Giannis Milolidakis

Demosthenes Akoumianakis

Chris Kimble

Digital traces for business intelligence: A case study of mobile telecoms service brands in Greece

Keywords: Virtual communities, Social media, Business intelligence, Facebook pages, Virtual excavations

Purpose -Data from social media (SM) has grown exponentially and created new opportunities for businesses to supplement their business intelligence (BI). However, there are many different platforms all of which are in a constant state of evolution. The purpose of this paper is to describe a generic methodology for the gathering of data from SM and transforming it into valuable BI. Design/methodology/approach -The approach taken is termed virtual excavation and builds on the similarities between the manipulation of technological artefacts virtual communities using various forms of SM and the excavation and analysis of physical artefacts found in archaeological settlements. Findings -The paper reports on a case study using this technique that looks at the Facebook fan pages of three mobile telecommunications service providers in Greece. The paper identifies many of the standard BI indicators as well as demonstrating that additional information relating to cross-page use can be collected by looking at how users manipulate artefact such as the "like" button in Facebook. Research limitations/implications -Although the methodology is widely applicable, the paper only reports on the analysis of one platform, Facebook, and is heavily reliant on visualization tools. Future work will examine different platforms and different tools for analysis. Practical implications -The paper discusses some of the ways in which this approach could be used and suggests some areas in which it might be applied. Originality/value -The approach of using virtual excavations to extract BI from virtual communities in online SM offers a systematic approach for dealing with a variety of information from a variety of different media that is not found in techniques based on information systems or management science.

Introduction

Business Intelligence (BI) has existed as a term since 1958 when Hans Peter Luhn [START_REF] Luhn | A business intelligence system[END_REF] used it to describe an automatic system for information dissemination by utilizing dataprocessing machines for abstracting, encoding and archiving any kind of documents of an organization. In this early conception of BI, the term business was considered as the "collection of activities carried on for whatever purpose" while the notion of intelligence was defined as "the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal". The overall objective of the system proposed by Luhn was to support organizational actions, speedily and efficiently, by providing appropriate information. Since such early work, BI research has grown and expanded in both range and scope including, from a technical viewpoint, advances in computer-aided decision and data mining systems, and from a managerial perspective, ways of getting the right information to the right people at the right time. Consequently, BI is broadly defined as the process of taking items of data, analysing them, and condensing their essence into the basis of business actions, enabling management to gain new insights and thereby contributing to their business decisions [START_REF] Davenport | Competing on analytics[END_REF][START_REF] Gilad | A systems approach to business intelligence[END_REF][START_REF] Li | Applications of data warehousing and data mining in the retail industry[END_REF]Pirttimäki, 2007a;Tyson, 1986;Watson and Wixom, 2007). Today, BI embraces a variety of activities, including social media analysis, to allow businesses to gain deeper understanding of their organization and build competitive advantage. Social media (SM) refers to a variety of genres of technology with a wide range of capabilities. For example, blogging platforms allow users to maintain a log of their activities, opinion or thoughts, triggering other users' feedback by following or commenting on the content. Micro-blogging services, such as Twitter and Foursquare, make provisions for users to publish short messages regarding news stories, the user's current location, or nearby events and share them with their connections/followers. Social networking platforms such as Facebook allow the construction and maintenance of social relationships among users with common interests or activities. As SM become increasingly available across a wide variety of different devices and platforms, new business opportunities arise and large amounts of social data are generated with the potential to benefit business enterprises.

With respect to BI and SM, a recent survey aimed at businesses and IT executives [START_REF] Stodder | Customer Analytics in the Age of Social Media[END_REF] indicates that 26% of respondents' organizations are currently analysing data from SM; 22% are planning to analyse such data within one year; and 16% are going to do so within two years. From the same report, the top three objectives of organizations pursuing BI with SM are:

(a) gaining a deeper understanding of their customers (56%); (b) identifying paths to buying decisions (31%); and (c) monitoring and measuring sentiment drivers (30%).

It is worth noting that all three objectives relate to gaining a deeper understanding of customers through SM. The above indicates not only the increasing importance of SM for modern organizations but also the compelling need to analyse what is embedded in SM and how this can be used for the benefit of business. The literature indicates that the features embedded in SM include user profiling mechanisms, provisions for creating and sharing user-generated content, tools for communicating, making and sustaining connections [START_REF] Kim | On social Web sites[END_REF]. However, the possibility of appropriating these features for BI has been only partially explored. For example, [START_REF] Chau | Business Intelligence in Blogs: Understanding Consumer Interactions and Communities[END_REF] concentrate on a multi-method framework for blogs, which may not be applicable to other types of systems. The complexity of the task stems from the fact that there are a variety of systems (e.g., blogging and microblogging platforms, pages in social networking services, channels in video-sharing services) that contain a variety of data (e.g., popular opinion on products, services, market trends and information about competitors). Moreover, such information is typically embedded in a number of different artefacts, such as unstructured text, videos, patterns of user actions, etc., which are not uniform and do not afford pre-determined and standard manipulative actions. Complexity also results from constant evolution of SM where new functions (e.g., new versions of public APIs, improved search mechanisms, etc) are introduced in order to update and improve existing services. This makes it harder to find a one-off solution for collecting and analysing data contained in SM; something that could be much more easily achieved in the more stable environment of a traditional business supported forum or a bulletin board. Consequently, there is a need for a methodology to guide both the efficient gathering of the right data at the right time, and their processing and transformation into valuable BI. We believe that our research will contribute to this by describing a method grounded in a conception of viewing SM as virtual settlements.

The notion of a virtual settlement was introduced by [START_REF] Jones | Virtual-communities, virtual settlements & cyber-archaeology: A theoretical outline[END_REF] in an effort to understand virtual communities through what he termed cyber-archaeological inquiries. Re-focusing the problem from virtual communities to more general cyber-phenomena allows us to approach social technologies as a kind of archaeological settlement whose excavation may facilitate the discovery of BI. Specifically, we argue that SM such as social web sites and social networking services host a wealth of data, which are made available with the end users' consent, that reveal not only cultural information about past and / or on-going online communities but also about market trends, consumer behaviour and other business related insights. Depending on the theoretical footing and research strand, such information can be processed and analysed from various perspectives such as social network analysis [START_REF] Scott | Social network analysis[END_REF], ethnographic assessments [START_REF] Harrison | Excavating Second Life[END_REF], data mining or information discovery [START_REF] Fayyad | From data mining to knowledge discovery in databases[END_REF]. This article describes a systematic method for conducting virtual excavations in online computer-mediated settlements and demonstrates the application of this method to BI through a case study of three mobile telecommunication services in Greece.

The article is structured as follows. The section "Theoretical Motivation & Research Objectives" provides a critical appraisal of current conceptions and prominent efforts to advance BI by exploiting SM. Our analysis reveals not only the need for systematizing inquiries but also shortcomings in the type and scope of available perspectives. Building on this, the "Methodology" section describes our method, emphasizing the features that make virtual excavation a useful metaphor for analytical insight. The method is then validated using a case study covering the telecommunications sector in Greece. The article concludes with a critical discussion on the findings, the implications and the limitations of current work and directions for our future work

Theoretical Motivation & Research Objectives

Striving for BI has been a long-standing aim of firms in different sectors of industry (Wixom and Watson, 2010). This is evidenced not only by investments made, but also by the intensity of research that characterizes the field. This section offers a synthesis of past and on-going research efforts in the area of BI, which provides the motivation and focus of our present work.

