Vitaly Ambalov

Irina Heim
email: i.heim@pgr.reading.ac.uk

Cluster Nodes as a Unit for Value Co-creation: The Role of Information Technologies in Competitiveness of Kazakhstan's Oil and Gas Industry

Keywords: Eclectic Paradigm, Value Co-creation, Clusters, MNEs, ICT, Oil and Gas Industry, Local Content Policy

Both the national competitiveness paradigm and cluster theory have prompted decision-makers in emerging countries to take a closer look at the cluster approach, and to consider the possibility of using this approach to boost economic growth. However, this has not led to wider use of cluster analysis as a major instrument for studying complex economic processes in these countries. The reason for this is that the majority of the reviewed concepts of cluster development implemented in emerging countries, such as Kazakhstan and Russia, use of the cluster approach based on foreign experience disregarding the importance of initial local conditions. Thus a more formal study of clusters in emerging countries, taking into account the impact of institutional factors and the individual structural uncertainty of economic systems, recurrent crises and market shocks, is required This research fills the gap by proposing a cluster node as a unit of analysis that allows subdivision of any big industrial and commercial groups, of economic sectors, and of multinational enterprises (MNE) and other structures, into interconnected nodes of a smaller scale, and applying this up to the smallest nodes that are of interest to researchers. This concept will create theoretical foundations for the transformation of national economic clusters, which is strategically important for national governments seeking to attract foreign investments (FDI) and increase local content, thus attracting MNEs to invest in national economies. This research demonstrates how the concept of cluster nodes can be applied to the analysis of linkages between oil and gas industry (O&G) and industries responsible for the development of information and communication technology (ICT) in Kazakhstan.

Introduction

The national competitiveness paradigm and cluster theory were introduced by Porter in 1990s [14; 15]. They are the result of a large-scale research project that led to global changes in the perception of international trade and the manufacturing industry. These transformations evolved in the increasing role of specialized firms, and accelerated development of science and technology, in particular in the ICT sector. Simultaneously, the share of R&D services in the added value had been steadily increasing [START_REF] Castellani | R&D and Productivity in the US and the EU: Sectoral Specificities and Differences in the Crisis[END_REF], while the competitiveness of goods and services had come to rely on harmonized cooperation and simultaneous competition of firms as part of an economic cluster. The role of multinational enterprises has changed dramatically. Going beyond national borders MNEs have introduced new products and services into the global market, offering tremendous opportunities to small and medium-sized enterprises that could never before have claimed a share in foreign markets. Such transformations prompted decision-makers in emerging countries to take a closer look at the cluster approach and to consider the possibility of using this approach to boost economic growth. However, the extensive interest in the cluster approach has not led to wider use of cluster analysis as a major instrument for studying complex economic processes. In emerging countries, a cluster is viewed as a geographically limited association of small and medium-sized enterprises providing services to homogeneous market segments. This has led to mistakes on the side of government decision-makers who seek to create a vertical regulation structure and to implement an industrial policy that views the cluster approach as a concept which has nothing to do with the major economic sectors. In the majority of the reviewed concepts of cluster development implemented in emerging countries, such as Kazakhstan and Russia, the cluster approach is based on copying foreign experience that disregards "the importance of initial conditions and the danger of arguing that one cluster structure is necessarily always more effective than another one" [START_REF] Rugman | Multinational Enterprises and Clusters: An Organizing Framework[END_REF]. These circumstances prompt a more formal study of clusters, taking into account the impact of institutional factors and the individual structural uncertainty of economic systems, recurrent crises and market shocks. For the purposes of formalization of cluster systems, we make use of the results of many years of work of international business researchers, and, first and foremost, an eclectic paradigm, which we strongly believe most accurately defines the nature of a company that often acts using apparently incompatible strategies (eclectically), achieving incredible results in an eclectic environment.

2

Theories and Literature

Theory of Clusters and Competitiveness

Spatial clustering or agglomeration of firms with similar interests might yield agglomerative economies and an industrial atmosphere, external to the individual firms, but internal to the cluster [START_REF] Dunning | Regions, globalization, and the knowledge-based economy[END_REF]. The era of alliance capitalism demonstrated the impact of "trans-border elements" on cluster functioning [8;19]. Therefore, it is critical to identify a cluster in which MNE will utilize its own advantages. This identification seems deceptively simple, but in reality, it is a complex task, because there is no definitive and unambiguous understanding of the term "economic cluster". The need for cluster analysis arises when a researcher studying a large system needs to select elements of a certain class from its entire assembly. A classic example is using cluster analysis in order to study country competitiveness. In accordance with the task, Porter [14;15] confined the selection of objects for analysis to a specific geographical zone and defined a cluster as a geographically close group of interrelated companies and associated agencies united by common external factors and areas of activity. This definition of competitiveness underpins the concept of the Global Competitiveness Index, reflecting the growing demand to take into account a larger and more complex set of factors that have an impact on a country's prosperity. At present, global organizations work with the definition offered by Porter: "Competitiveness is defined as the set of institutions, policies, and factors that determine the level of productivity. The level of productivity, in turn, sets the sustainable level of prosperity that can be earned by an economy (and) a more competitive economy is one that is likely to grow at larger rates over the medium to long run" [START_REF] Snowdon | The Enduring Elixir of Economic Growth[END_REF]. However, many researchers criticize this definition of competitiveness at the macro-level [e.g. 1;12;16] arguing that this gives a distorted picture of competitiveness due to the protectionists regulations used by the countries to protect their markets. For example, Atkinson [START_REF] Atkinson | The Competitive Edge: A Policymaker's Guide to Developing a National Strategy[END_REF], defines competitiveness as the ability of a nation's non-mineralbased traded sectors to effectively compete in global markets in the absence of subsidies and government protections, while receiving a strong price premium that enables strong terms of trade. The approach suggested by this definition will be used to interpret the increasing competitiveness as a result of value co-creation in clusters.

Value Co-creation in Clusters

Value co-creation is a concept that "encompasses all the specific theoretical and empirical occurrences in which companies and customers generate value through interaction" [21;11]. Co-creation in MNE settings can be defined as the interaction between headquarters, subsidiaries, employees, subcontractors, and customers with each other for the development of new business opportunities [START_REF] Heim | Value co-creation in ICT services company: a case study of a cross-border acquisition[END_REF]. The major actors in the process of value co-creation in MNE settings -Headquarters, Subsidiaries, Employees, Customers and Subcontractorsinteract with each other in trying to meet customer expectations. To this end, each of the actors adds value. However, it should be taken into consideration that knowledge of customers' needs is also an important value that plays a crucial role in resource transfer managed by the MNE's subsidiary. The responsibility to do this is delegated to the subsidiary by the headquarters. Each cycle of adding value is preceded by a transaction of resource exchange. We would like to emphasize that the process itself does not create any value for the participants of the value co-creation process. The value is generated once a transaction is complete. Thus, in different periods each of the actors specified by the model can be either a supplier or a recipient of resources.

Eclectic Paradigm

The modern MNE has to be a 'meta-integrator', able to leverage knowledge within and between the different constituent affiliates of its international network, which requires efficient internal markets and well-structured cross-border hierarchies [START_REF] Narula | The Modern MNE as an Efficient Meta-integrator: Emerging Market MNEs Need to Foster Internal Embeddedness to Succeed[END_REF]. These empirical findings confirm that the major factors of competitiveness of domestic companies is not protection of the market against MNEs, but rather local content development through technological upgrade and growth, based on value co-creation in clusters comprising foreign and indigenous companies.

The study of related literature [2;9;13] and other literature brings us to the conclusion that the eclectic paradigm [3;4;5;6;7], coupled with internalization theory [17;18] is a representative analytical basis for describing structure, and the genesis of economic systems of any tier, not excluding economic clusters of various typologies. The eclectic paradigm is based on three easily definable factors (OLI factors or OLI): the first is the ownership advantage -O, which allows distribution between firms of advantages associated with owning various types of assets. The second is locational advantages -L, which is the advantage of location, such as natural and man-made resources and assets, institutional system of restricting actions of economic actors, as well as factors related to human capital. The third factor is internalization advantages -I, which can be viewed as advantage in terms of systemic links. Inter-and intra-cluster links (I) is one of the important indicators of cluster formation and will be studied in this research.

Theoretical Frameworks

The abovementioned properties and principles of the eclectic paradigm that bring together numerous heterogeneous elements and arrange them into three uniform groups, create a foundation for introducing a new unit to analyze interrelated industrial and commercial structures; such as industrial agglomerations, economic clusters, and MNEs. The new unit of analysis should encompass company capacities in conjunction with locational and internalization advantages. We use the term cluster node (CN) as such a unit of analysis, where CN is a set of interdependent factors.

In this research we define cluster node as a dynamic scale-invariant economic OLI system with institutional regulation of internalization of locational advantages (L), in which internalization (I) serves as a link between L and O advantages in this location, and a link with other cluster nodes outside this location. The use of a cluster node as a unit of analysis allows division of any large industrial and commercial groups, economic sectors, MNE and other structures into interconnected nodes of a smaller scale and applying this up to the smallest nodes that are of interest to researchers. The typology of the cluster nodes is discussed in the next section (see Table 1 below for definitions).

