G Pozzi
email: gpozzi@liuc.it

F Pigni

C Vitari

EXPLAINING BIG DATA IMPACT ON HEALTHCARE ORGANIZATIONS: A TECHNOLOGY AFFORDANCE APPROACH

Keywords: Big Data, Affordance, Organizational Change, Healthcare

Big Data as pervasive, disruptive, and innovation-driven technology are now a hot topic in academic and business fields. In particular, the healthcare industry is one of those most exposed to the organizational changes produced by the Big Data and Born Digital Data phenomena. Healthcare organizations have to adapt their organizations to manage an ever increasing amount of data affecting their ability to deliver high quality services that must be both effective and efficient. We advance that the Technology Affordance theory can be used to explain how information technology is involved in organizational changes. The paper highlights the importance of an Affordance-Based Theory used to explain this particular issue.

Introduction

The amount of data produced by current computer based information systems is growing exponentially in any business industry. Organizations use various analytical techniques to study structured and unstructured forms of data. These data analytics can aid new products and processes discovery, new organizational forms and layouts, better productivity and new policies configurations (Tallon P.P., 2013). New opportunities for value creation have effectively emerged. Data can be seen as drivers of innovation when organizations are capable to exploit them to take decisions, improve processes and solve problems (Micheal K., Miller K.W., 2013). At the same time organizations face new challenges with respect to how much data to save and store, to data security and to customer privacy protection. For these reasons, Big Data are seen as a disruptive technology. That's particularly true in healthcare. Healthcare manages vast amount of digital contents: electronic health records, insurance records, pharmacy prescriptions, patient feedback and responses. These data are generated by numerous patient points of care, sophisticated medical instruments and web-based health communities. That's what means Big Data in healthcare: a single data matrix might have hundreds of thousands of patients with many records, heterogeneous in formats, and collected over a long period of time. Furthermore, these data have to be managed daily by hospitals and private clinics [START_REF] Chen | Business Intelligence and Analytics: From Big Data to Big Impact[END_REF]). Big Data are radically changing the entire health community creating discontinuities affecting healthcare organizations. Managing data is increasingly becoming a core competence in daily routine, requiring an appropriate organization and a technological structure to be present to guarantee right decision making and value creation. This study proposes to shed light on these challenges and issues through the affordances lens. Affordances are seen as actions potential for behaviors associated with achieving a specific goal and arising from the relation between information system and a goal oriented actor or organizations. We will clarify how the use and the adoption of an Affordances-Based Theory would benefit the resource looking into this particular issue. This paper is organized as follows. Section 2, 3 and 4 present an overview of the background literature on Big Data, Organizational Change and Affordances respectively. Section 5 presents the importance of an Affordances-Based Theory to study how the adoption of a particular technology affects and impacts individuals and organizations. Section 6 explains how Affordances can be linked to Big Data as a fresh look to analyze their impact on organizations. Section 7 concludes and presents the possible future works as well as the implications of what stated in the previous sections.

Big Data

The "Big Data" problem is not just about size. It also refers to the speed, the volume and the computational and analytical capacity required to manage data and derive insight [START_REF] Hemerly | Public Policy Considerations for Data-Driven Innovation[END_REF]. The term Big Data has increasingly been cited in scientific publications during the last 7 years, indicating the growing interest in the phenomenon from scientific and entrepreneurs' communities. The enormous amount of data produced every day reflects the magnitude and the impact of data-related problems to be solved in present-day business organizations such as database and information lifecycle management, storage, analytical capabilities (Chen et Unfortunately, current Big Data research is focused on analytics studies, and hardware and software management aspects. Dimensions of value creation and business opportunities are disregarded. Information systems literature too mainly explores the phenomenon through the logic of its adoption, and does not take into account the interplay between technology and organizational changes, and the fact that the Big Data usage profile and effective value are properties that arise from the interactions between social actors and the technology artefact.

Affordance-Based Theories are particularly suited to provide a proper understanding of the uses and the consequences of the IT artefact, considering this mutual interaction between social actors and the technological element itself (Majchrzak A., Markus M.L. 2012).

Big Data and HealthCare

According to Chen et al. (2012), the health community is facing a tsunami of health-and healthcare-related content generated from daily care activities and patients feedbacks. Extracting knowledge from health Big Data, identifying and developing new decision models, understanding the Big Data management impact on individuals and organization can generate meaningful impacts on healthcare quality and long term care and empowerment of patients and physicians.