BI and recent advancements

Since it was first coined in 1958, the term BI has acquired a number of different connotations (Pirttimäki, 2007b). [START_REF] Gilad | A systems approach to business intelligence[END_REF], referred to BI as a process that produces relevant and reliable information for a company's strategic goals and objectives. [START_REF] Ghoshal | Building Effective Intelligence Systems for Competitive Advantage[END_REF] recognized BI as an essential competitive tool for the collection and analysis of information on markets, new technologies, customers, competitors and broad social trends. Later, Vedder, et al (1999) suggested a dualistic approach referring to BI as both a process and a product. According to Vedder et al, BI as a process is "the set of legal and ethical methods a company use to harness information" and as a product, it is the "information about competitors' activities from public and private sources". In her doctoral dissertation, Pirttimäki (2007a) examined several different views of BI describing it as a multifaceted concept that refers to processes, technologies, methods, information products and tools to support managing business information and making faster and better decisions. Computerized systems for BI are commonly referred to as decision making tools [START_REF] Power | A Brief History of Decision Support Systems[END_REF] while data mining techniques [START_REF] Cabena | Discovering data mining: from concept to implementation[END_REF] are often used to reveal hidden information from massive amount of data. In spite of their high added value, BI efforts are not without their critics. It is often claimed that BI delivers reports and analytical insights expost to an event. In other words, by the time BI results are available, key decisions and/or commitments have already been made. Others criticize the high investments made in infrastructure, storage and processing of data, while others point to the lack of systematic and methodological frames of reference to integrate BI processes into organizational routines, which would improve BI's economic efficiency and efficacy.

Increased recognition of these problems has led researchers to consider a variety of improvements and extensions in the traditional toolset of BI. For instance, efforts concentrate on real-time or near real-time BI [START_REF] Azvine | Towards real-time business intelligence[END_REF]Watson et al., 2006) to reduce the latency between operational data and the analysis over those data. Similarly, cloud based BI solutions aim to address the increased cost of building and maintaining data warehouses [START_REF] Gurjar | Cloud Business Intelligence -Is What Business Need Today[END_REF]. Pervasive BI [START_REF] Mckendrick | Pervasive Business Intelligence Means "BI for the Masses[END_REF]Vesset and McDonough, 2009) and BI 2.0 [START_REF] Nelson | Business Intelligence 2.0: Are we there yet[END_REF][START_REF] Trujillo | Business Intelligence 2.0: A General Overview[END_REF] take advantage of the capabilities of web 2.0 technologies to bring BI to as many users of the enterprise through interactive web applications. Finally, the current discussion of the future of BI [START_REF] Chen | Business intelligence and analytics: From big data to big impact[END_REF] is concerned with BI and mobile devices (BI 3.0), in particular exploiting data produced by the increasing use of ubiquitous computing and technologies such as RFID, barcodes, and radio tags. As this work concentrates on BI using SM, the following section will focus on more detailed account of work in this area.

BI and related attempts on SM

SM have become the focus of increased attention in the BI field because most users' daily activity on the Internet takes place in these platforms [START_REF] Kaplan | Users of the world, unite! The challenges and opportunities of Social Media[END_REF]. Recent research has examined the impact of SM in decision-making [START_REF] Meredith | A Framework for Understanding the Role of Social Media in Business Intelligence Systems[END_REF]Power and Phillips-Wren, 2011); how SM drives business performance in terms of marketing and brand awareness [START_REF] Callarisa | Harnessing social media platforms to measure customer-based hotel brand equity[END_REF][START_REF] De Vries | Popularity of Brand Posts on Brand Fan Pages: An Investigation of the Effects of Social Media Marketing[END_REF]; communication and reputation management [START_REF] Seebach | Sensing Social Media for Corporate Reputation Management: A Business Agility Perspective[END_REF]; product development and crowdsourcing [START_REF] Piller | From Social Media to Social Product Development: The Impact of Social Media on Co-Creation of Innovation[END_REF][START_REF] Tran | Crowd Participation Pattern in the Phases of a Product Development Process that Utilizes Crowdsourcing[END_REF]; human resource management [START_REF] Kluemper | Future employment selection methods: evaluating social networking web sites[END_REF][START_REF] Roberts | Social Networking Web Sites and Human Resource Personnel[END_REF] and the use of SM to increase enterprises' intellectual capital [START_REF] Sussan | Consumer interaction as intellectual capital[END_REF]Vasileiadou and Missler-Behr, 2011). In addition, SM platforms' openness and interoperability make it easy for third-party applications to exploit their features and the user-generated content they contain for business-related purposes. Nevertheless, the challenges involved are numerous and concern not only technical problems but also issues related to ownership, security and privacy [START_REF] Rosenblum | What anyone can know: The privacy risks of social networking sites[END_REF]. Table 1 summarizes some indicative examples of recent works that have recognized the value of such data and have striven to realize the potential benefits of SM data for the enterprise.

[Insert table 1 near here] A review of these studies quickly confirms that a number of different strategies are available for appropriating social data [START_REF] Kietzmann | Social media? Get serious! Understanding the functional building blocks of social media[END_REF]. A popular approach relies on SM monitoring software [START_REF] Laine | Monitoring Social Media: Tools, Characteristics and Implications[END_REF][START_REF] Mayeh | Understanding the role of social media monitoring in generating external intelligence[END_REF] such as Lithioum, Jive, Infegy, Radian, TweetFeel, Twitter Sentiment, Jodange and ScoutLabs. Typically, these are deployed by large enterprises with the intention of 'listening' to user opinions and needs [START_REF] Rappaport | Listen First!: Turning Social Media Conversations Into Business Advantage[END_REF]. Another strategy exploits the concept of business-sponsored virtual communities [START_REF] Füller | Community based innovation: How to integrate members of virtual communities into new product development[END_REF]. These are cyber-structures created by an enterprise to establish a channel for bi-directional interaction with its clientele. These may be used to raise awareness, keep customers informed or stimulate users' involvement in design processes. The choice of infrastructure deemed appropriate for this purpose may vary from social web sites [START_REF] Hagel | Net gain: Expanding markets through virtual communities[END_REF], dedicated practice-oriented toolkits [START_REF] Akoumianakis | Virtual Operations in Common Information Spaces: Boundary Objects and Practices[END_REF], to networking services such as Facebook and YouTube [START_REF] Mangold | Social media: The new hybrid element of the promotion mix[END_REF]. BI tactics also vary. The literature reports on the use of instruments such as design contests, lead user workshops, consumer opinion management, toolkits for user innovation (Von Hippel, 2001), co-design toolkits [START_REF] Franke | How communities support innovative activities: an exploration of assistance and sharing among end-users[END_REF][START_REF] Franke | Satisfying heterogeneous user needs via innovation toolkits: the case of Apache security software[END_REF] and communities for customer co-creation [START_REF] Piller | From Social Media to Social Product Development: The Impact of Social Media on Co-Creation of Innovation[END_REF]. In all cases, the common ground is the desire to gather data which will bring about competitive advantage, particularly in cases such as firms in highly innovative and knowledge-intensive sectors of the industry, where consumer cocreation, product differentiation and product innovation are valued. More recently, BI efforts have been re-directed from the online exchanges of outsiders to the enterprises' internal staff. This perspective has gained considerable attention from middle and higher level management seeking 'inside-the-company' improvements [START_REF] Dimicco | Motivations for social networking at work[END_REF][START_REF] Jackson | Corporate Blogging: Building community through persistent digital talk[END_REF][START_REF] Kim | Investigating the influence of employee blogging on IT workers' organisational citizenship behaviour[END_REF] social networking platform for employees that mimics the way Facebook users engage in conversation.

SM as Virtual Settlements

In many ways, the established approaches to BI using SM have come out of the need for enterprises to appropriate the social web and the increased availability of user-generated content. The efforts reviewed earlier are indicative of the importance of BI for modern enterprises. However, they do not follow a common protocol and do not allow for comparisons across cases. Moreover, they are frequently limited to what is 'traceable' using a certain tool at the expense of what is needed and is most useful for an enterprise. To address these challenges, the present work links to efforts that conceive of BI using SM as a type of archaeological inquiry conducted in a virtual settlement. This perspective aims to bring to the surface digital remains that are of potential value to enterprises.