Investments in national economic cluster are always accompanied either by the internalization of existing cluster nodes, or the appearance of new cluster nodes. Moreover, independent if the investor is local or foreign, they need to exactly identify the type of the cluster node they invest as well as develop the strategy for value co-creation within the national cluster node. This is especially important for MNEs as OLI should best be seen as a way of looking at the phenomenon of multinational enterprises and their activities, resulting in answers on the important questions of MNE activities: why, where and how. Each of these questions can be addressed at a different level: macro, meso or micro [START_REF] Eden | A critical reflection and some conclusions on OLI[END_REF]. Figure 1 below shows the three-level architecture of a national economic cluster, in which a territorial cluster and an industry cluster, complying with restrictions imposed by national and supranational institutions, may have a common coordination body, while the remaining nodes should necessarily conform with the restrictions imposed by regulatory bodies (top officials), or coordinating bodies (the so-called cluster organizations).

The inclusion of people in the typology of clusters (clusters skill/Cs), has a number of significant reasons. The information that is created, accumulated and transferred in the network, the movement of knowledge holders within the created network, as well Fig. 1. Architecture of the national economic cluster. Source: Authors. as internetwork links, all have an impact on the efficiency of the subsidiary and the very MNE that equals, or is even greater, than that of the processes determined by the system. This is the impact of a human being both as an owner and carrier of intellectual property. Therefore, each employee should be viewed as a self-similar and invariant actor of the network communication because the cluster analysis will be incomplete without the intellectual component. Indeed, in any cluster and in any firm, tangible assets and internalization do not make sense without inclusion of the owners/professionals who act as knowledge holders.

The eclectic paradigm does allow one to go a step further by relating the OLI configuration facing MNEs to a number of structural or contextual variables» [START_REF] Dunning | Reappraising the eclectic paradigm in an age of alliance capitalism[END_REF]. This research will adopt the theory of value co-creation [START_REF] Vargo | Service-dominant logic: continuing the evolution[END_REF] as a theoretical lens through which IT-enabled local capabilities development in clusters are explained.

Case Study: Linking Kazakhstan's Oil and Gas and ICT Clusters

This concept is a promising instrument for optimizing the existing cluster nodes and synthesizing new cluster nodes, including in the complementary O&G and ICT sectors. The complementarity is based on three factors: (i) digitalization of business and broader use of sensing systems and other high-tech devices, which lead to the accumulation of big data, to be selectively used by managers and technical specialists; (ii) accumulation of big data prompts drastic transformation of the system itselfdata as such will be viewed as a valuable source of knowledge about an object and its current status and behavior. This will help to improve the system of quality management, as well as to synthesize innovative tasks and run projects. In other words, there will be integration of all components of two sectors; (iii) a capability to analyze the current status of the system and total automatization of processes, which will gradually erase borders between ICT and business, and emphasize the importance of specialists being able to work with big data.

We study the possibility of increasing the local content and accelerating the growth of the ICT cluster node of Kazakhstan by linking it with the O&G cluster node, integrating this pool with national clusters of other countries, thus making use of the mutually beneficial cooperation with the unified international cluster system. The cluster Macro level (National economic cluster -Cn) The matrix is based on official statistical data and information provided by international institutions. These data confirm the country's progress in high-technology sectors. In particular, in line with The Networked Readiness Index 2016, Kazakhstan is ranked 39th out of 139 countries. Thus, Kazakhstan is ahead of Turkey, Poland and Italy and is close to the Czech Republic in this index [START_REF]The Global Information Technology Report[END_REF]. The analysis of the OL matrix reveals that Kazakhstan's cluster nodes responsible for telecommunications and provision of computer equipment and communication facilities are well balanced in terms of supply and demand. Institutions that are necessary for regulation of value co-creation processes in cluster nodes are created (National ICT Holding "Zerde", National Agency for Development of Local Content "NADLoK" and other). However, certain disproportions in the linked nodes have come to the surface. Indeed, the potential demand of Kazakhstan's O&G industry for computing equipment and services (over $1 billion annually)

is not yet saturated. This fact allows high-tech companies, including subsoil users, not to consider the ICT cluster node as a partner able to address these needs. At the same time, information and communication technologies for the O&G industry are developed and purchased outside Kazakhstan's CN. Apart from this, the Ownership-Location matrix demonstrates: (i) weakness of the ICT cluster node responsible for software development, which is a serious challenge for the ongoing local content policy because information technologies are and will be a key instrument for enhancing the performance of the domestic O&G industry. (ii) another weak point is unavailability of hands-on experience for graduates of new university departments/new universities because of an inadequate number of projects related to development of original IT solutions. At the same time, the launch of new IT projects is prevented by inadequate competencies of programmers. (iii) the country's education system in the ICT field has made considerable progress and domestic universities turn out more graduates for this field than the national economic cluster currently can employ.

Nevertheless, the capacities of the linked nodes of ICT and O&G can be augmented, if the IT business, supported by government institutions, uses FDI instruments to team up with a developed IT cluster of a country that is similar in culture and other parameters determining distance. In line with the cluster nodes concept, if Kazakhstan's leading IT firms set up subsidiary companies in the countries that have developed IT clusters, the available significant advantages will meet to enable large-scale projects and to provide university graduates with an opportunity to get hands-on experience.

Conclusions

The traditional approach to studying and defining economic clusters focuses on geographically close groups of companies, and is based on using companies as a unit of analysis. In this work, we suggest the consideration of a cluster as a dynamic OLI system defined as a node, a unit of analysis in a cluster node model. The study of economic clusters in Kazakhstan suggests that competitiveness of clusters is determined by a number of firm specific advantages, available to all cluster participants. The analysis of ICT and O&G clusters of Kazakhstan reveals that the concept is a promising instrument for optimizing the existing cluster systems and synthesizing new systems.

Table 1 .

 1 Classification of cluster nodes. Source: Authors.

	Type of a cluster node	Acronym	Definition
	National economic cluster	Cn	Advantages and their owners as well as institutions regulat-
			ing internalization in certain countries
	Territorial cluster	Сt	Advantages and their owners as well as institutions regulat-
			ing internalization in certain territory
	Industry cluster	Сi	Actors taking part in value creation of certain types of prod-
			ucts
	Clusters alliance	Сa	Actors with weak links, interacting and competing with each
			other
	Clusters group	Сg	Actors with strong links and low competition (hierarchical
			corporations, including state-owned holdings)
	Company	Cc	Company where processes are determined
	Node process	Cp	Process performed in a company
	Clusters skill	Сs	Person, owner of the technical knowledge and skills required
			for successful career in the industry

  Matrix of the links between two nodes: ICT and O&G, current status (2017). Source: Authors based on data provided by the Committee on Statistics, the Ministry of National Economy of the Republic of Kazakhstan, RAEX rating agency, Kazakhstan and authors calculations based on media reports.

	nodes concept allows cluster analysis to be conducted across selected indices describing
	competitiveness. In this case we have used one index, namely the index showing the
	Territorial change of the share of products assembled by Kazakhstan's ICT, and consumed by the Meso level O&G industry. Figure 2 below shows the matrix of the current status of the explored cluster -Сt Cluster alliance -Сa Micro level system.
	Industry cluster -Сi O Advantages	Cluster group -Сg Business processes, Company -Cc O&G	Node process -Cp Education	Cluster Education skills -Cs and
		international transactions,	Training of	R&D
		equipment, ICT	ICT specialists	in
	L Advantages			ICT and O&G
		Capital investment:	13,558	21,269
	O&G cluster	$26.83 billion	graduates with	peopled
		Production volume:	degree in	employed in
		oil -86 million tones (80% MNE)	electronics and	R&D (all
		gas -61.6 billion m 3	ICT	sectors)
		Laws and regulations aiming to attract FDI
	Internalization	Local content regulations
	advantages	Regional legislation, including Eurasian Economic Commission
		World Trade Organization membership
	ICT cluster	Market size:
		$2.68 billion, including:	Low demand for software and
		O&G cluster is < 10%	R&D specialists, a low number of
		Services and software:	software programmers
		$644 million, or < 25%
	O&G cluster	Added value of products and ser-	Insufficient financing of projects to
	demand for IСT	vices related to IСT is estimated	develop high-technology solutions
	products and	to exceed $1.0 billion
	services
	Number of	Communication: 34,500 people
	specialists in	Information services: 7,600 people
	ICT cluster	Computer programming, consulting and services: 3,000 people
	Demand for
	human	High demand for highly qualified specialists
	resources in ICT	An outflow of e employees abroad
	cluster
	Fig. 2.


Shixiong Liu

Weizi Li
email: weizi.li@henley.ac.uk

A Framework to Evaluate Semiotic Interoperability for Information Sharing

Keywords: Interoperability Evaluation, Semiotic Interoperability, Information Sharing, Business Process

Interoperability is the ability of entities in organisation to work together that covers aspects ranging from the technical to the business level. Over the last decades, the interoperability concept and its context have been changing rapidly. It expands from the largely IT-focused area to business-focused area. The evaluation of interoperability is a rising concern in various research domains. There is increasing number of researchers that have been started concentrating on not just digital aspects, but also business related, human related, and social environment related aspects. Our previous investigation reveals in that interoperability issues from those perspectives are becoming a rising concern. When we study information sharing and business collaboration within organisations, the business activities and operations in organisation, which directly affect business performance, are driven by business processes. Therefore, the interoperability between business processes is the key to information sharing assurance. This paper proposes a framework from a new perspective -semiotics perspective, for enhancing interoperability evaluation. The framework derives from a feasibility study that investigates interoperability barriers in organisation. The framework offers the capabilities of analysing, measuring, and assessing the interoperability between business processes.