Healthcare sector has vastly increased supply of information: pharmaceutical companies has been aggregating research and development data into medical databases, payors and providers have digitized patient records, governments and public stakeholders have opened clinical trials data and information on patient covered under public insurance programs (Kayyali B., Knott D. and Van Kuiken S. 2013).

In healthcare, where data are personal and sensitive, managing Big Data impacts also security and privacy manners that make the operations of sharing, aggregating, sorting and analysing particularly challenging [START_REF] Ahier | Big Data Is the Next Big Thing in Health IT[END_REF].

Recent literature (Ahier, 2012; Kayyali B., Knott D. and Van Kuiken S. 2013) shows several example of Big Data adoption in hospitals and clinics worldwide. Significant clinical knowledge, such as predicting medical treatments effectiveness, and deeper understanding of patient disease patterns can be gained from electronic healthcare records; data deriving from health social media, e.g. claims and comments, are able to lead to healthcare decision support and making, set the basis for patientcentered therapy and social, environmental and economical knowledge.

Although Big Data adoption in the healthcare system is already been studied from a medical and clinical point of view, references on how healthcare organizations have to be prepared on the adoption, and the consequences of it, in terms of organizational impact, change and readiness, and the management of Big Data are missing.

IT and Organizational Change

The widespread application of information technology is transforming the way in which the information is collected, exchanged, and stored, requiring a high level of coordination within units and organization.

The role that IT plays in organizations is radically changing over time. If IT was primarily used to automate existing operations and to increase the speed of communications, nowadays IT is mainly seen as an enabler for organizational changes that can lead to productivity gains [START_REF] Zammuto | Information Technology and the Changing Fabric of Organization[END_REF]. IT has taken over many coordination and control responsibilities from hierarchy, allowing people to organize around the work itself and what could be done with information. [START_REF] Leonardi | Activating the Informational Capabilities of Information Technology for Organizational Change[END_REF] study demonstrates how the information generated by state-of-the-art IT can lead the change in the social structure of an organization. By exploring the informal advice networks that constitute an organization's social structure, [START_REF] Leonardi | Activating the Informational Capabilities of Information Technology for Organizational Change[END_REF] understands how the informational capabilities of an IT implementation -the potential a technology has for creating, modifying, transmitting and storing information -can lead to organizational change. Studying the introduction of an information technology into an organization and recognizing that the adoption of a new technology is always followed by organizational accommodations and changes, [START_REF] Volkoff | Technological Embeddedness and Organizational Change[END_REF] propose a technology-mediated organizational change theory that contemplates both the focus on the technology and the influence of the social interaction. With the imbrication metaphor, [START_REF] Leonardi | When Flexible Routines Meet Flexible Technologies: Affordance, Constraint, and the Imbrication of Human and Material Agencies[END_REF] explains how the interaction with technology leads to organizational changes, either adjusting work routines or introducing new form of information system. As digital technology is becoming pervasive new organizational forms associated with the adoption of this technology have to be studied [START_REF] Yoo | Organizing for Innovation in the Digitized World[END_REF]).

Affordances

The concept of Affordances has a long history, starting from the first studies in ecological psychology to the adoption of the theme in the information system field. According to Greeno (1994), Gibson's concept of affordance (1986) can be explained as the interaction involving an agent with some other system. The conditions that enable that interaction include properties of the agent along with some properties of the other system. Affordance is a property of whatever the person interacts with and it has to be a property that interacts with a property of an agent in such a way that an activity can be supported. Affordances are preconditions for activity, but do not imply that that specific activity will occur [START_REF] Greeno | Gibson's Affordances[END_REF]. Gibson originally placed the concept of affordance in the ecological psychology field, where the theme was also developed with other contributions.

Considering the complex relationships between technologies and the social, and the interactional circumstances in which they exist and are meaningful, Hutchby (2001) argues that affordances are functional and relational aspects. purpose can do with a technology or information system". Their study is relevant because emphasizes the importance of a technology affordance theory to study how people and organizations use information systems and how the use of information system affect individuals, organizations, and their performance.

