic; clarifications and conceptual development[END_REF]. It also defines innovation as a continuous and interactive process that involves the interaction among groups of actors who are interrelated in a dense network [START_REF] Gummesson | Marketing as value co-creation through network interaction and resource integration[END_REF]. Therefore, service perspective conceptualizes innovation as a process of joint value creation with customer and other partners based on a wide integrated network approach [START_REF] Russo-Spena | Five Co-s" in innovating: a practice-based view[END_REF].

As these definitions propose the previously ignored field e.g., network approach [START_REF] Russo-Spena | Five Co-s" in innovating: a practice-based view[END_REF] or service systems [START_REF]Succeeding Through Service Innovation: A Service Perspective for Education[END_REF] to maximize the value of innovation, it imagines the concept of ecosystem, where the value is co-created with its partners. This idea of business ecosystem was initially proposed by Moore [START_REF] Moore | The death of competition: Leadership & strategy in the age of business ecosystems[END_REF], which is defined as "an economic community supported by a foundation of interacting organizations and individuals-the organisms of the business world" [START_REF] Moore | The death of competition: Leadership & strategy in the age of business ecosystems[END_REF]. According to European Commission (EC), business ecosystem is the network of buyers, suppliers and makers of related products or services as well as the socio-economic environment [START_REF]European Commission: Digital Ecosystems[END_REF].

This concept of SIBE denotes the process of innovating services e.g., types [START_REF]Succeeding Through Service Innovation: A Service Perspective for Education[END_REF] through combining ecosystem partners. This concept of business ecosystem has been used widely. For example, European Commission (EC) has promoted the concept of ecosystem and service innovation under Euro 2020 to boost growth and jobs [15].

The concept of ecosystem flourishes from establishing relationships between business networks to the level of co-creation of value with business partners and their customers. In this process of co-creation of value in business ecosystem various factors play key role, which are termed as enablers. The following section discusses SIBE enablers.

Identification of Service Innovation Enablers in Ecosystem

The study of Tax and Stuart [START_REF] Tax | Designing and implementing new services: the challenges of integrating service systems[END_REF], Fyrberg and Juriado [START_REF] Fyrberg | What about interaction? Networks and brands as integrators within service-dominant logic[END_REF], and Vargo et al. [START_REF] Vargo | On value and value co-creation: A service system and service logic perspective[END_REF] supports the active involvement of actor in service innovation. Similarly, RBV approach to service innovation [START_REF] Mention | Co-operation and co-opetition as open innovation practices in the service sector: which influence on innovation novelty?[END_REF][START_REF] Rusanen | Accessing resources for service innovation-the critical role of network relationships[END_REF][START_REF] Vargo | From goods to service(s): Divergences and convergences of logics[END_REF] supports the view that service innovation process requires bundle of resources. The study of Vargo and Lusch, [START_REF] Vargo | From goods to service(s): Divergences and convergences of logics[END_REF], Vargo et al. [START_REF] Vargo | Advancing service science with service-dominant logic; clarifications and conceptual development[END_REF] argue that value proposition or benefit that any innovation brings must be considered for any tie ups. Similarly, work of Edvardsson and Tronvoll [START_REF] Edvardsson | A new conceptualisation of service innovation grounded in S-D logic and service systems[END_REF] demonstrates the significance of schemas in service innovation process. Thus, it is proposed that SIBE is enabled and simplified by four fundamental factors as;

 Resource capabilities: this includes all the operant i.e., skills and knowledge and operand i.e., physical resources [START_REF] Vargo | Advancing service science with service-dominant logic; clarifications and conceptual development[END_REF] required for service innovation  Actor involvement: this involve actor willingness, relationship, trust amongst innovation actors, their leadership skills and vision  Changes in schemas: these are norms and rules that guide the actor and institutionalised forces that supports innovation [6]  Value proposition: this includes for example, benefits that new offerings bring, equal distribution of value amongst service provider and customer

Capabilities are competence of resources mobilization and resources are as taken from the S-D logic both operant and operand resources that are required for service innovation [START_REF] Vargo | Advancing service science with service-dominant logic; clarifications and conceptual development[END_REF][START_REF] Vargo | From goods to service(s): Divergences and convergences of logics[END_REF]. Actors are considered as resource integrators [START_REF] Vargo | From goods to service(s): Divergences and convergences of logics[END_REF] who guide service innovation process. Changes in schemas such as to challenge status quo, norms and rules e.g., from inward R&D focus to open innovation orientation also enable service innovation [START_REF] Edvardsson | A new conceptualisation of service innovation grounded in S-D logic and service systems[END_REF]. Appropriate distribution of value amongst actors, and the benefits that new innovation brings to the ecosystem is defined as value propositions, which play key role to enable service innovation [START_REF] Vargo | Advancing service science with service-dominant logic; clarifications and conceptual development[END_REF][START_REF] Vargo | From goods to service(s): Divergences and convergences of logics[END_REF].

These enabling factors bring S-D logic to the center of service innovation concept as it introduces service-dominant thinking [START_REF] Vargo | From goods to service(s): Divergences and convergences of logics[END_REF][START_REF] Vargo | On value and value co-creation: A service system and service logic perspective[END_REF] to elaborate these factors e.g., operant resources i.e., application of skills and knowledge of actors to propose value. Proposed model also highlights the significance of social norms for service innovation, which was previously ignored [START_REF] Edvardsson | A new conceptualisation of service innovation grounded in S-D logic and service systems[END_REF].