Introduction

In the current industrial and economic context, market demand and technological evolution are changing sharply. Organisations seek to become more agile, responsive and competitive. Enterprises tend to maximise their Information Technology investment in order to support information sharing among not only digital systems, but also business processes [START_REF] Clabby | Web Services Explained: Solutions and Applications for the Real World[END_REF]. [START_REF] Panian | Why enterprise system integration is inevitable[END_REF] summarises two business drivers: 1) the need to consolidate and globalise, which indicates that many leftover mission-critical systems caused by mergers and acquisitions are requiring a better interoperability between them in order to enhance information utilisation; and 2) the search for increased productivity, indicates that collaboration of business processes can aid organisations to increase productivity and to reduce costs. The successful collaboration provides instant operations for organisations, which lead to optimise decision-making processes [START_REF] Chen | Architectures for enterprise integration and interoperability: Past, present and future[END_REF]; EN/ISO I9439, 2003). Successful information sharing also helps stakeholders to manage in responding to the changes [START_REF] Kaye | Loosely coupled: the missing pieces of Web services[END_REF], and improve organisation's agility by providing the flexibility to quickly adapt information systems in order to accommodate growth and meet arisen business challenges. In order to study the effectiveness of the information sharing between digital systems and between business processes in responding to the changes of market demand and technological innovation, the concept of interoperability, which was originally used for evaluating connection of technical devices and integration of ICT systems, is becoming a widely accepted tool for assessing not just the technical integration, but also collaboration in business in the last decade (Liu et al., 2013). Interoperability is the ability of entities in organisation to work together that covers aspects ranging from the technical to the business level. Over the last decades, the interoperability concept and its context have been changing rapidly. It expands from the largely IT-focused area to business-focused area. The evaluation of interoperability is a rising concern in various research domains. There is increasing number of researchers that have been started concentrating on not just digital aspects, but also business related, human related, and social environment related aspects. Our previous investigation reveals in that interoperability issues from those perspectives are becoming a rising concern. When we study information sharing and business collaboration within organisations, the business activities and operations in organisation, which directly affect business performance, are driven by business processes. Therefore, the interoperability between business processes is the key to information sharing assurance. This paper proposes a framework from a new perspective -semiotics perspective, for enhancing interoperability evaluation. The framework derives from a feasibility study that investigates interoperability barriers in organisation. The framework offers the capabilities of analysing, measuring, and assessing the interoperability between business processes. The paper starts with a recap of previous study on evaluation of semiotic interoperability, and then conducts a feasibility study that adopts organisational morphology to investigate barriers at three layers (i.e. Informal, Formal, and Technical). The identified barriers and its corresponding solutions are consolidated to form a framework -Semiotic Interoperability Evaluation Framework, which is proposed with discussion in the end of this paper.

Background

Semiotic Interoperability Definition

In our previous work (Liu, 2015;Li, 2013), the concept of semiotic interoperability is defined based on the semiotic framework from organisational semiotics. As introduced the semiotic framework provides a sound theoretical foundation for understanding of the nature of the sign-based communication, and a holistic view about signs, information, systems and organisations [START_REF] Stamper | Information in business and administrative systems[END_REF][START_REF] Liu | Semiotics in Information Systems Engineering[END_REF]. The semiotic interoperability allows information systems to work together through communication, and also enables collaboration of business processes through understanding of intention and social consequence. Fig 1 describes the concept of semiotic interoperability and its functional context at six constituting levels. Physical interoperability enables seamless communication between senders and receiver handling the physical tokens transmitted via a route at the destination without loss of physical properties. Interoperability at Empiric level is achieved when the receiver can reconstitute the same sequence of symbols that were sent by the sender, irrespective of any problems at the physical level. Interoperability at Syntactic level is achieved when the expression of information, or language, or formula can be recognised by different information systems. The data structures and format of file and message have to be readable to both ends of communication. Semantic interoperability can be achieved to give the same meaning to exchanged information between information systems, and it requires a conceptual model which describes what information is exchanged in terms of concepts, properties, and relationships between these concepts. More specifically, the semantic interoperability not only entails the data to be universally accessible and reusable, but also address the lack of common understanding caused by the use of different semantic representations, different purposes, different contexts, and different syntax-dependent approaches. Interoperability at the pragmatic level ensures that business processes supported by the information systems in individual contexts can be aggregated to achieve the overall intended purpose. It enables the alignment of business workflows, alignment of processes, and alignment of rules. Besides, the pragmatic interoperability also aligns social aspects such as culture, norms, environment, and actor's behaviour patterns in order to solve conflicts of cohesiveness. Interoperability at Social level ensures the intention or purpose of the sender has led to a social consequence to the receiver, and the social consequence can be social commitment, obligation, and norms. The social interoperability also ensures that those social consequences support the business strategy, vision, objectives, and the business environment. There are a few sub-areas under the topic of social interoperability such as alignment in traditions, alignment in policies, alignment in culture, alignment in ethics, alignment in management style, and alignment in environment (Barbarito et

Evaluating Interoperability between Business Processes

The focus of this paper is the upper two levels of the semiotic interoperability: pragmatic and social levels. In our previous studies, a review of all existing interoperability evaluation frameworks points out that most of current works have tackled the interoperability issues at the semantic, syntactic, empiric and physical levels [START_REF] Liu | Pragmatic Oriented Data Interoperability for Smart Healthcare Information Systems[END_REF]. But very limited amount of works is concentrating on evaluating interoperability at pragmatic and social levels. According to the definition of semiotic interoperability, the pragmatic interoperability is to ensure that business processes supported by the information systems in individual contexts can be aggregated to achieve the overall intended purpose. To evaluate the interoperability between business processes, this thesis deals with the issue from the organisational morphology perspective. As discussed in the first previous sections in this chapter, organisation can be seen as an information system, because information is created, stored, and processed for communication, coordination and achieving the organisational objectives (Liu et al., 2002). From an organisational perspective, information systems are defined by the cultural and legal norms that regulate people's behaviour (Gazendam and Liu, 2005). Thus, the definition of organisation is extended to a wider sense such that, a group of people, a society, a culture, do not only share language, customs, and habits, but also participate in the social construction of their own rules. In summary, the organisation is regarded as an informal information system where meanings are established, intentions are understood, beliefs are formed, commitments are made, and responsibilities are negotiated through the decision of physical actions. The organisation morphology categorises those meanings, intentions, beliefs, commitments, and responsibilities into three layers: the formal, the informal, and the technical. Business processes, at the formal layer, plays dominant role that drives business activity and operation, which directly affects business performance. However, the business process cannot be treated in isolation to aspects at other two layers: the informal and the technical. Because the studies in information system have never treated the information system as a set of separate components but considered it as a whole. Thus, the business process also requires supports from both technical and informal layers. According to the definitions given by Li (2010) and Zutshi et al. ( 2012), business process is a set of activities that occur in a coordinated manner pursuing one common goal. By looking at the entire information system, the activities are dynamic in nature but consider static aspects as well. The dynamic aspects include culture, norms, different behaviour patterns; and the static aspects include technical system capacity, data structure, data transmission, connection etc. Therefore, to evaluate the interoperability between business processes is to evaluate the interoperability at the formal, the informal, and the technical layers. The informal layer contains aspects such as community, social norm, policy, and culture. Those aspects can be expanded to be different behaviour patterns of both organisations and individuals. The interoperability at this layer is to align the different aspects in order to solve conflicts of cohesiveness. In the formal layer, business process plays dominant role that specifies on how functions should be carried out and how tasks should be performed. The interoperability at this layer is to align procedures and rules in order to achieve higher efficiency. It defines business goals, and models business processes, also brings the collaboration of administrations that aims to exchange information and have different internal structures and processes. The technical layer mostly refers to the technical computer systems and the implementation of their services, integration, and functions. The interoperability at this layer is to align technical functions and interfaces ensuring that the implementation has been done properly in order to achieve higher system productivity.

The organisational morphology theory is applied to evaluate interoperability between business processes from three layers: the formal, the informal, and the technical. The three levels are the foundation for developing the framework -Semiotic Interoperability Evaluation Framework (SIEF). Before proposing the SIEF, it is necessary to investigate the feasibility. To conduct a feasibility study, several industrial cases for interoperability evaluation are investigated. The findings of the feasibility study identify concerns and barriers at the three layers when considering interoperability evaluation between business processes and those concerns and barriers will be later transformed into metrics contributing to the SIEF.

Findings of Feasibility Study

The feasibility study is mainly conducted through interviews and surveys in a healthcare software company and one of the hospitals in China where the company provides solutions for. The company currently runs an integration project, which aims to enable information sharing among systems such as Radiology Information Systems (RIS), Electronic Health Record (EHR), and Picture Archiving and Communication Systems (PACS). The Radiology department provides diagnostic and interventional radiology for inpatients, outpatients and general practitioner referrals. Various healthcare services such as Computed Radiography (CR), Computed Tomography (CT), X-ray, and Interventional Radiology produce a huge amount of information regarding patient's healthcare delivery and clinical process. Both relevant employees of the company and clinicians in the hospital are selected for the interview.