According to [START_REF] Volkoff | Critical Realism and Affordances: Theorizing IT-Associated Organizational Change Process[END_REF], the concept of affordance helps specify mechanisms and enables to build theories of the effects of introducing new systems into organizations. They define affordance as the potential for behaviors associated with achieving an immediate concrete outcome and arising from the relation between an object (e.g. an IT artefact) and a goal oriented actor or actors. [START_REF] Strong | A Theory of Organization-EHR Affordance Actualization[END_REF] distinguish between affordances as potential for action and actualization as actions taken by individuals to realize those potentials. They define actualization process the process by which the affordance, the potential for behavior, is realized in an organization and elevate the concept of affordance to organizational affordance, indicating the potential actions to be actualized by a group of individuals in order to achieve organizational level immediate concrete outcomes in support of organizational level goals.

Volkoff and Strong's (2013) study is particularly relevant since argue how affordances arise in the real domain from the relation between the assemblages of organizations and of IT artefacts, how affordances are actualized over time by organizational actors, and how these actualizations lead effects observable in empirical domain. Strong et al. contribution to the affordances literature is particularly significant because authors define an Affordance-Actualization mid-range theory for a particular IT adoption in a specific field sector, explaining IT-associated organizational change.

Hence, this study advances that Affordances-Based Theories are a correct method to study organizational changes that organizations are experiencing due to the disruptive introduction of technology and information systems. We assume moreover that anticipatory models can be created on the base of Affordances-Based Theories.

5

Importance of the Affordance to study IT-Associated Organizational Change

One framework that is used to study how people and organizations interact with technology and to explain the increasingly symbiotic relationship between IT and organizations is the one of affordances (Zammuto et al. 2007, Majchrak and[START_REF] Majchrzak | Technology Affordances and Constraints in Management Information System (MIS)[END_REF].

In this context affordances can be seen as a framework that help to study the technology adoption, the technology use, the impact on organizations and the consequences that derive from. Affordance-Based Theory overcomes the gap set by psychological, social psychological and sociological theories taking into consideration the possibility that individuals, using technology can create new practices and obtain new outcomes that could not occur without the use of technology (Majchrak and Markus, 2012). Beating the assumption in which technology was seen as something fixed and immutable, affordances need to be perceived and triggered by the actor to achieve the immediate concrete outcome, producing changes in the organizational routine that may affect future actions (a phenomenon called "imbrication"), such as IT and organizational changes [START_REF] Volkoff | Critical Realism and Affordances: Theorizing IT-Associated Organizational Change Process[END_REF]. With the same point of view, affordances also are well accommodated in the context of Business Intelligence, technology that affords dramatic changes in decision making process and performance (Chen et al.

2012).

Affordance-Based Theory surmounts limitations of the theories that focus only on social behavior, but as a sociotechnical theory does not derive from either a technological or social imperative, but both technological and organizational aspects emerge together and are taken into account with the same consideration. Affordance-Based Theory does not make simplistic and deterministic assumptions and does not incorporate constructivist logics, but, as based on critical realism, better explains in the context itself the phenomenon of the interplay, as a product of the human interaction with the artefact. Thus, Affordance-Based Theories can be use to better explain how information technology is implicated in organizational change [START_REF] Volkoff | Critical Realism and Affordances: Theorizing IT-Associated Organizational Change Process[END_REF].

Importance of Affordance to study Big Data Impact on Organization

As a pervasive digital technology, Big Data incorporate digital capabilities that deeply into the very core of the products, services, and operations of many organizations radically change the nature of them. Big Data provide an environment of technology affordances able to create innovations characterized by generativity, the capacity to produce unprompted change in organizational functioning [START_REF] Yoo | Organizing for Innovation in the Digitized World[END_REF]. The application of an Affordance perspective on the Big Data facilitates the comprehension of the interplay of the social actor, as people or organizational intents, and the technology. Through the Affordance lens, the impact of Big Data on organization can be studied as a phenomenon not yet considered by the current research literature.