The analogy between computer-mediated communication spaces and archaeological settlements was initially introduced by [START_REF] Jones | Virtual-communities, virtual settlements & cyber-archaeology: A theoretical outline[END_REF] in his effort to develop a theory of a cyber-archaeology. In its original formulation the concept of a virtual settlement was conceived as a construct for studying virtual communities through their online cultural artefacts. Jones referred to online infrastructuresplaces where online communities can be found -as virtual settlements. Jones' view is based on the conception of virtual communities as socially enacted structures whose formation and sustainability are subject to the following criteria: (a) a minimum level of interactivity; (b) a variety of participants; (c) a virtual commonpublic-space where a significant portion of community interactions occur; and (d) a sustained, stable minimum level of membership. The above establishes the guiding principles for separating virtual settlements from virtual communities. Thus, it is the virtual settlement and not the virtual environment, which is a pre-requisite for an online community. Jones' ideas have been influential amongst social scientists and his cyber-archaeological perspective has been applied to a variety of SM, including Second Life [START_REF] Harrison | Excavating Second Life[END_REF], blogs [START_REF] Blanchard | The experienced sense of a virtual community: Characteristics and processes[END_REF][START_REF] Chin | Finding evidence of community from blogging co-citations: a social network analytic approach[END_REF], the blogoshere [START_REF] Efimova | In search for a virtual settlement: An exploration of weblog community boundaries[END_REF] or blogspaces (Zhou and Davis, 2007), micro-blogging services such as Twitter [START_REF] Gruzd | Imagining twitter as an imagined community[END_REF] as well as social networking services like Facebook [START_REF] Hewitt | Crossing boundaries: Identity management and student/faculty relationships on the Facebook[END_REF]Zhao et al., 2008) and YouTube [START_REF] Akoumianakis | Retaining and exploring online remains on YouTube[END_REF].

These efforts indicate that SM can be subjected to a virtual archaeological inquiry to solicit insights on both the historical traces of users' past activity and on emerging trends and digital culture. Nevertheless, the diversity in the techniques used and the level of fidelity of the results, point to a need for a structured and organized method. The key issues that need to be address by such a method are:

1. What is the phenomenon under investigation? 2. What traces (or artefacts) can be used to provide evidence of this phenomenon? 3. Are these traces of users' activity readily available, and where and how they can be collected? 4. What type of analysis is required?

To gain further insight, it is worth reminding ourselves that traditional archaeological inquiries provide the means for studying human activities through the data that a previous society has left behind. As direct contact with the creators of such data is lacking, archaeologists are primarily concerned with remains that provide information about their creators' lives and culture. Consequently, the baseline of such an inquiry is rooted in a normative perspective that views culture as something that people do at a particular point in time, but which fades into memory and disappears leaving only the artefacts it created [START_REF] Lyman | Archiving digital cultural artifacts[END_REF] be interpreted in relation to the function that they had within the culture in which they were created [START_REF] Hodder | The interpretation of documents and material culture[END_REF].

Virtual excavations of SM sites however differ in several ways. Firstly, the digital objects do not necessarily reflect a distant or past culture. In fact, if this were the case, then their practical value for BI would be questionable. Secondly, in virtual excavations cultural participants do not vanish as in the case of traditional archaeological inquiry. Indeed, virtual excavations for BI should be targeted to locating, identifying and following the activities of current participants, as these constitute a live source of useful information. Finally, virtual excavations are more aligned to facilitating an emergent knowledge process [START_REF] Markus | A Design Theory for Systems That Support Emergent Knowledge Processes[END_REF] rather than an improved understanding of the past. The above has consequences for the theoretical framing of virtual excavations as well as the methods to be used. In what follows, we briefly describe how these implications are dealt with the present work.

Research focus

In our analysis so far, we have argued that SM retain data which can bring to the surface 'hidden' knowledge that can be of potential value to an enterprise, and that this knowledge, once revealed, can have implications for a firms' business strategy. Nevertheless, the means through which this data is managed (i.e., extracted, filtered and transformed), remains a challenge. This is particularly the case when the data resides in multiple SM. We believe that an approach based on the concept of virtual excavations provides one solution to this problem. The key issues that must be addressed in devising such an approach are:

1. What is relevant digital trace data and what artefacts leave the required traces? 2. Where do they reside and what boundaries prohibit their access? 3. How are they retrieved and what sense can be made of them?

Although there is work that attempts to address these issues, the results have been suboptimal as they fail to systematize BI using SM and do not provide a scalable and customizable technique. This is primarily due to constraints stemming from the perspective adopted and their underlying philosophy. Thus, for example, information systems researchers frequently emphasize the technicalities of SM and devise methods (i.e., algorithms, crawling strategies, screening methods, etc.) which are only applicable to certain platforms; consequently, cross-media comparisons cannot be made. Similarly, management scientists and organizational researchers tend to neglect the technicalities of different SM platforms, concentrate on aggregate artefacts (e.g. blogs) and extract embedded intelligence using text processing methods [START_REF] He | Social media competitive analysis and text mining: A case study in the pizza industry[END_REF] or document visualization techniques [START_REF] Abbasi | CyberGate: A Design Framework and System for Text Analysis of Computer-Mediated Communication[END_REF]. This level of analysis is useful, but is disassociated from the meaning of actions, such as pressing a 'like' button, which demonstrate a different form of interaction between users.

The above are some of the factors that have driven our desire to develop a more systematic and organized treatment of SM that can be used for BI purposes. To this end, the present work constitutes part of an on-going attempt to advance BI through virtual excavations of SM. In this vein, our baseline is that by extracting and processing digital trace data, we can foster new BI capabilities, just as archaeologists create new understandings of the past by excavating physical remains.

Methodology

To test the concept of virtual excavations as a method for extracting BI, we undertook a case study of three prepaid (pay-as-you-go) mobile telecommunications service brands that operate in Greece using their fan pages on Facebook. For anonymity, we will refer to the Management,27(1),2013,. DOI: 10.1108/JEIM-09-2012-0061 three brands as 'A', 'B' and 'C'. According to (Socialbakers.com, 2012) the telecommunication sector is the most socially devoted sector with a 60.4% response rate for their Facebook fan pages. The same source also acknowledges that the three Facebook fan pages of this study are ranked in the 6th, 55th and 56th position among Greek Facebook fan pages. Table 2 depicts data regarding the fan base and the activity of the Facebook pages. The figures displayed were retrieved from Facebook on August 20, 2012. It should be noted that the three telecommunication companies offer not only prepaid plans but also regular plans; our focus is only on the Facebook pages for the prepaid plans.

[Insert table 2 near here] Table 2: Facebook fan pages Prior to launching the virtual excavation, a detailed analysis of the virtual settlement was undertaken to identify relevant artefacts and the associated actions that create the digital traces that are retained and made available through the platform's public API. This analysis confirmed Facebook's provisions for creating and sharing user-generated contents in specific forms and under certain conditions. For our purposes, Facebook fan pages constitute the top-level digital artefact through which users engage in interactions with the company and with other users. Such pages frequently act as identity mechanisms, inviting users to externalize their opinions or state of mind.

The 'glue' bringing together different people is the page's wall. This is as much an online meeting point as a newsfeed aggregator, acting as the host of a wealth of useful information. In the case of the three brands in our study, such information may untangle the company's cultural values as revealed by the presence or absence of an eco-friendly profile, its commitment to sustainable development, policy declarations, etc. This information can be found in digital objects such as photos, videos or textual announcements that the firm uploads on its fan page wall and reach their fans through each fan's news feed. First-time visitors can also declare positive feedback to a page by hitting a button that is labelled with a thumps-up icon and the word 'like'; this will automatically make the user a fan of the web page with all contents published by the page being displayed in their news feed.