To select appropriate case, a method developed by Seawright and Gerring (2008) is adopted. Following the method, firstly the selection method is qualitative research approach. Secondly, the goal of the feasibility study is to support the feasibility of the proposed SIEF by conducting interviews. Thirdly, the size of the case is medium enterprise which has approximately 200 employees working on various projects. The project where this case study conducts involves 28 staff, and from the hospital side, 15 clinicians are involved.

To select appropriate interviewees, several criteria are set up for the screening process. Firstly, by working experiences: should have more than 3-year experiences relating to software and systems development for employee; and should have more than 3year experiences directly using the above information systems for clinicians. Secondly, by technical relevance: should have been directly involved in the development, other supporting roles are not accepted for employee; because the clinician is the user, this criterion does not apply to them.

Following the two criteria, 31 semi-structured interviews (summarised in Table 2) were conducted with relevant stakeholders. The interviews lasted around 40 minutes and were on a one-to-one basis. The interviews were tape-recorded and later transcribed and rendered anonymously. The questions asked are derived from the SIEF which is presented in the next Chapter. The results of the interviews are summarised into categories presented in the following sections. The open interview basically asked what concerns and barriers should not be ignored when assessing interoperability from the three layers: the technical, the formal, and the informal. As the purpose of the interview is to investigate the feasibility of interoperability assessment, thus the questions asked aim to gather wide opinions from those participants, and more specific data analysis techniques such as content analysis are not applied. The key results from the interview are summarised in following paragraphs.

To enable collaboration between business processes, a shared intended purpose plays a key role that supports perceiving of personal beliefs and organisational ground rules, whereas an un-shared purpose may be considerable conflicts between the organisational level and personal level. Issues like restriction to staff behaviour, information collaboration (information channels alignment), and privacy and security concerns are raised attentions. Taking one example from one of the interviewed hospital, the informal level is concerned with the understanding of the healthcare, regulatory, legislative and healthcare environment in which information systems need to be deployed to support healthcare delivery. It requires agreement on key organisational concepts such as policies, processes and roles; it also captures relevant patterns such as compliance, governance, legislative and change management. Table 3 summarises the concerns and barriers at informal layer from the interview.

Table 3. Concerns and barriers at informal layer summarised from interview results

Culture issue

Tacit knowledge has not been explicitly stated and shared

Ethical issue

Appropriateness of taking actions on healthcare service delivery Behavioural factor

Willingness to be open and to share

Management style

Leadership style influencing the degree of willingness of collaboration Policy and procedure Internal control process, work flow, staff relationships, communication patterns, cut-across political boundaries, etc.

Restriction to staff behaviour

Staff's fear on integrated working process as restriction that might control their behaviour Privacy and security Sensitive information of patient to be protected by law At Formal layer, the concerns and barriers are collected from the interview and observation are summarised in Table 4. To assess interoperability, we should not only be concerned with information exchanged between technical systems, but also the knowledge of the context that the information exists within each system or process. As quoted from one IT project managers: "…It is important to articulate the requirement for context awareness that process representation begins. The context of the target system should also be made available to the origin system. Key questions such as what process will first operate on the information at the target system once it receives it, and what state of preceded processes are should be concerned…" By understanding this context, the system engineer and integration designer can ensure pragmatic interoperability is addressed for the needs of process integration. To define the context, one manager of the logistics department said that "Assume that the context is about internal workings of the process, in other words, the initialization state, the end state, the nature of data transformations, and details about the timing of the process are all considered, so that the receiving process can make better use of information it receives. This information is in context, but it also shows the dynamic nature of that context to the receiving system, because it now has specific information about the dynamic context within the originating system." Purchased systems from various venders with low capability For example, context is seen as a demand for more information between the Model Manufacturing Demand Service and the Rental Fulfilment Service, so that a specific understanding of the models requested has a deeper meaning. This could be a specific based on the timing of the data, the initialization state of the Rental Fulfilment Service, and the data transformations. This gives a more dynamic picture of the context for the information being produced by one process for another, and allows for a deeper understanding of the meaning of that context.

The purpose at technical level is to make exchanged data available for supporting the processes at upper level. For example, in healthcare environment, it is concerned with the understanding of technical functionality for supporting information systems; from a project management perspective, the manager needs to ensure both data and service integration have been successfully implemented beforehand. It requires agreement on a core set of technical concepts, such as technical components and devices, the interactions between components, interface and technical services; it also captures relevant patterns such as technical architecture styles and styles of component interactions. However, the study at this level is not the focus of the interoperability evaluation in this thesis, although this layer is vital to support the understanding the concerns of pragmatic interoperability. Therefore, during the interview, only few concerns regarding technical level are summarised below.

Table 5. Concerns and barriers at technical layer summarised from interview results

Semantic heterogeneity

Refers to the variation of semantic meaning in information resources which will lead to the semantic conflicts and complication for data integration Ontology structure

Approaches that employ ontologies for information systems This paper tends to not view the interoperability assessment separately by levels, but to think it as a whole. The three levels in the SIEF are all concerned with the pragmatic and social levels in the semiotic framework. If there must be borderline in between, then the group of technical and formal metrics, and informal metrics could match the pragmatic and social levels respectively with overlapping. The relation between the metrics in the SIEF and the levels in the semiotic framework is that the pragmatic levels and social levels comprise but not limited to those levels.

Fig. 3. Interoperability between business process A and B

Therefore, the interoperability between the two business processes is the combination of the interoperability at the three levels, which can be presented as the Equation (1):

x + y = z Figure 3 illustrates how the interoperability evaluation is conveyed between two business processes. In the informal level, the metric is also named indicator, which means the indicators of process A such as the management style, religions, appropriateness of taking actions, employee's motivation, employee's honesty, should be in line with the indicators of process B. in the formal level, the metric is also named performance measure, which means the measures of process A such as clarity in business strategy, backup strategic plan, management of External relationships, clarity in responsibility, should be in accordance with the performance measures of process B. In the technical level, the metrics of process A such as design of services, model of business document, and implementation of data/service integration, should match with the metrics of process B.

Conclusion and Future Work

Assessing interoperability is still challenging because those metrics cannot be easily quantified for more specific measurement. As discussed previously, on one hand, the metrics are partly derived from results of the case study, which reflect key concerns and barriers of interoperability; on the other hand, there are also metrics derived from the existing works, which have been explained in the previous section. For each metric/concern/barrier, we have investigated corresponding solutions/methods/tools, which will be used as criteria to evaluate that whether the concern or the barrier has been addressed or not. In this case, to measure each metric, all relevant methods, solutions, and tools will be developed in future work.

Fig. 1 .

 1 Fig. 1. Semiotic Interoperability

  use of terms across departments Implementation of data integration Defining source and target data format; data transformation and mapping; deploy on execution infrastructure Implementation of service integration Services for connecting processes and message exchange 4 Semiotic Interoperability Evaluation Framework Based on the findings from the feasibility study, key barriers are identified while considering the interoperability assessment. The empirical investigation supports the SIEF to derive its associated metrics. Besides, based on the previous works from Li (2010), Liu et al. (2014), Meyers et al. (2005), and other widely used interoperability evaluation frameworks, the assessment metrics are developed accordingly. Fig 2 presents the SIEF, and there are no explicit borderlines among the technical metrics, formal metrics, and informal metrics.

Fig. 2 .

 2 Fig. 2. Semiotic Interoperability Evaluation Framework (SIEF)

  ,Pb): Interoperability between two processes A and B; ITe(Pa,Pb): Interoperability at technical level; IFo(Pa,Pb): Interoperability at formal level; IIn(Pa,Pb): Interoperability at informal level.

  al., 2012; Boonstra, Broekhuis, Offenbeek, & Wortmann, 2011; Gregory, Dixon, & Ham, 2012; S. Liu, Li, & Liu, 2014; Saag et al., 2012), those sub-areas cannot represent all the concerns and issues that social interoperability deals with, because the topic is still under investigation and development.

Table 1 .

 1 Table 1 describes the key aspects at each layer. Three layers for evaluating interoperability between business processes.

	Layer	Description
	Informal	Community, social norm, people, policy, culture, eth-
		ics, environment, alliances etc.
	Formal	Organisational strategy/vision, business governance,
		domain analysis, organisational roles, functional pro-
		file, rules, procedures, management etc.
	Technical	Data semantics, information infrastructure, infor-
		mation model, schema, script, interface, platform, de-
		ployment model, resources, products etc.

Table 2 .

 2 Summary of interviews

	Sectors	Sample	Years of experience
			3-5	6-10	11-15	16-20	20+
	Industry	22	7	11	2	1	1
	Hospitals	9	3	2	1	1	2
	Total	31	10	13	3	2	3
	22 participants from industry have been interviewed: 6 of them are junior-manager
	level, 10 of them are senior-manager level e.g. senior system architect, project manager;
	and 6 of them are executive level e.g. CEO, CTO, director of software design, and
	director of service delivery.
	9 participants from hospitals have been interviewed: 4 of them are physicians, 3 of
	them are radiology technicians, 1 IT manager with 11 years' experiences, and 1 medical
	administration manager.