These four factors set the foundation for firm to instigate their ecosystem. Ecosystem formation cycle is presented in the following section.

Towards Ecosystem Formation Cycle for Service Innovation

Moore [START_REF] Moore | The death of competition: Leadership & strategy in the age of business ecosystems[END_REF], Thomas and Autio [START_REF] Thomas | Emergent equifinality: An empirical analysis of ecosystem creation processes[END_REF] presented various phases of ecosystem emergence. Service science and S-D logic have extended the boundary of firm for value creation.

The significance of ecosystem is that no single firm could have created alone [START_REF] Adner | Match your innovation strategy to your innovation ecosystem[END_REF].

Based on the earlier discussion, as well as considering the theory of RBV and S-D logic this work offers six phases of ecosystem formation for service innovation. Fig. 1 provides the six different steps that are required for the firm to initiate service innovation activities within their ecosystem. The formation of ecosystem for the purpose of service innovation starts right from the instigation of ecosystem [START_REF] Moore | The death of competition: Leadership & strategy in the age of business ecosystems[END_REF] by identifying purpose of new initiative, partner, supplier, market, customer or other stakeholder who may be interested in the development of new service [START_REF] Thomas | Emergent equifinality: An empirical analysis of ecosystem creation processes[END_REF]. Ecosystem instigator needs to clarify the purpose of ecosystem that they want to build. The first step may involve the ecosystem activities identified by Thomas and Autio [START_REF] Thomas | Emergent equifinality: An empirical analysis of ecosystem creation processes[END_REF].

Fig. 1. Formation cycle of an ecosystem for service innovation

Having identified purpose, partners and market for the new development, the next step requires assessing and identifying resources which are needed for the new development. The work of Rusanen et al. [START_REF] Rusanen | Accessing resources for service innovation-the critical role of network relationships[END_REF] provides guidelines for identifying resource need for the new service offerings.

As value is created through integration of resources that is brought forwards by actors [START_REF] Vargo | Historical perspectives on service-dominant logic[END_REF], it is essential for firms to establish system/procedure that simplifies resources integration between different stakeholders. However, this step is challenging as there is less academic insight as well as danger of disrupting existing service system of a firm. It also involves cost, time and effort [START_REF] Tax | Designing and implementing new services: the challenges of integrating service systems[END_REF].

Choosing types of resources is also reliant on types of service innovation [START_REF]Succeeding Through Service Innovation: A Service Perspective for Education[END_REF]. Type of service innovation can be divided into several dimensions such as business model innovation and process innovation [START_REF]Succeeding Through Service Innovation: A Service Perspective for Education[END_REF]. Choosing one dimension over other options vary in terms of available resources and objective of ecosystem formation. Business model innovation, for example, may need more customer operant resources in comparison to organization's internal process innovation. Similarly, nature of business also affects this choice, e.g., a mining company may need more operand resource in comparison to a consulting firm. Thus, choosing resource is dependent on the nature of business and types of SI.

Similarly, resources implementation stage brings various challenge, e.g., firm needs to carefully design processes that fulfil the resources for new development. Only the right resource implementation strategy brings assurance of finishing the new development at the right time. Therefore, proper planning is required, which involves various managerial works. Last but not least, the overall process may end up producing something unique that can be achieved as a result e.g., product or services. Customer can experience the service, or they may use product. How they experience the service/use product, their feedback is valuable to improve the overall function of ecosystem. It can be improved based on market feedback, and it is continuous process, thus, it is called formation cycle.

From the ecosystem instigation to the resource implementation phase, the success of process is more dependent on the initiator of the service innovation project, as initiator is the one who wants to create new space or modified service system. The rest of the phases of formation cycle rely on the customer as they are the ones who can define new space for new offerings.

Conclusion and Future Research

This work has drawn literature on service innovation in ecosystem. It has identified service innovation enablers as well as proposed ecosystem formation cycle. By proposing such enablers and cycle, we have responded the call of Thomas and Autio [START_REF] Thomas | Emergent equifinality: An empirical analysis of ecosystem creation processes[END_REF] among others [START_REF] Rusanen | Accessing resources for service innovation-the critical role of network relationships[END_REF][START_REF] Edvardsson | A new conceptualisation of service innovation grounded in S-D logic and service systems[END_REF] The work contributed to the Service Science and SDL literature by identifying service innovation enablers and ecosystem formation cycle.

The paper also attempts to discuss the concept of ecosystem from the perspectives of Service Science and SDL. In our knowledge the concept of SDL is not applied to the ecosystem literature, and previous researches have ignored the application of SDL in ecosystem [START_REF] Thomas | Emergent equifinality: An empirical analysis of ecosystem creation processes[END_REF][START_REF] Moore | The death of competition: Leadership & strategy in the age of business ecosystems[END_REF]. This study reveals that there are many factors that enable service innovation in ecosystems and firm has to go through various stages of ecosystem formation cycle. Initiating firm's own business ecosystem needs wider attention from the management so as to better manage enabling factors and formation cycle. Proposed model could serve as a basis for studying ecosystem formation cycle.