The functionality of the 'like' button can also be found in other artefacts of Facebook such as comments, photos, videos, etc., thus making it possible for users to externalize intentions by sharing posts, declaring 'likes' and commenting in the page's Facebook wall in a manner that is visible to other users. Such micro-interactions can lead to aggregate formations, such as friends lists, that are useful cultural bindings and reveal the users' social circles. In light of the above, it is argued that digital remains on Facebook designate on the one hand the brand's identity, intentions and cultural values, and on the other hand, the fans' opinions, concerns and pre-occupations. Such digital remains are manifested as traces of the users' actions with artefacts such as fan pages, wall posts, friend lists, posts, comments, etc.

Table 3 summarizes what were deemed to be relevant artefacts and what sorts of digital traces can be compiled. Depending on purpose and scope of BI inquiry, the type and range of artefacts may be extended or narrowed, which will impact on the digital trace data sample.

[Insert table 3 near here]

Data samples and instruments

The data on which the present research is grounded was extracted on August 24, 2012 and covers a period of three and a half years including interactions dated from the 5th February 2009 to 23rd August 2012. Table 4 presents the identity of the data sample. It is worth noting that several of the traces suggested by our analysis (see Due to a specificity of the Greek internet where users frequently communicate though 'Greeklish'a transliteration of Greek using the Latin alphabet -we used a 'Greeklish' to Greek text transformation engine [START_REF] Chalamandaris | All Greek to me! An automatic Greeklish to Greek transliteration system[END_REF] to properly visualize our data set through specific tools and count word frequencies. No further screening or filtering of the data was performed. In terms of qualifying users as fans of a page, it was assumed that Facebook users declaring a positive feedback on a page using the 'like' button are fans of the page. We were not able to extract who these users were using the Facebook API, we could only access the number of total users for each page as presented in Table 2. All Facebook fan pages considered in this study were open, which means that every Facebook user is allowed to create or 'like' content (e.g. wall posts, comments) that appears on the pages' walls. As we were not able to access information about whether or not active fans (i.e. fans where that performed activities in a page's wall) were actual fans of the pages (i.e. to have hit the page's 'like' button), for reasons of simplicity we decided to consider active fans as actual fans of the pages.

The data extracted was stored in a relational database representing details such as type of trace, owner, time of creation etc. This allowed us to calculate metrics such as operator response rates and times to active fans' questions. Nevertheless, our primary interest has been to examine more complex cyber-phenomena that qualify the parties engaged, the types of artefacts chosen to convey contributions and cross-page activities of contributors. In terms of analytical instruments, our work relies on the use of a set of visualizations crafted specifically to support the cyber-phenomena under investigation. As discussed in the following section, a variety of visualizations were used to derive useful insights on aggregate patterns of activity as well as factors determining the relative success of different brands during certain periods.

Findings

This section discusses the most prominent findings leading to ascertaining the added value of our approach. In doing so, we do not aim for an exhaustive treatment of all possible BI related issues due to space limitations. Thus, the focus is limited on a relevant subset of key findings by following a chronological account of the activities undertaken, starting with the role of the fan page operators and moving to an analysis of interaction patterns revealed.

Contributors, type of contributions, response rates and times

Our data sample represents online activities undertaken by three types of contributors, namely page operators (n=3); active fans (n=42,914) and casual contributors (n=16,485). The operators are the users acting as representatives of the three brands and as the administrators of each page. Active fans are Facebook users other than page operators who contribute (in whatever way we could capture using the API) to at least one of the three pages. Contributors are Facebook users who create content through wall posts or comments.

Attempting to understand the attention paid by brands to their social networking fans, it was deemed appropriate to examine the operators' response patterns in the fans' wall posts and the associated time differences in response time. Such measurements are typical BI metrics widely acknowledged by SM monitoring tools (Socialbakers.com, 2012). Nevertheless, these metrics are not enough to establish informative indications of the quality and the rate of the responses nor how much attention is paid by the operators to users' questions or complaints. Deeper and more involved analysis of the choice of artefacts used as well as their design affordances can provide further useful insights. In terms of artefacts used, there was an overwhelming preference for comments, replies to comments and 'like' buttons for expressing positive opinion. Moreover, comments may be intertwined in structured sequences of 'comment-reply' depicting comment replies to the operator's response. As an illustration of the above, Figure 1 depicts two instances of users' wall posts with comment replies created by the fan page operators. In one case (wall post A) the operator's comment has been 'liked' by the creator of the wall post while in the other case (wall post B) there is a comment reply with the phrase 'Thank you very much' in Greek.

[Insert figure 1 near here] Tracing and analysing these exchanges (i.e., counting comment replies) and taking account of the owner (i.e. who replies) as well as their contents (i.e. whether they contain expressions such as 'thank you' either in Greek (i.e. /ευχαριστώ/) or in English) can be useful. From a total of 10,503 Facebook wall posts traced, (A=7,619; B=1,572; C=1,312), 936 (8.91%) were posted by the operator of the fan page, while 9,567 (91.09%) were posted by fans. In an effort to qualify the posts representing questions, content analysis was undertaken. A post was classified as a question if it contained at least one occurrence of a question mark (i.e. the symbols ';' in Greek and '?' in English) in the body of the post. Special markers such as smiles that combine the characters ';' and ')' were found in 73 wall posts (e.g., approximately 0.70% of total wall posts) and those wall posts were not qualified as questions. From the total of fan wall posts, 5,641 (58.96%) had question marks in their content. These wall posts were made by 3,592 distinct users (A=2,870; B=474; C=347). Operators responded to 4,466 (46.68%) of total fan wall posts. For wall posts qualified as questions, operators responded in 3,405 (60.36%) of them. The number of wall post creators in each brand does not sum up to the number of distinct users reported, as users were found to cross post in more than one fan page. The average operator response time (i.e. time passed from the creation of the wall post to the first operator response) to a fan question was 924 minutes (A=909; B=934; C=998). More specifically, there were 1,288 (37.83%) fan questions that had an operator response in less than an hour; while in 249 (7.31%) questions the responses were made in less than five minutes. In 553 (16.24%) fan questions, the operator response time was more than a day. Table 6 summarizes the response times.

[Insert table 6 near here] Fan questions had in total 14,172 comments, with 4,473 (31.59%) made by operators, while 9,699 (68.44%) by the fans. Out of the total fan comments, 5,016 were left by the creators of the question. Those comments were made on 2,794 different questions. More specifically, fans left 3,759 comments on 2,090 of their own questions with a response by the operator. Gathering these 3,759 comments, extracting their contents and visualizing them can reveal popular words, with either positive or negative connotation. We have used word cloud visualizations (see Figure 2) with the following colour coding scheme. Green designates the words 'thank', 'ευχαριστώ' (i.e. 'thank you') and 'ωραία' (i.e. 'nice') allowing for spelling errors and related abbreviation such as 'thnk', 'thx', etc. Red is used for the words 'πρόβλημα', 'ντροπή', 'κακό' that stand for 'problem', 'shame' and 'bad' in English, respectively. Needless to say that there could be any other combination of words that justifies a certain cause. Moreover, the results of measuring frequencies of word occurrence for a selection of words implying negative or positive meaning are summarized in Table 7.

[Insert figure 2 near here] From the 3,759 comments posted by fans on their own questions and responded by the operator, we counted how many of them had a gratitude expression. A total of 1,118 comments were found to contain a gratitude expression made by the question creator. These comments were made in 1,031 fan questions that represent the 49.33% of the questions that had a response by an operator. In 945 (91.66%) questions, gratitude comments were made after the first operator response while in 86 (8.34%) questions the gratitude comments made before the first operator response.

Focusing on 'likes', a total of 525 fan questions with a response by the operator (i.e., 15.42% of total questions with operator responses) were 'liked' by the creator of each question (see Table 8).

[Insert table 8 near here] In light of the above, we can argue that the telecommunication companies considered in this study are evidently paying attention to their social networking fans. This is evident not only from the response rates, which amount to almost 50% of the questions posted by the fans, but also by the timing of the operator's response which in some cases was immediate (i.e. a few seconds) resulting in an average response time of 15.4 hours. Brand 'A' was found to score the lowest response rate (56.86%) in contrast to the highest response rate (73.32%) of brand 'C'. It turns out that brand 'A' is the most promptly responded with a median response time of approximately 3.3 hours and a minimum response time of 16 seconds. On the other hand, the less promptly responded brand appears to be brand 'C' with a median response time of 10.7 hours and 19.04% of responses made after a day. The above metrics show the type of results that can be extracted by the traditional SM monitoring tools.