Table 4 .

 4 Concerns and barriers at formal layer summarised from interview results

	Organisation structure	Centralised, decentralised, hierarchical, matrix, net-worked, etc.
	Harmonized strategy	Aligned operations to be applicable on the strategic level
	Performance constraints	Fewer investment but more effective collaboration
	Cost constraints	Unexpected budget
	Data source interoperabil-ity	Multiple data sources used for supporting process
	Context awareness	Knowledge of context of both collaborative par-ties/processes
	Varieties of purchased sys-
	tems


Eric Afful-Dadzie
email: eafful-dadzie@ug.edu.gh

Anthony Afful-Dadzie
email: aafful-dadzie@ug.edu.gh

Local Government Open Data (LGOD) Initiatives: Analysis of Trends and Similarities among Early Adopters

Keywords: Open Government Data (OGD), Local Government Open Data (LGOD), Open Data, Early Adopters, Clustering

This paper focuses on developments of open government data (OGD) at city, municipal, county, federal state, regional and provincial levels around the world. This is in line with recent OGD trends where local government authorities, especially in developed countries, are launching separate OGD web portals to complement central governments' efforts at liberating public data. Focusing on early LGOD adopters, an inventory audit of contents and functionalities in use at LGOD web portals was conducted. The data generated was analyzed for trends and (dis) similarities among early LGOD adopters. The results of the study points to a general sense of heterogeneity among LGODs across the world in terms of adherence to OGD web publishing standards. There is also a lack of uniformity in terms of OGD web portal functionalities and contents even among local government authorities within a same country.

Introduction

Though relatively new, there is a growing interest in Open Government Data (OGD) from governments, civil society groups, the media, researchers, among others. OGD which primarily seeks the liberation of government controlled data, began with President Obama's open data initiative of 2009 and was subsequently strengthened by the 2013 G8 Open Data Charter [START_REF] Tauberer | History of the Movement[END_REF], [START_REF] Attard | A systematic review of open government data initiatives[END_REF], [START_REF] Castro | Open Data in the G8: A Review of Progress on the Open Data Charter[END_REF]. These initiatives sought to encourage countries to make open and for free, public data to the citizenry. To further facilitate the adoption of OGD, the Open Government Partnership (OGP) was created to bring together countries that have affirmed their willingness to provide easy and free access to public data. Typically, countries are admitted as members of OGP when they submit to a number of processes including, formal expression of interest by heads of sovereign states, endorsement of open government declaration, submission of a country action plan and finally, a commitment to an independent reporting mechanism1. Member countries are then expected to launch an OGD web portal as a public data repository. Both the initial processes together with the opening of OGD web portals, often occur at the instance of central governments. That is to say that, central government politicians and administrators are often the visible faces in these OGD projects. Such roles played by central governments have partly made central government open data (COGD) the main focus, often at the neglect of other variants. Recently however, local governments through cities, municipalities, counties, federal states, regions and provinces have been launching their own independently operated OGD web portals. This is welcoming, since it complements central governments' efforts at releasing public data to entrench democracy and spur economic growth and innovation among citizens [START_REF] Janssen | Benefits, adoption barriers and myths of open data and open government[END_REF]. The idea of an independently-run local government open data (LGOD) is fast gaining momentum especially in the developed world. For instance, there are as many as 290 local authorities comprising of cities, counties, federal states, regions and provinces that are actively running independent OGD web portals at their local administrative levels. It must be noted that though central governments' role in OGD is crucial, local governments are the real policy actors when it comes to both the supply and demand sides of OGD implementation. This is true since most public datasets are first generated at the level of local authorities or agencies, while a functional OGD system also assists local governments to transform service deliveries through significant cost savings and regular evaluation of local services performance [START_REF] Rahily | Innovation in Local Government: Open Data and Information Technology[END_REF], [START_REF] Hammell | Open data-Driving growth, ingenuity and innovation[END_REF]. This helps to actualize the value creation potential of OGD. We therefore argue in this paper, that an equal measure of attention should be given to local government open data (LGOD) as given to central government open data (CGOD). To generate more research and advocacy interests in LGODs, this paper focuses on activities of LGOD early adopters around the world. The paper first audits technical standards in use at LGOD web portals around the world. Data derived from the web content and functionalities audit are analyzed for trends and (dis)similarities among early local government-based OGD adopters.

Though researchers continue to report on OGD activities and initiatives at the level of central governments, very few works have focused on local government open data initiatives. None of the few published works on LGODs comprehensively reviews technical features of web portals for trends and similarities. The closest works in terms of the OGD web audit approach, came from [START_REF] Chatfield | A longitudinal cross-sector analysis of open data portal service capability: The case of Australian local governments[END_REF] and [START_REF] Afful-Dadzie | Open Government Data in Africa: A preference elicitation analysis of media practitioners[END_REF]. The work by Chatfield & Reddick audited open data portals of twenty local governments in some of Australia's large cities. In [START_REF] Afful-Dadzie | Open Government Data in Africa: A preference elicitation analysis of media practitioners[END_REF], an audit of the content and functionalities of OGD web portals in seven countries in Africa. The work by [START_REF] Sáez Martín | An international analysis of the quality of open government data portals[END_REF], looked at the quality of open data web portals but only focused on CGODs. Similarly, [START_REF] Susha | Critical factors for open data publication and use: A comparison of city-level, regional, and transnational cases[END_REF] also looked at open data at local government levels but focused on determining factors that influence the success or failure of open data initiatives. This paper fills the gap by auditing web portals of 'independent ' LGOD early adopters and further analyze the data to glean vital information regarding trends and (dis) similarities among them.

Methodology

The methodology is divided into two stages. In the first stage, an inventory audit of LGOD web portals was carried out benchmarked against widely accepted technical standards for publication of OGD. Two of such technical recommendations come from the World Wide Web Consortium (W3C) authored by [START_REF] Bennett | Publishing open government data[END_REF] and the World Bank's open data toolkit [START_REF] Herzog | Technology Options for Open Government Data Platforms[END_REF]. In the second part, the data generated from the inventory audit was analyzed using association rules mining and K-Means clustering techniques to determine frequent trends and natural groups among the early LGOD adopters. The following sections present brief introduction to the audit and the techniques used in the analysis.

Table 1. Attribute selection for Inventory strategy.

Feature Audit strategy

LGOD web portal content and services

Data format

Open government data requires public organizations to publish data in a form that is easily accessible by data users. In particular, the W3C and the World Bank strongly recommend that public data are published in machine-readable non-proprietary electronic format. Non-proprietary machine-readable data formats ensure easy access and processing by data users. In the audit however, most web portals were found to provide both non-proprietary and some proprietary formats. Some of the common list of data formats audited were RDF, CSV, PDF, XLX(S), JSON, XML, KML, GeoJSON, ODS/ODF etc.

Metadata

This is one of the essential OGD publication features which requires public organizations to accompany datasets with relevant information that describe the dataset. Some relevant metadata features could be the publication date of data, author attribution, and unit of measurements of data attributes among others. The audit checked for the presence or otherwise of metadata on

LGOD web portals by randomly scanning through 5 datasets on each subject (category).

Data currentness

Here, the audit sought to find the age of the most current data published on the portal. If the latest data is less than 30days old, it is regarded in this research as "current". However, if the latest dataset is more than 30days old, then it regarded as "not current". In the data collection, LGOD portals exhibiting current datasets were assigned the code 1 while a 0 for not current data sets.

Data visualization Another requirement of OGD web portals is the provision of a data visualization tool. Such tools help data users to preview datasets before download. Visualization features come in various forms including tables, graphs, maps etc. The audit strategy checked whether the OGD portals provided this feature in the design or not. If this functionality was provided, the local authority is a 1 in the data collection stage. However, if not available, it is assigned a 0 Data search

This OGD attribute seeks a data functionality that makes it easier for users to search specific information. Supported by the W3C and the World Bank, OGD web portals are expected to be searched by data types, subject and organization. The audit looked for the presence of this feature and scored a 1 for availability and a 0 for a non-availability of the functionality

LGOD web portal functionalities Links to external sites OGD web portals are additionally required to embed links to external data sources. This serves as a guide to data users to access related data stored on other public servers. If an OGD web portal does not have an ownership right to a data, providing the external link aids data users to access it on a platform that holds the valid license.

Social media integration

This World Bank Technical Option recommends the integration of social media plugins in OGD web portals. This in line with contemporary ways of engaging with people, ensures that users are able to share their views, experiences and general concerns on the web platform to be subsequently addressed. The feature therefore checks for the inclusion of at least one social media plugin. If this criterion is satisfied, the value 1 is assigned, otherwise a 0 indicates an absence.

License

The World Bank's technical option on open data stipulates that OGD web portals display license for each dataset. In view of this, this feature checks for the existence of an open data license on the web platform. Typically, most OGD portals are licensed either under Creative Commons or the Open Data License. Some other standards are used by some LGODs.