There are various limitations of this work; the overall work needs to be empirically validated as it only offers conceptual progressions requiring practical application of the model to validate the ecosystem formation cycle. Similarly, there are various challenges associated with every step of ecosystem formation. How an ecosystem initiator overcomes these challenges that makes a difference in the success of an overall project. Therefore, resources integration as well as their implementation for the new development remain research opportunity.


Feng Jiang
email: jiangfeng@cqu.edu.cn

Min Gao
email: gaomin@cqu.edu.cn

Qingyu Xiong
email: xiong03@cqu.edu.cn

Junhao Wen
email: jhwen@cqu.edu.cn

Yi Zhang
email: cquzhangyi@cqu.edu.cn

Robust Social Recommendation Techniques: A Review

Keywords: Social recommendation, Multiple relationships, Shilling attack

Social recommendation plays an important role in solving the cold start problem in recommendation systems and improves the accuracy of recommendation, but still faces serious challenges and problems. Ratings or relationships injected by fake users seriously affect the authenticity of the recommendations as well as users' trustiness on the recommendation systems. Moreover, the simplification of relationship treatment also seriously affects the recommendation accuracy and user satisfaction to the recommendation systems. This paper first analyzes up to date research of social recommendation and the detecting technology of multiple relationships. Furthermore, it proposes a future research framework for robust social recommendations including modeling and feature extraction of multidimensional relationships, social recommendation shilling attack models based on social relationships, the analysis of the relationships in social networks as well as the roles of relationships on recommendation, and robust social recommendation approaches taking multiple relationships into consideration.

Introduction

At present, the rapid development of electronic commerce retail and the rise of social network marketing make social recommendation a research hotspot [START_REF] Li | Online Recommendation Systems in a B2C E-Commerce Context: A Review and Future Directions[END_REF]. Social recommendation refers to recommendation technology taking social relationships as additional input [START_REF] Tang | Social Recommendation: a Review[END_REF] to improve the sales of merchandise and users' satisfaction [START_REF] Li | Online Recommendation Systems in a B2C E-Commerce Context: A Review and Future Directions[END_REF][START_REF] Tang | Social Recommendation: a Review[END_REF][START_REF] Levandoski | Lars: A Location-Aware Recommender System[END_REF]. Social recommendations mainly combine users' social relationships and collaborative filtering technologies [START_REF] Liu | Progress in Personalized Recommender Systems[END_REF] to make recommendations. The social relationships between users can be combined with memory-based collaborative filtering method to find nearest neighbor or model-based collaborative filtering as an implicit vector constraints to form memory-based or model-based social recommendation. Social recommendations utilize users' similarities reflected from social relationships [START_REF] Gao | Exploring Social-Historical Ties on Location-Based Social Network[END_REF] to play an important role in solving the cold start problem in the recommendation systems and improve the accuracy of recommendations, but still face serious challenges and problems. First, shilling attacks seriously affect the authenticity of the recommendation in a social recommendation system [START_REF] Shrivastava | Mining (social) Metwork Graphs to Detect Random Link Attacks[END_REF][START_REF] Zhang | Securing Recommender Systems Against Shilling Attacks Using Social-Based Clustering[END_REF]. Second, the noise relationship will lead to recommendation failure. There are many relationships in social networks. These relationships are usually dealt with as the same, which is an important reason that explains current social recommendations did not play its due role [START_REF]IBM's black Friday report[END_REF].

Comments injected by fake users seriously affect the authenticity of the recommendation list as well as users' trust degree to the recommendation system; while the simplification of relationship treatment also seriously affects the accuracy of the recommendation list and users' satisfaction to the recommendation system. To solve these issues, the social relationship is a key factor. This paper analyzes up to date research of social recommendation and the detecting technology of interference relationships, as well as proposes a research framework to solve these issues.

Social Recommendations and Multiple Relationships Detection

In this section, we will analyze up to date memory-based and model-based social recommendation approaches, fake user detection approaches, and multiple relationship detection approaches because these approaches are essential techniques to robust social recommendation.