Conclusion and Future Work

Information technology has been identified ad a key element for effecting needed changes in healthcare organizations. To achieve desired outcomes, such as better quality care, higher efficiency/productivity, reduced costs, and greater patients, providers and staff satisfaction, healthcare organizations have to be able to manage ITassociated changes, due to the massive production of data. In this specific context, affordances can be used to explain organizational change process associated with the accommodation and management of Big Data, improving the record of reaching the desired goals mentioned above. Because Affordance-Based Theories are quite new and not fully developed, more studies and researches can be carried on. Some gaps have been identified in developing, for example, valuable grand theories, general theories applied to all organizations and all IT artefacts, and further mid-range theories, theories applied in specific contexts and selected organizations focusing only on particular technologies. Affordance-Based Theories can be used to develop empirical studies based on the interaction between actors and IT in organizational changes, since interaction mechanisms are not yet clear. Methodologies can be also developed that are able to anticipate tendencies and to better understand and manage organizational changes for practitioners and managers.

In short, understanding the human interaction with the IT object through an Affordance-Based Theory can be meaningful to highlight how this interplay is shown and the healthcare field is a critical context where this relationship is particularly consistent. To conclude, we first summarized the key contributions from the literature, and highlighted why affordance are the turnkey to effectively study the interplay between information technology and organizations.

  They frame, but do not determine, the possibilities for action in relation to an object. Switching to the concept of affordance in terms of the sociology of technology, technology can be understood as artefact. This artefact may be shaped by and/or shaping of the practices humans use in interaction with, around and through them. Hutchby (2001) develops Gibson's concept of affordance declaring that affordances are functional and relational, in the terms that are enabling factors in a given actor's attempt to engage in some activity and that are different for one actor than for another. In an artefact, affordances do not necessary derive from natural feature of the artefact's materiality, but can be designed into the artefact; affordances set limits on what it is possible to do with, around, or via the artefact (Hutchby, 2001).[START_REF] Zammuto | Information Technology and the Changing Fabric of Organization[END_REF] use affordances to explain the relation between IT and organization and identify affordances for organizing as a concept that emerge from the inter-

section of IT system and organization system. Five possible affordances are listed (Visualizing Entire Work Process, Real-Time/Flexible Product and Service Creation, Virtual Collaboration, Mass Collaboration, Simulation/Synthetic Representation) as potentials/capabilities made possible by the combination of technical and organizational features. Zammuto et al.'s (2007) research is relevant for us as they were the first to coin the term affordance for organization as a bridging concept that emerges from the intersection of IT system and organization systems, highlighting the intertwining of IT and organizational features. Majchrzak and Markus (2012) set a light on what they named technology affordance as "action potential, that is, to what an individual or organization with a particular


Pigni Federico

Vitari Claudio

DDGS affordances for value creation

Keywords: Digital Data Genesis and Streaming, affordances, intents, technological capabilities, value creation, service engineering, service management

1

Research introduction

The constant interaction of users with devices embedding digital capabilities both voluntary and unconsciously is a reality that has far reaching implications for individuals and human relationships, always and everywhere connected [START_REF] Yoo | Organizing for Innovation in the Digitized World[END_REF]. Volumes of data are born in digital form both as a result of humans' and devices' activities (Digital Data Genesis). Current studies in the Information Systems domain approach the phenomenon from a data management perspective, mainly focusing on the concept of big data. The core reasoning of these studies is that organizations now have the opportunity to collect, process, and store ever-growing and complex datasets to support their decision making processes and strategies. These large and complex databases require specific storage, management, analysis, and visualization technologies to be effectively used, such as: business intelligence and analytics tools to dive and make sense of big data [START_REF] Chen | Business Intelligence and Analytics: From Big Data to Big Impact[END_REF][START_REF] Pospiech | Big Data -A State-of-the-Art[END_REF]. Very few studies tried to explain the strategic opportunities for extracting value from the realtime streams of digital data that are being created in massive quantities other than through analytics [START_REF] Piccoli | Harvesting External Data: The Potential of Digital Data Streams[END_REF]. We think that new forms of value creation emerge from the interplay of real time data streams and a firm's complementary resources [START_REF] Piccoli | Harvesting External Data: The Potential of Digital Data Streams[END_REF]. To understand the uses and consequences of real time data streams we adopt a framework rooted in Technology Affordances theory and we consider the dynamic interactions between people and organizations -the social actors -, and the technological artifact. In this paper we will present new conceptual tools to understand and profit from real time digital data streams.