Data Collection -LGOD Web Portal Audit Criteria

Attributes used in the study as shown in Table 1 were guided by requirements put forward by the W3C and the World Bank regarding OGD web data publication. Some common standards shared by the two bodies are (1) the need for datasets to be released or published in their raw forms (2) a metadata supplied for each dataset (3) data to be published in machine-readable, non-proprietary electronic formats such as CSV, JSON, XML, KML etc. ( 4) an open license to be provided by each data web portals and (5) a data visualization tool to guide data users. The criteria (attributes) for the audit were divided broadly into two categories namely; web portal contents and functionalities and are shown in Table 1. This approach is similar to the one used in [START_REF] Afful-Dadzie | Open Government Data in Africa: A preference elicitation analysis of media practitioners[END_REF]. 1. Most of the functions and content of the portals were identified on the home page through visual examination. However, some required a thorough search through all the web pages as well as the html source codes to ascertain the presence or the absence of the feature in question. If a criterion is available on the web portal, the feature was assigned 1 to indicate its presence, otherwise, the feature was assigned a 0 to mean an absence.

K-Means Clustering

Clustering methods partition data points into homogenous groups called clusters. The K-Means clustering is an unsupervised algorithm that seeks to detect natural groups in unlabeled data. The term unlabeled is used to mean data that does not have pre-defined output [START_REF] Rai | A survey of clustering techniques[END_REF]. In K-Means, the number of clusters are typically chosen apriorimeaning clustering intends to partition n objects into k clusters in which each object belongs to the cluster with the nearest mean. The goal of this algorithm is to find groups in the data, with the number of groups represented by the variable K. The algorithm works iteratively to assign each data point to one of K groups based on feature similarity. The rationale behind the use of the K-Means algorithm in this study was to find LGOD early adopters who share similar features in web content and functionality and to further determine whether they are in the same country or otherwise. The next section presents research questions to help elicit the right answers from the study.

Research Questions

The following research questions were used to guide the study. The questions are further analyzed in the result section. Kingdom, France and Australia (See Figure 1). There are however three LGOD adopters in South America (Brazil, Argentina and Chile) and in Asia (Taiwan, South Korea and China). No African city or region is currently implementing an OGD web portal separate from its country-level portal as of the time of this paper. Preliminary audit report as seen in Figure 1 shows that, the U.S. and Canada are currently the leading implementers of LGODs in the world. LGODs belonging to the category of provinces/federal states/regions while 195 of them were cities/towns/counties. In terms of content and functionalities provided on LGOD web platforms, the audit data showed that on the whole, most LGOD early adopters are providing adequate OGD web functionalities and content that meet most of the standards put forward by W3C and the World Bank. For instance, in terms of web functionalities, the study observed that most local authorities, representing 84.25% were providing data search features, 78.02% provided open data licenses and 63.7% had social media plugins integrated in their web portals. Similarly, in terms of web content, most LGODs representing 77.3% provided metadata to accompany datasets, 59.7% had up-to-date data sets and majority had a wide range of data formats on offer to help data users to easily access, share and redistribute data. In particular, it was observed that the most used data format on most LGOD web platforms was the comma separated value (CSV), which was present on 70.7% of the LGOD web portals audited. Other notable data formats heavily in use are XML, data APIs and JSON data formats as shown in Figure 2. The use of the non-proprietary data formats is welcoming since it supports some of the ideals of OGDto make public data progressively free and easily accessible. There are however other data formats that are not being giving much attention. For instance, majority of the LGODs were silent on geographical data formats such as GeoJSON, KML and shape files. The audit data further showed that, most of the geographical data formats were provided by local government authorities in Canada; mostly in the form of either KML of GeoJSON files. There were some equally important OGD web portal features that are not particularly being paid attention to. Some of these are data visualization (previews) and links to external data sites. These requirements are both suggested by the W3C and the World Bank as key to aiding data users in their search of public data. Data visualization for instance ensures that the user previews the data with graphical tools before download. In spite of some of the short comings with LGOD web portals, there is a general satisfiable trend by most of the early adopters to adhere to the international standards for publishing OGD.

Overall, the level of adherence to international standards as far as LGOD's are concerned can be described as high.

The analysis further focused on detecting natural groups or clusters within the LGOD early adopters as stipulated in RQ2. The study sought four groups (k=4) in the data, separately for OGD content and OGD functionalities. In all, there were 73 LGODs in cluster 1, 63 in cluster 2, 66 in cluster 3 and 71 in cluster 4. The U.S and Canada dominated in all the clusters contributing a total of 94 and 49 local government authorities out of the 273 in our study.

Fig. 3. Strength of LGODs in terms of OGD web contents

Overall, LGODs in clusters 3 and 4 respectively provide the most OGD features in terms of content and functionalities. Contributing to 16.67% of the local authorities in cluster 3, Canada leads in terms of the provision of OGD content benchmarked in the study. The U.S, Spain, Italy and France follow Canada in cluster 3. Similarly, in cluster 4, the U.S contributes to about 63.4% of the total LGODs, and therefore provides far richer OGD functionalities and services than all other countries observed in the study. Canada, Italy, Spain and France follow in that order in terms of OGD functionalities. Specific to OGD contents, the strength of LGODs in cluster 4 as shown in Figure 3, is the provision of features such as metadata, non-proprietary data formats and current datasets. For instance, in terms of data formats, LGODs in cluster 4 provide most of the recommended non-proprietary formats such as CSV, JSON, XML, RDF, as well as RSS and data APIs. However, LGODs in cluster 3 of which Canada dominates, focus more on geographic data formats such as Shape, GoeJSON and KML/KMZ. Geographic data is increasingly becoming one of the sought after data on OGD web portals and therefore adequate provision of appropriate Geo data formats would ease access of such data. Though overall, the clustering shows that, LGODs in clusters 1 and 2 lag behind their counterparts in clusters 3 and 4, one significant observation seen in cluster 2, is the provision of the XLX(S) data format. This spreadsheet data format is proprietary, and therefore not recommended highly by OGD web publishing standards. In terms of provision of OGD web functionalities, clusters 3 and 4 again provided more OGD functionalities than the other clusters. For instance, the strength of LGODs in cluster 3 can be seen in the provision of open data licenses and links to external data sites.

LGODs in cluster 4 on the other hand, provided more of data visualization and data search functionalities.

LGODs in cluster 1 urged slightly above clusters 3 and 4 in terms of provision of social media integration plugins.

Discussion and Conclusion

The results of the LGOD web portal inventory audit give a general insight into the trends, relationships and similarities among LGOD implementers around the world. In terms of geographical distribution of LGODs, the apparent trend shows early adopters are mostly found in the global north. Significantly, North America represented only by the U.S. and Canada have a combined share of 41.4% of the total LGODs in the world. There are no cities, federal states, provinces or regions in Africa currently implementing the concept of a decentralized open government data. In Asia, one city each in China and South Korea and two in Taiwan are the early LGOD implementers. In South America, Brazil leads in terms of the number of early LGODs, followed by Argentina and Uruguay. Front runners of LGODs in Europe are Italy, Spain, U.K, France, Germany, Austria, Netherland, Finland, Sweden and Belgium in the order of number of local authorities involved. Australia represents the Oceania as the only implementer of LGODs.

The LGOD web audit afforded the opportunity to find clusters or natural groups into which LGOD early adopters belong. The clustering scheme though not meant to be a score card of performance of the LGODs, give an indication of the strength of each cluster in terms of which OGD features they provide to data users. This approach is to aid policy makers to support seemingly struggling LGODs in terms of technical support and resources. For instance, local authorities in cluster 1, lag far behind in terms of provision of standard OGD features. Therefore, though a welcoming attempt by local authorities in cluster 1, it is observed that they are not adhering to international OGD publishing standards. Such cities, towns, federal states, provinces etc. would need to be supported to attain the right standards. There is a general lack of uniformity by LGODs in terms of the content and features they publish for their data users. The seeming heterogeneity among LOGD web portals is affirmed by [START_REF] Sáez Martín | An international analysis of the quality of open government data portals[END_REF] and [START_REF] Zuiderwijk | Open data policies, their implementation and impact: A framework for comparison[END_REF]. For instance, even within the same country, the web template as well as the OGD features used tend to be different at the national, state and city levels. Apart from the U.S. which for the most part provides a common OGD web template for both its national as well as local authorities (federal states, cities, towns and counties), most of the 27 countries have their OGD web architecture different from their local authorities.

RQ1. 4 Results

 4 What are the global trends as far as local government open data initiatives are concerned? RQ2. Which cities, federal states or provinces fall into unique natural groups and why? RQ3. What do the similarities and dissimilarities among early LGOD adopters say about local government adherence to OGD web publication standards? This section provides answers to research question 1, which sought to understand the global trends as far as local government open data initiatives are concerned. Currently, as of 2016, there are about 27 sovereign countries around the world where local government authorities have launched completely separate OGD portals that are different from what their central governments operate. Conveniently referred to as 'early adopters' in this study, majority of them are in the U.S., Canada, Italy, Spain, United

Fig. 1 .Fig. 2 .

 12 Fig. 1. Country representations of early LGOD adopters.

  The list of local authorities (cities, counties, municipals, federal states, regions and provinces) were collected from open government data U.S. (https://www.data.gov/open-gov/) and Data Portals (http://dataportals.org/search). In all, 288 local governments were identified as operating independent OGD web portals different from their national portal. In this paper, local administrations around the world that have shown early interest in LGOD are classified as early adopters. During the web portal audit, about 15 LGODs were found to have nonfunctional Uniform Resource Locators (URLs) and therefore could not be accessed. This brought the total number of LGOD portals studied in this paper to 273. The data collection period was from November, 2016 to March, 2017. A web content analysis was carried out to determine how each local government authority faired on each of the criteria. To this effect, the web portal of each LGOD was examined to identify the features outlined in Table


Anupam Nanda
email: a.nanda@henley.reading.ac.uk

Does It Pay to be Socially Responsible for Construction Companies?