Social Recommendations

Social recommendations usually refer to utilizing social relationships to conduct the merchandise recommendation, conducting the recommendation of friends, microblogs, or utilizing celebrities' social influences to conduct the recommendations. The social recommendations in this paper belong to a narrowly-defined social recommendation [START_REF] Ester | Recommendation in Social Networks[END_REF]: a kind of item recommendation technique combining the social relationships. Social recommendation can be divided into memory-based one and model-based one. Memory-based social recommendations directly use ratings and directly linked users as neighbors. Rokach et al. [START_REF] Rokach | Recommender Systems Handbook[END_REF] propose a social relationships' weighed average method to find the neighbors. According to the shortest path length from the target user to all users, Golbeck et al. [START_REF] Golbeck | Generating Predictive Movie Recommendations from Trust in Social Networks[END_REF] gradually calculate the trust degree and chose those users whose trust values were greater than a threshold value as the neighbor. Jithin et al. [START_REF] Justin | Nearest Neighbour Based Social Recommendation Using Heat Diffusion[END_REF] use the theory of thermal diffusion for reference to conduct trust transfer in the social relationship networks, meanwhile combined it with traditional rating similarities. Model-based social recommendation takes the modelbased collaborative filtering as its foundation, such as the probability matrix decomposition models [START_REF] Jiang | Scalable Recommendation with Social Contextual Information[END_REF] making relationships between users reflected to preference relationships and achieved the method of social trust integration, the random walk model [START_REF] Chen | A Modified Random Walk Framework for Handling Negative Ratings and Generating Explanations[END_REF] on social relationships to deal with both positive and negative relationships under the condition of ensuring the convergence and then realize the recommendations, the Social Bayes Personalized Rank (SBPR) model [START_REF] Zhao | Leveraging Social Connections to Improve Personalized Ranking for Collaborative Filtering[END_REF] used in combination with friends' preference to predict ratings, the tensor decomposition model [START_REF] Zou | Social Recommendation Based on User Trust and Tensor Decomposition[END_REF] and the multidimensional trust relationship model [START_REF] Tang | mTrust: Discerning Multi-Faceted Trust in A Connected World[END_REF]. These social recommendation approaches utilize users' social relationships and generate effective recommendation as rating matrices are sparse or recommending cold start items. Therefore, the approach can solve the sparsity problem and the cold start problem to some extents; meanwhile improve the accuracy of recommendation. Social recommendation widely drew academia and the industry's attention, but due to a lower cost of establishing social relationships in social networks, social recommendation suffers from the problems related with interference relationships: 1) fake users who improve or lower items' recommendation probabilities by injecting fake users and relationships, which is called the social recommendation shilling attack [START_REF] Shrivastava | Mining (social) Metwork Graphs to Detect Random Link Attacks[END_REF]; and 2) The problem of noise relationship leading to recommendation failure. There are multiple noise relationships that have side-effects on the recommendation, such as temporary friendships or random friendships [START_REF] Yeung | Strength of Social Influence in Trust Networks in Product Review Sites[END_REF]. If these relationships are treated equally with friendships and colleague relationships, it will make the recommendation results be questioned to a certain extent.

Social Relationships Detection

Fake user detection. At present, the studies on fake users' interference mainly concentrate on shilling attacks detection in the traditional recommendation systems as well as the methods of detecting fake users in social networks.

In the recommendation systems, Mobasher et al. [START_REF] Mobasher | Toward Trustworthy Recommender Systems: An Analysis of Attack Models and Algorithm Robustness[END_REF] propose a supervised learning method that used the rating degree mean agreement (RDMA), Degsim, and other features training classifiers to detect fake users. Other researchers [START_REF] Lee | Shilling Attack Detection-A New Approach for a Trustworthy Recommender System[END_REF][START_REF] Cao | Shilling Attack Detection Utilizing Semi-Supervised Learning Method for Collaborative Recommender System[END_REF] put forward a non-supervised learning method based on feature clustering to detect fake users. Cao, et al. [START_REF] Cao | Shilling Attack Detection Utilizing Semi-Supervised Learning Method for Collaborative Recommender System[END_REF] raise a semi-supervised learning method which utilized a small amount of labeled data and a large amount of non-labeled data to improve the detection accuracy. Zhang et al. [START_REF] Zhang | Preventing Recommendation Attack in Trust-Based Recommender Systems[END_REF] put forward a method to improve recommendation systems' anti-shilling attacks ability by building a users' multilayered trust model. Wang et al. [START_REF] Wang | A Service Recommendation Method Based on Trustworthy Community[END_REF] raise an attack detection algorithm based on target item identification.

In social networks, fake users with the intention of spreading particular contents also can be called "shills" [START_REF] Zhang | Securing Recommender Systems Against Shilling Attacks Using Social-Based Clustering[END_REF] or "water army". The research on fake users in social networks concentrates on that detecting abnormal users based on user features or network structures. Aggarwal et al. [START_REF] Aggarwal | Detection of Spam Tipping Behaviour on Foursquare[END_REF] extract characteristics from users' registration information, published contents and other information and then built classifiers. Limsaiprom et al. [START_REF] Limsaiprom | Social Network Anomaly and Attack Patterns Analysis[END_REF] detect fake users by clustering users' source and target addresses.

In social recommendation, Zhang et al. [START_REF] Zhang | Securing Recommender Systems Against Shilling Attacks Using Social-Based Clustering[END_REF] detect fake users based on the cluster of friends' trust relationships. Shrivastava et al. [START_REF] Shrivastava | Mining (social) Metwork Graphs to Detect Random Link Attacks[END_REF] raise that detecting random link attacks by clustering social network diagrams.

Multiple relationship detection. Some research on the multiple relationship are focused on raising problems [START_REF]IBM's black Friday report[END_REF]. Liu et al. [START_REF] Liu | Progress in Personalized Recommender Systems[END_REF] believe that social recommendation did not distinguish friendships is one of reasons for its recommendation failure in some case at present. Ghosh et al. [START_REF] Ghosh | Understanding and Combating Link Farming in the Twitter Social Network[END_REF] raise that in social networks, real users might also build up unreal relationships.

In the aspect of solving the problems, Yeung et al. [START_REF] Yeung | Strength of Social Influence in Trust Networks in Product Review Sites[END_REF] introduce the concept of relationship strength, and used the method of matrix decomposition and optimization to solve the relationship strength. Tang et al. [START_REF] Tang | mTrust: Discerning Multi-Faceted Trust in A Connected World[END_REF] analyze multidimensional trust relationships based on themes' multidimensional trust tensors and then conducted the recommendation.