What it is not clear from these results so far is to what extent fans were satisfied with the responses received by the operators of the fan pages. To this end, it should be noted that although Facebook was not built to support transactions of the kind that modern customer relationship management (CRM) systems are built to provide, yet it is used as a CRM system [START_REF] Prestus | Facebook as Agile CRM? A Business Intelligence Analysis of the Airline Ash Crisis[END_REF]. Sentiment analysis [START_REF] Pang | Opinion mining and sentiment analysis[END_REF] of textual content, as useful as it may be in some cases, can lead to obscure findings. We do not claim that a full-scale sentiment analysis was performed in this study. Nevertheless, it is evident that the word 'thank' either in Greek or English was found to have more appearances on brand 'A' with 4.92% of total words that were identified on the comments posted by the creator of the question (on questions that had a response by an operator). This equates to approximately one occurrence every 20 words while brand 'B' was found to have one occurrence every 23 words and brand 'C' to have one occurrence every 33 words. By focusing on the gratitude expressions and the ratios that are being identified, it is revealed that brand 'A' has the lower percentage (47.93%) of questions with a gratitude expression when the operator responded; while brand 'B' scores almost 56%.

Additional information of consumer satisfaction may be found in non-textual information (chronological data) or the users' choice of interaction elements such as 'like' buttons. By counting how many operator responses to fan questions had been 'liked' by the creator of the question, Brand 'B' receives 'likes' in 20% of their responses where brand 'A' is limited to 14.10% despite the fact that it is the brand that responds most promptly. Further investigation on the questions with gratitude expressions showed that for brand 'B', in 97.55% of questions the comment with the gratitude expression was submitted after the last operator response. In contrast, brand 'A' had almost 10% of those questions having a comment with a gratitude expression submitted before the operator had submitted a response. From the above, it can be argued that what is 'hidden' in 'banal' manipulative actions (i.e., 'like' button presses) or other micro-interactions between the users can be used to reveal further information of value for the enterprise such as the level of consumer satisfaction, and better understand the engagement of users in such transactions.

Active fans and contributors

Active fans are users having at least one activity of any kind and contributors are users posting at least one wall post or comment. Our analysis was aimed at tracking such fans with activities in more than one page, which should be more likely to yield useful comparisons between brands, and detecting events that created impact for fans and active contributors.

In order to identify users that are active in more than one Facebook page, we rely on visualizations that blend users' activity in multiple Facebook pages to reveal cross page traffic. Figure 3 depicts such a visualization using our data set. In this graph, users are represented with green nodes and Facebook pages with red nodes. User nodes are connected to the page nodes in the case of at least one activity performed by the user in that page. The size of the user nodes can be adjusted to meet the specific properties of the user (e.g. the users presence in different groups, the number of the activities they performed for the connected pages). When the mouse is over a node, the edges connecting that node to other nodes are highlighted (in red) and a number appears at the middle of the edge, indicating the number of distinct actions of the user within the connected group. A force directed layout [START_REF] Barnes | A hierarchical 0 (N log iV) force-calculation algorithm[END_REF] has been applied in order to position users according to pages where they are active.

[Insert figure 3 near here] In light of the above, Figure 3 in the outer circle reveals three concentrations of users around the page nodes of the three brands. Each of these clouds indicates activity on a single page.

In the inner parts of the graph, there are activity aggregations representing contributors to brand 'A' and 'B', brand 'A' and 'C' and brand 'B' and 'C' respectively. Finally, one group of nodes at the centre of the graph indicates users that have activities in all three Facebook pages.

Further insights are obtained by studying the users' contribution in time. For this purpose, the data set was reconstructed as shown in Figure 4. Only 38.41% of total active fans fall in the category of contributors, indicating that 61.59% of total users contribute only 'likes'. These users, although they may be considered as the audience of the Facebook fan page and their collective activity may play an important role, are characterized by a relatively low degree of engagement with the brands. The same applies to the 56.15% of active fans that have only created one activity. As further study of these casual contributors was not deemed necessary, subsequent analysis focused on the activities of the most active contributors (i.e. those having 10 or more activities).

Figure 5 presents the contributors with ten or more activities of any type. In this graph, the size of the node depicts the total number of wall posts of the user. A closer look at Figure 5 reveals that for brand 'B' and brand 'C', more than half of contributors with ten or more activities are cross posting while the greatest part of contributors that have activity in brand 'A', are not cross posting. In order to gain insights into customer opinions and preferences between brands, we further narrowed most active contributors to those cross posting in two or more pages. These users are more likely to make comparisons between brands and their activity will allow us to spot events that had more impact for them.

[Insert figure 5 near here] Figure 6 presents timelines of the contributions by users with ten or more activities of any type in two or more pages. Careful inspection of this graph reveals areas of particular interest. Four of them have been annotated for the purposes of this discussion. Areas A and C reveal segments of the timeline activity of two users, highlighting points in time that users appear to switch their activities from one brand's wall to another. Figure 7 and Figure 8 untangle user activities in Area A and Area C in terms of type and content while Table 11 and Table 12 provide a translation in English of the content and further details about these activities. Apparently, these two users had substantial activity in a short period in two different walls. Looking closer at the first fan's activity (see Figure 7), it becomes apparent that at that time the user was actively engaged with brand 'A' and brand 'C'. It is worth noticing that the activity presented there, was performed in a period of three days. The fan created 'likes', wall posts and comments. The fan's posts on the wall of brand 'A' reveal a frustration about the signal coverage. This is depicted not only through wall posts (A-3, A-5) and comments (A-1) but also through the user's 'likes' of comments that express the same frustration (A-2). On the wall of brand 'C' the fan is reproducing advertisements (C-3) of brand 'C' and gives information to other users (C2). The second fan was rather frustrated about the lack of competitiveness among brands. The fan was found to be seeking information about moving from brand 'C' to brand 'A' but was informed by other users that brand 'A' is confronted with similar issues.

[Insert figure 6 near here] [Insert table 11 near here] Finally, examining events that created impact on Facebook for these three brands we focused on two areas of the timeline visualization of Figure 6. Area B depicts activities that were produced during a three-day contest for brand 'A'. Users were asked by the operator to create 'likes' or comments in three wall postsone for each dayin order to take part in the contest. To a similar extent, the activity of area D was created when the operator of brand 'B' posted one wall post asking the users to mention their friends (i.e. tag) in a comment in order to take part to the contest. Brand 'A' in its contest did not use the tagging functionality, as tagging was not available by Facebook at that time. From the analysis of those wall posts, it became evident that brands take advantage of the currently available functionalities (i.e. affordances of artefacts) offered by the virtual environment to create a buzz around their brand.

Discussion

In this section we discuss our findings, mainly at the level of the method, and focus on the value of an approach based on virtual excavations for BI. In particular, we focus on the three key issues identified in the paper's research focus section, namely them. Following this, we highlight some of the broader implications for our work, as well as its limitations and the directions it might take in the future.

Virtual excavations and BI

Virtual excavations, as presented in this article, should not be understood as merely web analytics. Their capabilities extend far beyond tracing the user's online activities and revealing the user's browsing and purchasing patterns. The full benefit of virtual excavations are realized when they are structured and organized around aggregate artefacts embedded in a technology. Virtual excavations constitute an approach for understanding online practice through its digital remains. This is achieved through the ability to interrelate different digital traces, which otherwise might appear as banal interactions, to create a more accurate picture of the context in which these interactions took place. For example, as we saw in the findings section, the sequence in which operator responses (e.g. likes and comments) are performed or frequent and recurrent posts by the same user of certain types of messages (e.g. including the prefix 'http://' or gratitude expressions), can be useful in making sense of the user's original intentions. In this sense, virtual excavations can provide not only retrospective accounts of historical data but also act as predictive analytics, simulating and assessing potential future outcomes grounded in the parameters of reliable historical trends.