Keywords: Built Environment, Corporate Social Responsibility, CAPM

Within the built environment, the Engineering and Construction (E&C) companies are very natural resource-intensive in terms of their business operations. In this paper, we focus on publicly listed E&C companies and analyse the role of Corporate Social Responsibility (CSR) activities on their corporate financial performance. The analytical framework is built around the economic theory of private provision of public goods. A basic Capital Asset Pricing Model (CAPM) is used to empirically examine the testable hypothesis with a panel data comprising 17 major E&C companies with monthly data over 2000-13. The results indicate that CSR activities can influence financial performance significantly after controlling for the firm size variable. We make use of several measures of CSR activities to test robustness. The broad results are robust to a range of alternative model specifications.

Introduction

The role of Corporate Social Responsibility (CSR) in corporate performance has been explored extensively in the literature. While our understanding has improved, it is far from being definitive as findings remain mixed and new issues and challenges have emerged in recent years. With growing concerns around climate change and increasingly loud calls for sustainable business practices from all corners of the society, the role of a corporation is being redefined and broadened to include a stronger commitment to the sustainability issues of respective businesses. Therefore, the companies need to look beyond the Corporate Financial Performance (CFP) and focus on Corporate Social Performance (CSP). A natural question arises: how CSR activities are associated with corporate performances? A substantial share of the economic activities that directly comes under the sustainability scanner is channeled through the built environment. Within built environment, the Engineering and Construction (E&C) companies are perhaps the most natural resource intensive in terms of their business operations. The E&C companies, being unavoidably resource-intensive, are often subject to public dissent and face government controls and restrictions. Across the world, the E&C companies are expected to contribute to the society. Often such activities include mitigation of environmental impacts, sustainability goals, investment into social activities, improvement of working conditions, commitment to employee wellbeing etc. The investment community is also very active in terms of prioritizing investible resources in companies with high CSR activities and performance. As a result, the marketplace may recognize the value that CSR activities can bring in terms of enhanced image or brand equity, increased revenue and ease of access to socially responsible funds. Many studies have contested whether the CSR profile of a company influences its financial performance. This paper focuses on seventeen major E&C companies in order to identify and evaluate the relationship between their Corporate Social Responsibility (CSR) and their financial performance by using publicly available data and a Capital Asset Pricing Model (CAPM). A number of econometric techniques are employed to extract best possible information from the data in order to inform the research question. To this end, we firstly present the context of our research question with a brief overview of the extant literature. Then, the empirical framework is discussed, followed by data description and results. Finally, some concluding remarks are provided.

Context and Related Literature

The idea of corporate social responsibility (CSR) came through concerns coming out of sustainable future, global warming and environmental risks. This means, companies will need to invest a part of their hard-earned profit back into society. The argument is: since companies use resources from the society (i.e. people/human capital and natural resources) to fulfil their profit motive, they also need to give back to the society. However, this may also go against the promises to the shareholder of increasing profit/dividend. Milton Friedman famously argued that 'the sole social responsibility of business is to increase its profits'. So, why should a company engage in such activities, which are apparent net draws to their surplus cash? This question has been repeatedly asked over the last few decades and therefore, the literature is rather extensive. It can be argued that CSR activities should also lead to increased revenue and performance of the company. It can be claimed that CSR activities can improve firms' competitive advantage by attracting socially responsible consumers and as a result of being a 'good corporate citizen', firms can enjoy enhanced image and reputation. Indeed, it is often argued that such brand enhancement and strategic positioning can successfully yield additional advantages linked to insulation from regulatory risk, public dissent, government sanctions and disruptions in activities, due to negative campaigns by social and environmental activists and non-governmental organisations [START_REF] Bagnoli | Selling to socially responsible consumers: competition and the private provision of public goods[END_REF][START_REF] Maxwell | Self-regulation and social welfare: The political economy of corporate environmentalism[END_REF].

The empirical literature examining the effect of CSR activities on firms' financial performance is long and it is fraught with mixed and inconclusive results [START_REF] Mcwilliams | Corporate social responsibility and financial performance: correlation or misspecification?[END_REF][START_REF] Mcwilliams | Corporate social responsibility: a theory of the firm perspective[END_REF][START_REF] Mcwilliams | Corporate social responsibility: strategic implications[END_REF]. The main reason is our inability to understand the mediating factors and data constraints proving significant in creating estimation biases (for reviews see, [START_REF] Stanwick | The Relationship between Corporate Social Performance, and Organizational Size, Financial Performance, and Environmental Performance: An Empirical Examination[END_REF][START_REF] Roberts | Corporate reputation and sustained superior financial performance[END_REF][START_REF] Margolis | Does It Pay to be Good? A Meta-Analysis and Redirection of Research on the Relationship Between Corporate Social and Financial Performance[END_REF][START_REF] Van Beurden | The Worth of Values -A Literature Review on the Relation Between Corporate Social and Financial Performance[END_REF] and see Fig. 1). In appraising the literature of CSR across the construction industry, Jones et al. [START_REF] Jones | Corporate Social Responsibility and the UK Construction Industry[END_REF] investigated the CSR issues of the UK's biggest construction companies. In their paper, they discussed the main characteristics of the CSR and the construction sector challenges by using the CSR reports and online published information of the leading construction companies of the UK. Their findings revealed very significant variations in the CSR reporting mainly focusing on environmental issues, but also, their research addressed issues related to health and safety, supply chain management, human resources, the communities, the government, ethics, etc. The authors suggested that although construction companies are taking the CSR principles into consideration, however, their performance indicators show poor evidence towards this direction as well as having low participation rates in the general benchmarking exercises. Moreover, Zhao et al. [START_REF] Zhao | A corporate social responsibility indicator system for construction enterprises[END_REF], by taking into consideration the CSR factor and especially its significance in the construction industry, developed a framework for CSR indicators for the construction enterprises globally as a tool for CSR performance. They considered the CSR stakeholders who are involved in the construction sector and proposed a CSR indicator system based on the stakeholders' theory.

Fig. 1. Conundrum of CSR and Financial Performance

Above literature review indicates that there is a wide-spread recognition of the impact of CSR/sustainability activities on firm-level performances. However, the effect is lot less clear when we try to quantify it. Robust empirical analysis of the dynamic relationship between CSP and CFP requires some consideration of a number of potential issues. Any empirical investigation is fraught with significant data constraints and estimation biases. With these caveats, we move on to an empirical investigation of the relationship between CSP and CFP for seventeen E&C companies.

Empirical Framework

In terms of empirical modelling, there are several approaches that have been tried in the literature. Ever since the CSR ratings data became available across a number of companies over time, some studies have modelled the effect of the CSR rating on Tobin's q and total returns. While all modelling approaches are contested on some grounds, there is no definitive agreement in the literature on the best empirical framework. In this paper, we employ a tested and long-established asset pricing model -Capital Asset Pricing Model (CAPM). Sharpe's [START_REF] Sharpe | Capital Asset Prices: A theory of Market Equilibrium under Conditions of Risk[END_REF] and Lintner's [START_REF] Lintner | The Valuation of Risk Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgets[END_REF] pioneering works, among others, have established the CAPM model as one of the most well-known and most frequently used models. The basic framework is represented as follows:

(𝑟 𝑖𝑡 -𝑟𝑓 𝑡 ) = 𝛼 + β 1 (𝑟𝑚 𝑖𝑡 -𝑟𝑓 𝑡 ) + u 𝑖𝑡 (1)
rit is the return rate of firm i's stock in time t; rfit is the return of a risk-free asset in time t that may correspond to firm i; rmit is the market's (benchmark index) return rate in time t that may correspond to firm i. The coefficient beta reflects the systematic risk or sensitivity of the expected excess asset returns to the expected excess market returns. The most stringent criticism against CAPM model has been the low explanatory power or r-squared and strong assumptions regarding equal and symmetric information processing by all economic agents. However, the ability to control for other confounding factors into the equation ( 1) is very useful and can aid in answering many questions. Much like the approach in Cardebat and Sirven [START_REF] Cardebat | What corporate social responsibility reporting adds to financial return[END_REF], we can add other control variables such as CSR and firm size as in equation ( 2) below.