Analysis and Discussion

At the time of achieving recommendation accuracy and solving the cold start problem, social recommendation is easy to receive fake users' shilling attacks and noise relationships' interference. The detection on fake users in the traditional recommendation system [START_REF] Mobasher | Toward Trustworthy Recommender Systems: An Analysis of Attack Models and Algorithm Robustness[END_REF][START_REF] Wu | Shilling Attack Detection Based on Feature Selection for Recommendation Systems[END_REF][START_REF] Li | A Metadata-enhanced Variational Bayesian Matrix Factorization Model for Robust Collaborative Recommendation[END_REF], the recommendation based on the calculation of trust degree or target items' [START_REF] Zhang | Preventing Recommendation Attack in Trust-Based Recommender Systems[END_REF][START_REF] Wang | A Service Recommendation Method Based on Trustworthy Community[END_REF] and the detection on fake users in social networks [START_REF] Aggarwal | Detection of Spam Tipping Behaviour on Foursquare[END_REF][START_REF] Limsaiprom | Social Network Anomaly and Attack Patterns Analysis[END_REF] all involved less in the problem of shilling attacks produced when social networks are combined with the e-commerce to conduct product recommendations. In current study on fake users of social recommendation [START_REF] Shrivastava | Mining (social) Metwork Graphs to Detect Random Link Attacks[END_REF][START_REF] Zhang | Securing Recommender Systems Against Shilling Attacks Using Social-Based Clustering[END_REF], it is assumed that attackers use random link attacks to establish links randomly between fake users and normal users, and if attackers use other strategies to conduct attacks, then it would be difficult to deal with. The future research should focus on robust social recommendation, four research points are shown in Fig. 1: 1) The modeling of users' social relationships is the foundation of robust social recommendation. It focuses on the study of using complex network modeling to build fake user injection strategies and shilling attacks models of social recommendation; 2) Utilize the multi-view collaborative training, probabilistic graphical model, tensor decomposition, and other technologies to form the method of probability estimation of multidimensional social relationships; 3) Aim at the cold start, hot and general item exploration to explore function mechanisms of multiple social relationships on the recommendation. The influence of relationships on different types of items can be analyzed by comparing the baseline recommendation algorithms with that by the social recommendation algorithms; and 4) Combine the multidimensional social relationship probability matrix, and finally build up the algorithm of anti-interference social recommendation to achieve a robust social recommendation system.

Conclusion

This paper started from the analysis of the study of social recommendation and existing problems. It explained the characteristic that social recommendation is easy to be attacked by bogus social relationships and be interfered by multiple social relationships. It analyzed the future research point of robust social recommendation, and proposed four aspects: 1) the users' relationship modeling and characteristic extraction faced anti-shilling attack social recommendation; 2) strategies of fake relationship injection and social recommendation shilling attack models; 3) probability estimation of users' multidimensional social relationships and function mechanism of relationship types on social recommendation; and 4) robust social recommendation approaches combined multidimensional social relationships with traditional social recommendation algorithms.

Fig. 1 .

 1 Fig. 1. The Further Research of Robust Social Recommendation

Acknowledgments. This research is supported by the Basic and Advanced Research Projects in Chongqing (cstc2015jcyjA40049), Chongqing University Young Scholar Program, the Scientific and Technological Research Program of Chongqing Municipal Education Commission (KJ121607 and KJ131603) and the Chongqing Social Science Planning Project (2010QNRW54).


Lily Sun
email: lily.sun@reading.ac.uk

Chekfoung Tan
email: chekfoung.tan@henley.ac.uk

Simon Robertson
email: srobertson@biblesocieties.org

Kecheng Liu
email: k.liu@henley.ac.uk

Mark Cook
email: mcook@biblesocieties.org

Claire Collins
email: claire.collins@henley.ac.uk

Open Digital Business Ecosystems: A Pathway for Value Co-Creation

Keywords: Open Digital Business Ecosystems, Digital Business Capabilities, Value Cloud, Affordance, Open Innovation

Individual businesses enthuse over participating in the4"We Economy" era [1] through a digital business ecosystem (DBE). DBE is seen as a gateway for an individual company to penetrate new markets where new products, services, and highly personalized experiences are delivered to their customers. Despite the benefits of DBE, collaboration uncertainty is the main challenge for a company wanting to participate in a wider ecosystem. This paper presents an open digital business ecosystem model that prepares companies prior to participating in the world of digital business and to secure multi-faceted benefits. This model demonstrates three key functionalities: 1) evaluating a company's current digital and non-digital business capabilities driven by its vision, 2) capturing these capabilities in a value cloud register, and 3) integrating the business capabilities determined by innovative collaborations. A case study of a global non-profit organization is employed to demonstrate how a company connects its partners in an innovation ecosystem.

Introduction

Digital business has become a strategic transformation paradigm for a company to redefine its business model in a business network. Such a business network also redefines its environment as digital business ecosystems (DBE) which connect multiple companies to deliver products and customer experience as well as co-create values in a win-win-win fashion, a Business win, a Customer win, and an Innovation win [modified from 1]. However, new business environments impose collaboration uncertainties between the focal company and its lead partners, such as ineffective collaboration that affects the co-development of products or services and ill-aligned business capabilities that impact on achieving win-win-win innovation values. Hence, it is vital for the stakeholders to recognize these uncertainties prior to participation in the DBE.