The research perspective: affordances

Recent research has identified the interplay of social actors' intents and technology capabilities as at the origin of customer value and the consequent success of technological innovations and services [START_REF] Jung | Enhancing the Motivational Affordance of Information Systems: The Effects of Real-Time Performance Feedback and Goal Setting in Group Collaboration Environments[END_REF][START_REF] Zammuto | Information Technology and the Changing Fabric of Organization[END_REF]. It is from this interplay that uses and customer value emerge -whether planned or serendipitous. Uses and value could be conceived and expected by design or emerge unexpected with use, in either case what emerges are unique capabilities of the bundle that neither the technology nor the social actor alone could exercise [START_REF] Nevo | The formation and value of IT-enabled resources: antecedents and consequences of synergistic relationships[END_REF]. These uses are named Affordances [START_REF] Hutchby | Technologies, Texts and Affordances[END_REF]. An Affordance represents the "opportunities for action" as perceived by a social actor in its environment. Moreover, social actors perceive differently the possibilities of an artifact to afford an action. As a possibility for action, rather than the action itself, an Affordance is conceptually separate from a given behavior, and it is the necessary precondition to it. The affordance perspective assumes that a technological artifact has some recognizable functionalities but that their analysis "in-use" has to account for the awareness of social actors' intentions, thus recognizing the object's social nature [START_REF] Hutchby | Technologies, Texts and Affordances[END_REF][START_REF] Fayard | Photocopiers and Water-coolers: The Affordances of Informal Interaction[END_REF][START_REF] Montesano | Leaming Object Affordances: From Sensory-Motor Coordination to Imitation[END_REF]. Thanks to this concept, the materiality of an object and a social actor's perceptions are viewed relationally and jointly in terms of how each favors or shapes actions.

In Information Systems, affordances are the result of the confluence or intertwining of the capabilities provided by information and communication technologies and the actions taken by the social actors using them [START_REF] Majchrzak | Fostering Innovation and Intellectual Capital Creation: The Paradoxical Influence of Social Media Affordances[END_REF]. In this context, the embedding of digital capabilities in artifacts is indeed seen as a manifestation of the digital materiality of the object itself [START_REF] Yoo | Organizing for Innovation in the Digitized World[END_REF].

We adopted this alternative research perspective because we aim to take a fresh look at the interplay between technology and people, but to do so as in an emerging phenomenon. The affordance concept is promising for gaining a deeper understanding of the possible uses of information technology to engineer and manage services. In so far as we are interested in engineering and managing services we take into consideration affordances that make innovative digital services possible, referring to them as Service Affordances.

We consider that the current technological capability to digitally represent and stream actions in real-time (DDG and DDS) provides the opportunity for new service value creation.

Research focus

In this section we focus our attention on the specific category of Service Affordances emerging from Digital Data Genesis and Stream (DDGS). Our main interest is related to the opportunities for servicing customers emerging from organizational intents, technological capabilities, and individuals.

Defining Digital Data Genesis and Stream

We define Digital Data Genesis as the real-time inception of an informational representation of an event or state of an entity in digital form. The defining characteristic of this phenomenon is that data are born digital: the timing of digital data generation is concurrent with the event that the digital data represent [START_REF] Vitari | ANTECEDENTS OF IT DYNAMIC CAPABILITIES IN THE CONTEXT OF THE DIGITAL DATA GENESIS[END_REF][START_REF] Raguseo | Gaining Competitive Advantage from Digital Data Genesis Dynamic Capability and the Moderating Role of Environmental Turbulence[END_REF]. The fact of a restaurant waiter taking a food order with a palm electronic device as soon as a customer expresses his wishes is a simple example of DDG. The digital data is the information representation of the food order and its creation is concurrent with the customer's order formulation. It's also DDG when the customer her-self passes the order using a touch screen installed at the table. DDG is a different concept from digitalization. Back to the example, DDG would not occur if the order is taken on paper and subsequently input into the electronic cash register. In this case the data existed in another form before becoming digital, data was not born digital but digitalized.