(𝑟 𝑖𝑡 -𝑟𝑓 𝑡 ) = 𝛼 + β 1 (𝑟𝑚 𝑖𝑡 -𝑟𝑓 𝑡 ) + β 2 𝑐𝑠𝑟 𝑖 + β 3 (𝑜𝑡ℎ𝑒𝑟 𝑐𝑜𝑛𝑡𝑟𝑜𝑙𝑠) + u 𝑖𝑡 (2)
In equation ( 2), we also add other controls that may potentially impact the firm's performance such as number of employees, market cap and productivity (calculated as market cap per employee). We also try several alternative measures of CSR (see Table 1 for description of variables). Since our dataset covers 17 firms observed over 157 months (Dec. 2000 -Dec. 2013), we can model equation [START_REF] Maxwell | Self-regulation and social welfare: The political economy of corporate environmentalism[END_REF] within panel data framework. The advantage of this method is that it allows us to use both time series and cross sectional variations in the data, which increases the efficiency of the OLS estimates. A typical feature of panel data framework is the presence of unobserved heterogeneity. A potential bias in estimating equation ( 2) is the presence of correlation between unobserved heterogeneity at the firm level and the observables, which would otherwise violate standard assumptions of OLS estimation. Therefore, the disturbance term in equation ( 2) is specified as a twoway error component capturing firm-specific fixed effects and time-specific effects.

u 𝑖𝑡 = γ 𝑖 + δ 𝑡 + ϵ 𝑖𝑡 (3)
In this specification, heterogeneity is assumed to be constant over time and correlated with independent variables. The constant effect is removed by mean-differencing the data. This estimation strategy may be consistent with theoretical expectations that firmspecific unobserved characteristics can bring in permanent differences in individual firm's financial and corporate situation. Finally, due to presence of large variation in firm size, type of business and corporate structure, it is quite likely that the variance of the error term in regression models may be non-constant, which would violate one of the key assumptions of OLS modelling.

To address this inherent heteroscedasticity problem, we estimate all models with heteroscedasticity-robust standard error.

4

Data, Results and Analysis Some companies have missing observations over certain period i.e. the regression dataset is an unbalanced panel. The stock price variable is obtained from Google finance (listed in NYSE and NASDAQ) and we use year-over-year return in excess of risk-free rate (10-year US Treasury bond) as the dependent variable. The market benchmark is the Dow Jones Sustainability Index (DJSI). DJSI is very appropriate as a benchmark for our question since it is most well-established and keenly followed market index measuring sustainability performance. The Dow Jones Sustainability World Index was launched in 1999 as the first global sustainability benchmark. The index family tracks the stock performance of the world's leading companies in terms of economic, environmental and social criteria.

The most important variable in question is the CSR measure. We use the CSRHub rating (CSR rating) as a starting point. We have also used the firm's rating compared to all company average of CSR rating (CSR excess). Since there have been significant criticism of such ratings, we have created our own subjective measure of website content on CSR activities to rate companies (CSR web) as follows (as in 2014). 5 = fully dedicated and prominent link in home page with full details across ESG parameters with visible sustainability reports; 4 = dedicated and prominent link in home page with some details across ESG parameters with visible sustainability reports; 3 = webpage with few details across ESG parameters with few visible sustainability reports; 2 = webpage with little details across ESG parameters with no visible sustainability reports; 1 = no webpage with poor details across ESG parameters with no visible sustainability reports;

Other controls that we have examined are: number of employees obtained from Google Finance and company websites; market cap sourced from Google Finance; productivity calculated as market cap divided by number of employees. We first estimate equations 1 and 2 with the full sample. Table 2 reports seven model specifications. Model 1 is the baseline CAPM model that incorporates the size variables that may explain firm's performance. The number of employees has significant negative effects on excess return and market cap has significantly positive effects. When we add CSR variables in model 2, these results remain qualitatively unchanged. The CSR rating variable is significantly positive, while the CSR web variable affects financial performance negatively. The growing importance of online materials and use of such material for marketing purposes are important and this may play a role in negative effect on firm's performance. To address the problem of estimation in various firm sizes, we have used productivity variable in model 3, replacing other two size variables. It is possible that CSR activities are mostly viewed in a comparative sense i.e. compared with the market or industry standards. Therefore, in model 3, we include CSR excess computed as (all company average minus the firm's CSR rating) and it turned out to be negative which implies that if a company is under-performing compared to the all company average, it can entail significantly negative effect on financial performance. It is also important to note that issues of multicollinearity can be problematic and therefore, we have maintained parsimony in model specification. We find that the multicollinearity issue is not severe in our framework. 2 do not control for unobserved heterogeneity. As explained before, it is possible to model unobserved heterogeneity in two waysrandom effects and fixed effects. In model 4, we assume random effects and we assume fixed effect structure in model 5. We have performed Hausman specification test considering both models 4 and 5, and the results consistently suggested that a fixed effects specification was more appropriate [START_REF] Hausman | Specification Tests in Econometrics[END_REF]. Therefore, we take model 5 forward. The explanatory power (24%) of model 5 is highest among all models in Table 2 while this level of rsquared is not ideal but compared to many studies this is reasonable. In models 4 and 5, our CSR measure is time-varying, CSR int. Specifically, we interact CSR excess with DJSI total return with all ethical exclusion tags such as alcohol, tobacco, gambling, armaments, cluster bombs, landmines, firearms, nuclear and adult entertainment. While a valid concern against such interaction term is multicollinearity, we have found very low correlation (negative 3.1%) between CSR int and Market return. Moreover, such measure is valid as it compares CSR activities with that of all company average and also with the stringent DJSI sustainability index. We find significant positive effect (0.037) of excess CSR activity on financial performance in model 5. In our modelling framework and hypothesis testing, sign and significance of the effect is more relevant than the size of the coefficient. In general, we find that the CSR measures have significant effect on financial performance according to the CAPM modelling framework.

Conclusion

Being resource-intensive, the E&C companies often face government sanctions, public dissent and disruption of operations. The outcry of complying with strict sustainability regulations is perhaps louder for E&C companies than many other sectors. Therefore, it is quite an interesting exercise to study whether a 'responsible' E&C company 'does well by doing good' to the society. The empirical investigation of this question is fraught with many challenges starting with lack of good quality data on CSR activities.

In this paper, we focus on seventeen publicly listed E&C companies and analyse the role of their CSR activities on their corporate financial performance. The analytical framework is built around the economic theory of 'private provision of public goods'. We use basic Capital Asset Pricing Model (CAPM) to empirically examine the testable hypotheses. The model is enhanced by adding firm size and productivity controls that can boost the explanatory power of the specifications. Fixed effect modelling approach has been adopted to mitigate any attenuation bias in estimates from unobserved heterogeneity. The analysis of monthly data over Dec. 2000 -Dec. 2013 reveals that CSR activities can significantly influence financial performance after controlling for firm size variable. The broad results are robust to several alternative model specifications. More data and better estimates of CSR activities are some areas of future research.

  Our data comprises monthly returns across seventeen E&C companies over 157 months of December 2000 -December 2013 i.e. 2669 panel observations. The time period covered in this study is interesting as we can observe two economic cycles and moreover, the CSR activities and awareness took considerable prominence during this time period. The seventeen companies are:

	Fluor Corporation, KBR Inc., URS Corporation, McDermott International, Tutor
	Perini Corporation, CB&I/Shaw Group, Jacobs Engineering Group, Granite Con-
	struction Inc., Willbros Group Inc., Babcock & Wilcox Company, Orion Marine Group
	Inc., Layne Christensen Company, Foster Wheeler AG, First Solar Inc., Matrix Service
	Company, Great Lakes Dredge & Dock Corporation and Sterling Construction Com-
	pany.

Table 1 .

 1 Variable Description and Sources of Data

	Variable	Description	Sources
	Stock return	Year-over-Year return	Google Finance
	Market return	Year-over-Year benchmark return Dow Jones Sustainability
			Index -http://www.sus-
			tainability-indices.com/
	Risk free rate	10 Year Treasury Yield	US Federal Reserve
	Employees	log(no. of employees)	Company Websites,
			Google Finance
	Market cap	log(market cap)	Google Finance
	Productivity	log(market cap divided by no. of
		employees) -proxy for produc-
		tivity
	CSR rating	Rating of individual companies	CSRHub -
			https://www.csrhub.com/
	CSR excess	CSR Rating compared to the all	CSRHub -
		company average	https://www.csrhub.com/
	CSR web	Author' subjective evaluation	Company Websites
		(scale of 1-5, 5 being best) based
		on content on individual company
		website
	CSR int	CSR Rating interacted with
		month-to-month return of DJSI
		Sustainability Index with all ethi-
		cal exclusions

Table 2 .

 2 Full Sample Regression Results

		(1)	(2)	(3)	(4)	(5)
	Excess market	1.421***	1.411***	1.416***	1.420***	1.422***
	Return	(25.02)	(24.91)	(25.11)	(21.30)	(21.36)
	CSR rating		0.009***
			(3.30)
	CSR web		-0.042**
			(-3.04)
	CSR excess			-0.006*
				(-2.08)
	CSR int				0.006	0.037**
					(0.92)	(3.04)
	Employees	-0.038**	-0.027*
		(-2.94)	(-2.08)
	Market cap	0.056***	0.069***
		(4.33)	(5.55)
	Productivity			0.055***	0.076***	0.083***
				(4.36)	(7.74)	(8.07)
	Intercept	-0.408**	-1.051***	-0.238**	-0.387***	-0.402***
		(-3.17)	(-5.04)	(-2.89)	(-4.74)	(-5.56)
	Model Specifica-	OLS	OLS	OLS	Random	Company
	tion				Effects	Fixed Ef-
						fects
	R-squared	0.198	0.204	0.198	0.189	0.241
	N	1969	1969	1969	1969	1969
	t statistics based on robust standard error in parentheses;
	~ p<0.1; * p<0.05; ** p<0.01; *** p<0.001;
	Models 1 through 3 in Table