There is active research on evolving from a traditional business model to a networked digital business. This kind of business transformation reveals that the ecosystem approach has been incorporated for sharing vision and integrating other partners in the ecosystem from the business and IT perspective such as incorporating open data and the cloud computing approach [adapted from 2]. There are new challenges in the business and IT alignment research for a modern digital society that incorporates the ecosystem approach and innovation elements [START_REF] Coltman | Strategic IT Alignment: Twenty-Five Years on[END_REF][START_REF] Nachira | Digital Business Ecosystems[END_REF]. However there is a lack of mechanism for assessing a company's readiness prior to joining a DBE.

This paper hence presents a conceptual model of open digital business ecosystems (ODBE) that comprises a methodology for assisting a company prior to joining the ODBE. The paper is structured as follows: Section 2 reviews the related literature, Section 3 illustrates the conceptual model of ODBE, Section 4 demonstrates the application of ODBE in a global non-profit organization, Section 5 discusses and contributions, limitations and concludes the research with future work.

2

Theoretical Background

Digital Business and Ecosystems

Digital business research concerns what and how technologies create business value and build a digital economy [START_REF] Barua | Information Technologies and Business Value: an Analytic and Empirical Investigation[END_REF][START_REF] Melville | Review: Information Technology and Organizational performance: an Integrative Model of IT Business Value[END_REF]. Such research discovered that digital businesses compete and collaborate among diverse entities in a self-organized and self-evolved ecosystem which is formally defined as Digital Business Ecosystems (DBE) [START_REF] Nachira | Towards a Network of Digital Business Ecoystems Fostering the Local Development[END_REF][START_REF] Dini | The Digital Ecosystems Research Vision: 2010 and Beyond[END_REF][START_REF] Nachira | Digital Business Ecosystems[END_REF]. This concept enables organizations within the ecosystem to create new connections for offering new services or products, sharing experiences and all these ultimately drive open innovation. A digital business strategy is imperative for forming DBE that maximizes co-created values by leveraging the digital resources [START_REF] Bharadwaj | Digital Business Strategy: toward a Next Generation of Insights[END_REF]. The study of enterprise architecture (EA) is instrumental for modeling a digital business ecosystem [START_REF] Blosch | Future of EA 2025: Evolving From Enterprise to Ecosystem[END_REF]. The future of EA research predominantly lies in developing the digital business strategy and capturing the co-created value through an innovation ecosystem [START_REF] Coltman | Strategic IT Alignment: Twenty-Five Years on[END_REF]. The innovation ecosystem encourages the organizations within the ecosystem to strategically collaborate and develop new digital business capabilities for competitive advantages [START_REF] Grover | Cocreating IT Value: New Capabilities and Metrics for Multifirm Environments[END_REF].

Value co-creation through affordances in innovation contexts

Business participants in a DBE collaborate among each other to achieve mutual business goals and this mechanism promotes innovation that fosters value co-creation.

The innovation context of a DBE is described as an affordance that realizes business transformation. The concept of affordance is used to describe the behavior of an organism made available by some combined structure of entity and its environment [START_REF] Gibson | The Ecological Approach to Visual Perception[END_REF] This concept studies all organized behavior being affected by communications and interpretation of signs by people [START_REF] Stamper | Information in business and administrative systems[END_REF]. Affordance and norms are often employed for identifying semantic units of a domain of study and hence establishing relationships among these semantic units [START_REF] Liu | Semiotics in Information Systems Engineering[END_REF].

The study of affordances and norms clearly contributes to identifying value cocreation in a DBE. The typical values co-created in a DBE are: 1) customer value, which helps to increase customers' satisfaction towards the product or service; 2) information value, where information is shared across the ecosystem and the partners within the DBE; 3) operational value, where a certain level of process interoperability is established; 4) business value, where partners within the ecosystem share their best capability in driving the best product or service; and 5) talent value, where people knowledge or human capital are shared across the DBE [modified from 15].

Open Digital Business Ecosystem (ODBE)

DBE is seen as a disruptive business infrastructure at the business strategy and policy level but still it is a lack of a methodology in facilitating the participation of multiple companies. Therefore, the ODBE model introduced in this paper aims to facilitate open innovation that enables an alignment between open innovation and value cocreation in a manner of self-evolving and self-managing towards co-development of products [START_REF] Han | Value Cocreation and Wealth Spillover in Open Innovation Alliances[END_REF]. Fig. 1 depicts a conceptual architecture of ODBE that consists of four key functional components: 1) the business participants and their digital business capabilities, 2) the Digital business competency valuation, 3) DBE value cloud, and 4) an Innovation ecosystem.

Fig. 1. A conceptual model of ODBE

Business participant component

A focal company specifies its collaboration requirements which are used to qualify the participation of other partners based on their business capabilities. Such collaboration relationship is defined as a set of firms F={f1, f2, …, fm}. These firms may serve as a focal company in their own ecosystems. Each firm possesses a set of capabilities C={c1, c2, …, cn} which could potentially contribute to the ODBE. C is a proper subset of F, denoted by C F, ∀x{x∈C, x∉F}. f F is a candidate firm to enter digital business competency valuation where c∈C will be evaluated.