For example, our case study provided valuable insights into the effects of certain service strategies associated with the three brands. It revealed that brands that are quick to diversify through unlimited plans experienced high end user concentration, which then led them to initiate strategies to protect market share and customer base. Similarly, it would be possible to create projections of how external factors, such a legislation, marketing campaigns or television programmes, could influence consumers in their search for competitive services. We believe that virtual excavations for BI should be seen as part of an emergent knowledge process [START_REF] Markus | A Design Theory for Systems That Support Emergent Knowledge Processes[END_REF] that results from an unpredictable set of actors, unclear requirements and deliberations that have no pre-determined structure or sequence. This situation is precisely why methods are needed to guide and organize what is to be traced and where and how sense is to be made of it.

Implications

We will now briefly examine some of the implications of our work. Firstly, a pre-requisite for virtual excavations is the capability to locate and trace the appropriate set of digital remains. These need not be limited to the type offered by web analytics tools. Our experience shows that there is added value to be gained by a targeted consideration of the affordances of artefacts embedded in technology, the manipulative actions they invite, and the traces these actions leave behind. Such a focus, amongst other things, allows us to ascribe meaning to otherwise banal, keystroke level activities and guides our analysis and exploration of the data set. In our work, we have attempted this for Facebook (see Table 3), which has led not only to envisioning appropriate visualizations but also to highlighting shortcomings in the API that restricts traces of certain types. Thus, it is argued that approaches capitalizing on virtual excavations provide a structured method to untangle the hidden knowledge embedded in massive data sets originated from SM, which is achievable in a timely and purposeful manner and is flexible enough to account for the variety of hosting services and data formats i.e., textual, tabular, images, videos, channels, 'likes', 'tweets', etc.

This brings to the forefront the second implication of the present work, which relates to identifying where the relevant artefacts reside and what might prohibit their access. In an increasingly interconnected social web, relevant artefacts are spread across different virtual settlements, with variable capabilities and public APIs. At present crossing boundaries of virtual settlements is not straightforward. Although there are provisions such as sharing widgets, they are not sufficient to bring to the surface the 'hidden' knowledge that characterizes user communities. At the same time, recent improvements in semantic APIs only partially capture online traffic. The complications arising from this type of constraint were prominent in the analysis of the case presented in this article (see section on limitations and future work below).

Finally, it should be noted that the visualizations we used in the work described in this article provide only one approach to the way we might make sense of digital remains. They were chosen because visualizations constitute a powerful transformational technology. Nevertheless, they require a substantial investment to create and use since the tools that are available need adjustment and, in some cases, substantial refinements, extensions and enhancements before that can be used. Additionally, in order to deal with the sheer volume of data, computational efficiency is also an important factor. Consequently, if visualizations are to be used, it is important for researchers to be well informed about the technological implications of what they wish to do and to make good use of appropriate visualization toolkits.

Limitations and future work

Our research has highlighted a number of limitations with this approach; with some of them being pursued as part of our on-going activities. Firstly, our current work addresses a single platform, namely Facebook. However, our attempts to generate useful BI would benefit from investigations covering multiple virtual settlements and platforms. Specifically, in our reference case, the three brands maintain presence in video-sharing platforms such as YouTube to share TV adverts and other information. YouTube provides an open API and it would be of value to apply the method described in this article to produce complementary data relating to designing contests, inviting suggestions on adverts and so on. However, our initial intention to undertake such a cross settlement excavation, taking into account the presence of the three brands in both Facebook and YouTube, was plagued by problems associated with lack of interoperability, structural mismatches between the two platforms and an inability to form complete mappings between aggregate artefacts and their affordances.

Secondly, our experiences indicate that there are cases where it may not be possible to extract the full range of digital trace data that is needed. This can be due to either the restricted design affordances of certain artefacts (e.g. a user can be tagged in a comment or a photo) or the limitations of the APIs. For example, in our case study, it was not possible to access the time 'like' button traces are created. This is further complicated in crosssettlement analyses where the features of the various APIs that are available may pose challenges.

Finally, regarding the techniques used to make sense of digital traces, the present work relies primarily on visualizations. These however could be complemented by, for example, social network analysis, observation or interviewing strategies. Social network analysis might bring insights into patterns of relationships and interactions amongst users and lead to discovering the underlying social structure in user networks while observation or interviewing might provide contextual insights into user's behaviour, both of which would complement an analysis based on techniques of visualization.

Concluding remarks

The work we discuss in this article examines several scenarios to illustrate the notion of digital traces, how they are retained and the way in which they may obtain added value for enterprises. It also hints at the methodological challenges that need to be addressed when designing and conducting virtual excavations.

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  . Milolidakis, D. Akoumianakis and C. Kimble. Digital Traces for Business Intelligence: A Case Study of Mobile Telecoms Service Brands in Greece. Journal of Enterprise Information Management, 27(1), 2013, pp. 66 -98. DOI: 10.1108/JEIM-09-2012-0061

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  . Milolidakis, D. Akoumianakis and C. Kimble. Digital Traces for Business Intelligence: A Case Study of Mobile Telecoms Service Brands in Greece. Journal of Enterprise Information Management, 27(1), 2013, pp. 66 -98. DOI: 10.1108/JEIM-09-2012-0061

G

  . Milolidakis, D. Akoumianakis and C. Kimble. Digital Traces for Business Intelligence: A Case Study of Mobile Telecoms Service Brands in Greece. Journal of Enterprise Information

Figure 1 :

 1 Figure 1: Operators responses on users' wall posts

G

  . Milolidakis, D. Akoumianakis and C. Kimble. Digital Traces for Business Intelligence: A Case Study of Mobile Telecoms Service Brands in Greece. Journal of Enterprise Information Management, 27(1), 2013, pp. 66 -98. DOI: 10.1108/JEIM-09-2012-0061

Figure 2 :

 2 Figure 2: Word cloud

G

  . Milolidakis, D. Akoumianakis and C. Kimble. Digital Traces for Business Intelligence: A Case Study of Mobile Telecoms Service Brands in Greece. Journal of Enterprise Information Management, 27(1), 2013, pp. 66 -98. DOI: 10.1108/JEIM-09-2012-0061

Figure 3 :

 3 Figure 3: Active users on Facebook pages (Top graph represents the entire data set while the bottom a zoom area)

Figure 5 :

 5 Figure 5: Contributors that had ten or more activities of any type

Figure 6 :

 6 Figure 6: Timeline of contributors that had ten or more activities of any type on two or more pages

Figure 7 : 6 [

 76 Figure 7: Zoom on area A of Figure 6

Figure 8 :

 8 Figure 8: Zoom on area C of Figure 6

  (a) what is relevant digital trace data and what artefacts leave the required traces; (b) where do they reside and what boundaries prohibit their access; (c) how are they retrieved and what sense is to be made of G. Milolidakis, D. Akoumianakis and C. Kimble. Digital Traces for Business Intelligence: A Case Study of Mobile Telecoms Service Brands in Greece. Journal of Enterprise Information Management, 27(1), 2013, pp. 66 -98. DOI: 10.1108/JEIM-09-2012-0061

Figure 1 :

 1 Figure 1: Operators responses on users' wall posts

Figure 2 :

 2 Figure 2: Word cloud

Figure 3 :

 3 Figure 3: Active users on Facebook pages (Top graph represents the entire data set while the bottom a zoom area)

Figure 4 :

 4 Figure 4: Timeline of users' activities

Figure 5 :

 5 Figure 5: Contributors that had ten or more activities of any type

Figure 6 :

 6 Figure 6: Timeline of contributors that had ten or more activities of any type on two or more pages

Figure 7 : 6 G

 76 Figure 7: Zoom on area A of Figure 6

Figure 8 :

 8 Figure 8: Zoom on area C of Figure 6

Table 1 : Recent works utilizing SM data

 1

  . An example of this type of BI infrastructure is Socialcast.coma G. Milolidakis, D. Akoumianakis and C. Kimble. Digital Traces for Business Intelligence: A Case Study of Mobile Telecoms Service Brands in Greece. Journal of Enterprise Information Management, 27(1), 2013, pp. 66 -98. DOI: 10.1108/JEIM-09-2012-0061

  . Consequently, the significance and meaning of the material objects that humans create can G. Milolidakis, D. Akoumianakis and C. Kimble. Digital Traces for Business Intelligence: A Case Study of Mobile Telecoms Service Brands in Greece. Journal of Enterprise Information Management, 27(1), 2013, pp. 66 -98. DOI: 10.1108/JEIM-09-2012-0061

Table 3 : Cultural Artefacts of Facebook

 3

Table 3 )

 3 could not be captured due to limitations of the platform's API or due to privacy issues. Nevertheless, our data sample is considered fairly complete and adequate for the intended purposes.