Digital business competency valuation component

The digital business competency valuation components provides a set of assessment techniques that examine by the readiness of a company prior to participating to the ODBE adopting the capability-resource analysis [START_REF] Amit | Strategic Assets and Organizational Rent[END_REF][START_REF] Day | The Capabilities of Market-driven Organizations[END_REF] (Fig. 2). The capabilityresource analysis requires input from the organizational aspects which are represented by digital business strategy, objectives and the critical success factors (CSFs). The objectives set the business goals which are part of the digital business strategy and the performance of the core competency is measured by CSFs.

Fig. 2. Assessment of the digital business competencies

C is represented through a unique pair of {business service, ICT service}, at the business operational level [START_REF] Ray | Information Technology and the Performance of the Customer Service Process: a Resource-based Analysis[END_REF]. Business services provide the business functionality of business roles that serves business needs for the customer. This is achieved through internal behavior, i.e., business processes, which require resources, competencies, knowledge and skills in order to produce products and values [START_REF] Jonkers | Concepts for modeling enterprise architectures[END_REF]. The business services profile captures the description about the stakeholders and their roles, operational capabilities, business processes, and business norms. The social perception of the stakeholders while consuming the resource to produce the outcome is also captured in the business services. The ICT services profile captures the enterprise ICT resources, such as hardware, applications, infrastructure, and contracted ICT components and the cost of each ICT element is associated with its total cost ownership (TOC).

Both profiles are used to determine digital business capabilities which resulted from mapping the specific business service and ICT service [START_REF] Sun | Evaluating Business Value of IT towards Optimisation of the Application Portfolio[END_REF]. A group of digital business capabilities form the basis of an assessment by using a cost-benefit analysis model in conjunction with a SWOT model. The insights can be generated from large volumes of corporate datasets and external social media repositories. The assessment produces Business outcomes which can be used to identify the candidate digital business capabilities for DBE value cloud. Business outcomes are specified by a set of measures based on {C, I, B, O, T} [adapted from 15], where C is consumer experience value; I is relevant and quality information value [START_REF] Izquierdo | The Impact of Quality Uncertainty without Asymmetric Information on Market Efficiency[END_REF]; B is B-B market share value, O is internal process and productivity value, and T is level of employees' knowledge, skill, and experience value [START_REF] Duncan | How to Establish a Data-Driven Culture in the Digital Workplace[END_REF]. These measures reveal the quality of business performance and how the firm engages with customers, employees and partners. For those business capabilities with {C, I, B, O, T} that are scored High or a high end of Medium, they are likely able to enable co-creation of value when they are connected through Innovation ecosystem.

The business competency analytics technique diagnoses a current internal stage of a firm f in moving towards a sustainability-led innovation. This diagnostic analysis adopts the prescriptive and predictive analytical methods, e.g., business performance and margin predictive analytics [START_REF] Chandler | Gartner's Business Analytics Framework[END_REF], customer relationship analytics [START_REF]Gartner: Customer Relationship Management (CRM) Analytics[END_REF], marketing analytics [START_REF] Hauser | Marketing analytics: the evolution of marketing research in the twenty-first century[END_REF], cost-benefit analysis, and talent analytics [START_REF] Davenport | Competing on Talent Analytics[END_REF]. A key purpose of this analysis is for f to predict its business by recognizing its SWOT dynamics, being aware of influential issues, and evaluating the implementation of the Digital business strategy. F={f1, f2, …, fm} can internally carry out such diagnostic analysis when appropriate so that each f remains innovative.

Business transformation analytics diagnoses the current stage of f in its external environment and it puts the emphasis on how f should respond to the pressures from the marketplace as well as the impact for change required by the Innovation ecosystem. Any changes for optimizing the business performance may be revolutionary or evolutionary and require strategic decisions from the stakeholders. The decision making process is then influenced by the SWOT analysis [START_REF] Pickton | What's Swot in Strategic Analysis?[END_REF], competitor analysis [START_REF] Collis | Can You Say What Your Strategy Is?[END_REF], risk analysis [START_REF] Peltier | Risk analysis and risk management[END_REF] and change impact analysis [START_REF] Malek | Overview of Change Management and Its Implementation[END_REF]. The result may reform the existing Digital business strategy where each f can maintain its co-created value in an Innovation ecosystem and stay competitive.

DBE value cloud component

The DBE value cloud is a repository of sharable digital capabilities owned by the business participants. A digital business capability is seen as-a-service [START_REF] Dini | The Digital Ecosystems Research Vision: 2010 and Beyond[END_REF] that is featured as automated business processes, IT-enabled capabilities, data analytics models, technology infrastructures and data host services. Fig. 3 depicts an information model that registers business capabilities in the value cloud. The value cloud interfaces with the Innovation ecosystem (IE) via a connector. The connector: 1) registers and publishes the capability, 2) discovers the capability based on a query (i.e., a set of criteria) from the IE, 3) selects the appropriate capabilities, and 4) transfers the resultant capabilities (e.g., URIs) to the connector at the IE end.