G.

Milolidakis

, D. Akoumianakis and C. Kimble. Digital Traces for Business Intelligence: A Case Study of Mobile Telecoms Service Brands in Greece. Journal of Enterprise Information Management, 27(1), 2013, pp. 66 -98. DOI: 10.1108/JEIM-09-2012-0061 either [Insert table 4 near here]

Table 4 : Excavation identity

 4

  Table 5 provides further details and summarizes our findings by brand.

[Insert table 5 near here]

Table 5 : Response rates by operators

 5

Table 6 : Response times

 6

Table 7 : Word frequencies

 7

Table 8 : Fan questions, comments and 'likes'

 8

  When clicking on a mark, a pop up window summarizes additional information about that activity. Traversing the graph from left to right provides a chronological history of users' activity with the most recent placed to the right of the timeline. It is worth mentioning that for 'likes', Facebook API doesn't allow access to creation time. In order to overcome this problem, we have considered as creation time of the 'like', the creation time of the corresponding artefact (e.g. comment, wall posts) on which the 'like' was performed.

	[Insert figure 4 near here]
	Figure 4: Timeline of users' activities
	A total of 42,914 active fans had 134,090 interactions on the walls of the three brands. In this
	sample, the operators' activities have been excluded. Of the total activity captured, 59.41%
	was on the page of brand 'A', 21.64% was on the page of brand 'B' and 18.95% was on the
	page of brand 'C'. 56.15% (24,095) of active fans made just one activity while 4.95% (2,123)
	made ten or more activities. 38.41% (16,485) of total active fans contributing with at least
	one wall post or a comment. These users (i.e. contributors) made 66.61% (89,320) of total
	activities (excluding operators' activity). That is 35,545 comments; 9,567 wall posts; 41,010
	likes and 3,198 tags on photos or wall posts. 26.30% (4,335) of total contributors made just
	one activity while 10.96% (1,806) made ten or more activities. Table 9 summarizes these
	data for each brand.
	The graph depicts timelines of user
	activities (wall posts, comments, 'likes') ordered by time of creation. Each line of the graph
	(differentiated by white and grey stripes) represents one user. The coloured marks in each
	user's line indicate an interaction with a Facebook page's wall. Green marks indicate activity
	on the wall of brand 'A'; red marks indicate activity on the wall of brand 'B'; while blue marks

are activities on the wall of brand 'C'. Within the timeline of a user we can find two different shapes of marks. The round marks indicate activity of type 'like' while the square marks depict wall posts or comments. Round marks placed at the top of the user's timeline represent 'likes' on wall posts while those at the bottom depict 'likes' on comments. Similarly, square marks placed topmost indicate wall posts while the square marks at the bottom G. Milolidakis, D. Akoumianakis and C. Kimble. Digital Traces for Business Intelligence: A Case Study of Mobile Telecoms Service Brands in Greece. Journal of Enterprise Information Management, 27(1), 2013, pp. 66 -98. DOI: 10.1108/JEIM-09-2012-0061 indicate comments. [Insert table 9 near here]

Table 9 : Active fans and contributors

 9

	If we only focus on contributors, then 89.49% (14,752) of the total contributors had activities
	related to just one page; 9.11% (1,502) had activities in two pages and 1.40% (231) had
	activities on three pages.

Regarding cross posting in multiple pages (see Table

10

), 94.46% (40,535) of the total number of active fans had activities on only one Facebook wall; 4.94% (2,120) fans had activities on two different walls and only 0.60% (258) of fans had activities on all three walls.

[Insert table 10 near here]

Table 10 : Cross-posting fans

 10

Table 11 : Translated content from area A

 11

[Insert table 12 near here]

Table 12 : Translated content from area C

 12

Table 1 : Recent works utilizing social media data

 1 These relate to defining the virtual settlement and its boundaries, what traceable remains are useful and how they are revealed as well as what techniques may be employed to add value to bytes of code. Our on-going work concentrates on extending virtual excavations and the associated techniques needed to G. Milolidakis, D. Akoumianakis and C. Kimble. Digital Traces for Business Intelligence: A Case Study of Mobile Telecoms Service Brands in Greece. Journal of Enterprise Information Management, 27(1), 2013, pp. 66 -98. DOI: 10.1108/JEIM-09-2012-0061 facilitate insights into boundary spanning in virtual settings. Specific challenges in this direction include the development of mechanisms for locating digital remains across virtual settlements and aggregating them in a manner that will lead to establishing richer insights on a community's presence and practice across virtual settings and boundaries. Amongst other things, this is expected to test affordances that enable or constrain the interoperability of virtual settlements and the mechanics through which it is attained. Milolidakis, D. Akoumianakis and C. Kimble. Digital Traces for Business Intelligence: A Case Study of Mobile Telecoms Service Brands in Greece. Journal of Enterprise Information Management, 27(1), 2013, pp. 66 -98. DOI: 10.1108/JEIM-09-2012-0061

	Titles and scholarship	Social Media	Application
	"Inferring		preference	-Social network service	Identifying consumer preferences to
	correlations from social	(Essembly.com)	help sellers design customized
	networks", (Hogg 2010)		bundles of products
	"Discovering target groups	-Users opinions website	Analysing user opinions to discover
	in social networking sites:	supporting social networking	the user groups with max joint
	An effective method for	functions (Epinions.com)	influential power for marketing and
	maximizing joint influential	-Micro-blogging platform	enterprise reputation management
	power", (K. Xu et al. 2012)	(Twitter.com)
	"LCI: a social channel	-Micro-blogging platform	Conducting real-time and historical
	analysis platform for live	(Twitter.com)	sentiment analysis for inferring
	customer	intelligence",		human emotions to get instant
	(Castellanos et al. 2011)		customer insight on events like a
					new marketing or sales campaign
	"Facebook as Agile CRM?	-Social network service	Utilizing social media to manage
	A Business Intelligence	(Facebook.com)	customer relationships effectively
	Analysis of the Airline Ash		(as an agile CRM tool) and to
	Crisis", (Prestus & Bygstad		supplement		customer
	2010)				communication
	"Predicting Stock Market	-Micro-blogging platform	Predicting stock market indicators
	Indicators Through Twitter	(Twitter.com)	by measuring collective hope and
	'I hope it is not as bad as I		fear
	fear'", (X. Zhang et al.
	2011)
	"Twitter mood predicts the	-Micro-blogging platform	Predicting stock market indicators
	stock market", (Bollen et al.	(Twitter.com)	through mood tracker tools
	2011)
	"Opinion mining in social	-Online discussion platform	Exploring ways where online content
	media:		Modeling,	-Blogging platforms	from social media can be exploited
	simulating, and forecasting	(Technorati.com)	to inform decision makers about
	political opinions in the		constituent	opinions,	emerging
	web", (Sobkowicz et al.		trends, and on the feasibility and
	2012)				potential impacts of policy initiatives
	"Vehicle defect discovery	-Online discussion platform	Facilitate decision support for the
	from	social	media",	(honda-tech.com)	vehicle	defect	discovery and
	(Abrahams et al. 2012)		classification process
	"Social media use by	-Social network service	Leverage social media data by
	government: From the	(Facebook.com)	government officials to improve
	routine to the critical",	-Micro-blogging platform	communication with citizens and
	(Kavanaugh et al. 2012)	(Twitter.com)	managing crisis situations
				-Video-sharing platform
				(Youtube.com)
	"Harnessing social media	-Travel related social network	Measuring brand equity using online
	platforms	to	measure	service (Tripadvisor.com)	customer reviews
	customer-based	hotel
	brand equity", (Callarisa et
	al. 2012)
	"Social media competitive	-Social network service	Create	actionable	competitive
	analysis and text mining: A	(Facebook.com)	intelligence using web mining for the
	case study in the pizza	-Micro-blogging platform	restaurant industry
	industry", (He et al. 2013)	(Twitter.com)

G. Milolidakis, D. Akoumianakis and C. Kimble. Digital Traces for Business Intelligence: A Case Study of Mobile Telecoms Service Brands in Greece. Journal of Enterprise Information Management, 27(1), 2013, pp. 66 -98. DOI: 10.1108/JEIM-09-2012-0061

Table 2 : Facebook fan pages

 2

	Brand Fan base Talking about this Position across Greek fan pages Joined Facebook
	A	268,369	2,166	6	04-Feb-09
	B	42,404	223	55	05-Jun-09
	C	42,057	302	56	04-Feb-11

G. Milolidakis, D. Akoumianakis and C. Kimble. Digital Traces for Business Intelligence: A Case Study of Mobile Telecoms Service Brands in Greece. Journal of Enterprise Information Management, 27(1), 2013, pp. 66 -98. DOI: 10.1108/JEIM-09-2012-0061

Table 3 : Cultural Artefacts of Facebook

 3

	Cultural	General Description			Commonly used	Is	manifested	Action	Affordances	Traces
	artefact of						for			through	possibilities for	produced
	practice										users	during its use
	Page	A Facebook page -as	Dissemination of	Filling a page	Becoming a fan;	Affords	member	Members list,
		advertised by Facebook -is a	ideas;	linking	profile	view	content;	list;	hosts	wall	posts,
		place	for	businesses,	people around a		create a wall	applications such	comments,
		organizations and brands to	point of interest		post (i.e. post	as	discussion	'likes'
		share stories, content (e.g.	(e.g. a brand)			updates); view	board, reviews and
		videos, photos) and connect					milestones	customized
		with people. Pages can be					applications
		further customized to support
		additional content by adding
		applications
	Wall	The wall is a place within the	Presenting	and	The page or the	View and create	Aggregates posts	Wall posts,
		Facebook page where users	publishing content;	user profile	wall post	comments,
		can	share	information.	public space of the		'likes'
		Information is shared through	page;	newsfeed
		wall posts. The wall displays all	aggregator
		related with the page user's
		activity. The wall is a feature
		existing also in user profiles
	Wall post	A post on the wall can host a	Information		The wall	View;	view	Allows discussion;	Wall posts,
		short message that is shared	container; media		creator;	view	can be "liked" or	comments,
		among the pages fans. A wall	container;			and	post	shared by a user	'likes', views
		post can be commented, "liked"	expressing			comments; view
		or shared. A post can be text	opinion; seeking		and post 'like';
		optionally	combined	with	information			share
		photos, videos and links or
		questions

G. Milolidakis, D. Akoumianakis and C. Kimble. Digital Traces for Business Intelligence: A Case Study of Mobile Telecoms Service Brands in Greece. Journal of Enterprise Information Management, 27(1), 2013, pp. 66 -98. DOI: 10.1108/JEIM-09-2012-0061

Table 4 : Excavation identity

 4

	Date of data excavation		24 August 2012
	Facebook users		42,917
	Facebook activities		145,819
	Oldest Facebook activity		5 February 2009 at 20:18 of type 'status update'
	Most recent Facebook activity	23 August 2012 at 19:33 of type 'comment'
	Type	of	Facebook	activities	 Posts on wall (10,503)
	captured though API (number of	o Status updates (9,238)
	activities counted)		o Photos (539)
					o Videos (353)
					o Links (361)
					o Questions (12)
					 Likes (88,030)
					o On wall posts (64,023)
					o On comments (24,007)
					 Comments (42,954)
					 Tags (4,332)
					o On comments (4,125)
					o On photos (207)

G. Milolidakis, D. Akoumianakis and C. Kimble. Digital Traces for Business Intelligence: A Case Study of Mobile Telecoms Service Brands in Greece. Journal of Enterprise Information Management, 27(1), 2013, pp. 66 -98. DOI: 10.1108/JEIM-09-2012-0061

Table 5 : Response rates by operators

 5

	Brand 'A'	Brand 'B'	Brand 'C'
	n	(%)	n	(%)	n	(%)

G. Milolidakis, D. Akoumianakis and C. Kimble. Digital Traces for Business Intelligence: A Case Study of Mobile Telecoms Service Brands in Greece. Journal of Enterprise Information Management, 27(1), 2013, pp. 66 -98. DOI: 10.1108/JEIM-09-2012-0061

Table 6 : Response times

 6

	Brand 'A'	Brand 'B'	Brand 'C'
	n	(%)	n	(%)	n	(%)

G. Milolidakis, D. Akoumianakis and C. Kimble. Digital Traces for Business Intelligence: A Case Study of Mobile Telecoms Service Brands in Greece. Journal of Enterprise Information Management, 27(1), 2013, pp. 66 -98. DOI: 10.1108/JEIM-09-2012-0061

Table 7 :

 7 Word frequencies

	Brand 'A'	Brand 'B'	Brand 'C'
	n	(%) n	(%) n	(%)

G. Milolidakis, D. Akoumianakis and C. Kimble. Digital Traces for Business Intelligence: A Case Study of Mobile Telecoms Service Brands in Greece. Journal of Enterprise Information Management, 27(1), 2013, pp. 66 -98. DOI: 10.1108/JEIM-09-2012-0061

Table 8 : Fan questions, comments and 'likes'

 8

	Brand 'A'	Brand 'B'	Brand 'C'
	n	(%)	n	(%)	n	(%)

G. Milolidakis, D. Akoumianakis and C. Kimble. Digital Traces for Business Intelligence: A Case Study of Mobile Telecoms Service Brands in Greece. Journal of Enterprise Information Management, 27(1), 2013, pp. 66 -98. DOI: 10.1108/JEIM-09-2012-0061

Table 9 : Active fans and contributors

 9

	Brand 'A'	Brand 'B'	Brand 'C'
	n	(%)	n	(%)	n	(%)

G. Milolidakis, D. Akoumianakis and C. Kimble. Digital Traces for Business Intelligence: A Case Study of Mobile Telecoms Service Brands in Greece. Journal of Enterprise Information Management, 27(1), 2013, pp. 66 -98. DOI: 10.1108/JEIM-09-2012-0061

Table 10 : Cross-posting fans

 10

		Active fans (% of	Contributors (% of	Contributors with >= 10
		total active fans)	total contributors)	activities	(%	of	total
				contributors)
	Having activities	40,535 (94.46)	14,752 (89.49)	231 (1.40)
	in just one page
	Having activities	2,120 (4.94)	1,502 (9.11)	433 (2.63)
	in two pages
	Having activities	258 (0.60)	231 (1.40)	151 (0.92)
	in three pages

G. Milolidakis, D. Akoumianakis and C. Kimble. Digital Traces for Business Intelligence: A Case Study of Mobile Telecoms Service Brands in Greece. Journal of Enterprise Information Management, 27(1), 2013, pp. 66 -98. DOI: 10.1108/JEIM-09-2012-0061

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