Fig. 3. Information model of the DBE value cloud

Innovation ecosystem component

An Innovation ecosystem (IE) is an affordance of the business transformation from the connected individual companies where these companies co-create values. A connection is driven by the qualitative requirements of an IE based upon which the "fit for purpose" digital business capabilities contained in the DBE value cloud. An affordance function of discovering and deploying digital capabilities is executed through connectors and it deploys the participating digital business capabilities (e.g., API-led connectivity), manages the service level agreement (SLA), and communicates the feedback of impact for change with the concerned firm (f). The SLA related function deals with the shared responsibilities for the collaboration quality and outcomes of the F and the C, and relevant cost and profit models.

Co-creation of value is the strategic goal of IE. All the alliance of C must aim at this common goal. The performance of each c is monitored and their joint business outcomes are assessed. As a result, the impact of change is fed back to those concerned F. The feedback suggest changes to 1) the existing for improving its quality of offering due to the evolutional change in the IE, 2) the existing F for investing further in their own C or developing new c to meeting the revolutionary change in the IE, and 3) the IE affordance to deploy different alliances with more appropriate quality and performance to satisfy customers' new needs.

ODBE Application in a Global Non-Profit Organization

ODBE is employed in the United Bible Societies (UBS) which is a case study of a global non-profit organization (NPO). UBS is one of the largest Christian fellowships in the world and it consists of approximately 146 Bible Societies (BS) operating in more than 200 countries and territories [32]. The key ministry services provided by UBS are bible translation, publication and distribution, literacy training, bible engagement and advocacy. These services often involve collaboration with other bible societies within the fellowship, partners such as the local churches and other faithbased NPOs. In this case study, BS5, a focal bible society, intends to run a mission project teaching women's literacy on a large scale. BS5 is then seeking collaboration with other fellowship partners who have registered business services in the value cloud (Fig. 4). BS5 looks for service providers who could offer the capabilities in women's literacy: 1) producing literary training materials, 2) providing funding opportunities, 3) experience in training the tutors. These services in the DBE value cloud were selected by conducting capability-resource analysis. Fig. 5 shows the business outcomes of various literacy services available in the DBE value cloud. By following the affordance of business transformation process, BS5 decided to collaborate with BS4 and BS2 as: 1) BS4 has delivered high customer value in the experience of engaging with local churches to reach 5000 women, and possesses high operational and talent value in the experience of setting up a literacy center and talent pool of trainers, and 2) BS2 possess medium information value in terms of producing literacy materials, high operational value as they have own printing facilities, and high business value as they have good relationships with the funding bible societies. BS2 and BS4 also perceive certain mutual benefits yielded from this collaboration.

BS2 and BS4 adopted the UBS standard literacy framework for project execution and management. Hence, there are no conflicts in terms of the business process and business norms. As a result of the affordance process, FBS5={fBS2, fBS4}, the innovation ecosystem of women's literacy is formed. This collaboration has co-created five values which are qualitatively defined based on the actual outcomes achieved (Fig. 6). These values are beneficial to the partnership FBS5={fBS2, fBS4} as whole. However, these values also impacted fBS2 and fBS4 so that they improved their cBS2 and cBS4 (Fig. 7). For example, the information value of BS2 has changed from M to H, impacted by VC3 and VC5, where they are now capable of conducting women's literacy in different demographic. Similarly, the business value of BS4 has changed from L to M, impacted by VC4, as they have grown in reputation by helping other BS.

Discussions and Conclusion

The ODBE model has implications for the modern business environment in areas such as the digital oriented business network, business-ICT alignment, innovation and management. From the theoretical perspective, ODBE brings new ideas to the field of business and IT alignment by applying its principles to the existing digital business landscape and by introducing the ODBE, which enables companies to respond to the market changes driven by business and disruptive technologies. From the methodological perspective, ODBE enables companies to identify their digital business capabilities and assess their readiness for value co-creation in collaboration with multiple organizations. The assessment techniques are robust and can be iteratively carried out whenever appropriate. From the practical perspective, ODBE addresses the uncertainty inherent in new collaborations by indicating the values that could be gained by individual companies in a partnership through the DBE value cloud.

There are a few limitations in the ODBE. Each business participant has to be formally profiled. This profile provides useful inputs (structured and unstructured data) for the digital business competency valuation to instantiate the business analytics. External data sources need to be appropriately discovered and integrated to support the valuation analysis. The DBE value cloud requires technical mechanisms to register, store, manage, and discover business capabilities. An affordance query can then configure the innovation alliance collaboratively. The innovation ecosystem needs to be robust in co-creating values and can also generate feedback to aid the participants to maintain their service sustainability.

As for the future work, a number of structural methods will be developed for the ODBE such as the formal representations for DBE value cloud and innovation ecosystem and the interfaces for information exchange between the components. The technique of digital business competency valuation needs an integration of those analytical methods and executes the various analyses in a robust methodology. Furthermore, the technical functions of two types of connectors should be developed so that the ODBE model can be simulated scientifically. In addition, ODBE will be applied to multiple case studies in order to prove that it works in the real world.

Fig. 4 .Fig. 5 .Fig. 6 .

 456 Fig. 4. Value Cloud Register of C owned by F

Fig. 7 .

 7 Fig. 7. The innovation ecosystem of women's literacy impacted on the fellowship partners

Acknowledgments. This project is partially sponsored by Innovate UK (No. H4014200). We gratefully thank our industry partner who provided their substantial financial support and knowledge to the experiment of this study. This work is also partially supported by National Natural Science Foundation of China under grant No. 71532002 (key project), 71371125 and 61374177.