As a consequence, DDG enables information representations of real objects and events, without significant delays (in near real-time). In addition, the digital form of these information representations facilitates their interaction, manipulation and communication in the electronic information space. As previously defined, DDG activity is separated from any consecutive information processing or more sophisticated analytics. Digital Data Streams (DDS) are composed by the flow of data originating from DDG activities and can be intercepted and used by organizations or individuals [START_REF] Piccoli | Harvesting External Data: The Potential of Digital Data Streams[END_REF]. Rather than focusing on technical specifications (push versus pull, RSS versus messaging), we characterize a DDS pragmatically as a data source that is constantly (or very frequently) changing and evolving. A database that is accessible in real time through an API and is updated as new data becomes available is an instantiation of a digital data stream. Therefore, the defining characteristics of a DDS are timeliness and a continuous flow of DDG events. The prototypical instance of a DDS is one in which DDG events are streamed continuously in real-time. Twitter is an example: each tweet is a DDG event and the tweet-stream, eventually accessible through API, is a DDS. In conclusion, the combination of DDG and DDS makes Digital Data Genesis and Stream (DDGS): the flow of real-time inceptions of informational representations of events or states of entities in digital form that can be intercepted and used by organizations or individuals. For example, a single click on a hyperlink is the digital representation of a person's decision (a DDG event). Activity on a website is a stream of personal decisions, and is aptly called the clickstream.

Service Affordances from DDGS

The application of the affordance perspective on DDGS phenomena facilitates the comprehension of the interplay of a social actor's intents with the flow of real-time digital data and the respective technological capabilities that carry it out. The principal social actor's intents contributing to the emergence of service affordances are about the intent to provide value adding services. This intent, stemming from the technological capabilities of DDG and DDS, interacts with the customer's willingness to be serviced. Value is created when the social actor is able to enact the Service Affordances to increase the customer's willingness to pay for its offers or reduce the opportunity cost of the resources it needs to create existing value propositions. The principal technology capabilities contributing to the emergence of Service Affordances from DDGS concern ever more pervasive digital data acquisition, near realtime processing, and telecommunicating systems. In practice, the interplay of these digital capabilities and the above-mentioned social actors' intents results in the Service Affordances. The combination of the multitude of these social actors' intents with the multiplicity of technologies having these capabilities determines infinite Service Affordances: from the waiter's use of palm devices to register and stream food orders to the restaurant kitchen, to worldwide financial high frequency trading round the clock. Hence, we have decided to focus on the affordances that create value in radically new and unique ways. Through an analysis of around 60 different empirical business examples, we identified four of them: Sensing, Mass Sensing, Experimenting, and Coordinating.

Sensing

We define Sensing as the affordance gained by detecting in real-time the current state of a single given entity (human, object, event...). Examples are the location of a singleairplane, the speed of a specific car, or the mood of an individual person. We consider this affordance to be at the base of all the new value creation opportunities offered by DDGS, making it a first-order affordance, as the other DDGS affordances are built on Sensing. Effectively, Web 2.0 and social networks have allowed the creation of new ways for people to express themselves in real time. 4Square, for example, provides

Sensing on other people location and "exploration" behavior. Users are uniquely identified and traced through DDG capabilities ("the check-in" and the GPS data). Sensing is an affordance emerging also from machine-tomachine (M2M) data streams as electronic devices have an unprecedented ability to automatically and continuously sense the environment, and automatically react to the incoming flow of data. Examples are the automatic alerting system at an air traffic control center in case of specific flight path deviation or the automatic cruise control of the speed of a car. The technological capabilities that support Sensing affordance include the diffusion of electronic identifiers (RFID chips, flash-tag…) and relative readers (smart phones, RFID readers, cameras…) or those technologies providing the possibility to communicate a status update, and the processing capability to analyze the characteristics of the single entity in near real-time. These characteristics can be brought along by the entity it-self or remotely stored elsewhere and accessed at the moment of the individual identification.

The sensing intent is related to the social actors' intention to exceed current bounded perception related to their own sphere of experience. By tapping into a wider characterization of digitally represented entities cognition is extended beyond traditional physical barriers. Well-established examples relate to the Wal*Mart mandate to its suppliers to apply RFID tags to every single product or to all cattle in Canada and Europe.

Mass Sensing

We define Mass Sensing as the affordance gained by identifying the state of multiple entities in real time, altogether, contextualized by their relationships. Mass Sensing is a second-order affordance based on the Sensing affordance. For example, if real-time sensing makes it possible to locate a single vehicle, it is therefore possible to sense all the cars on a road and traffic congestion could be detected. Both TomTom (http://www.tomtom.com) and Inrix (http:// www.inrix.com) are examples of how sensing a mass of vehicles can be used to provide added-value services to drivers in the form of real-time traffic information and more efficient routes. Both companies aggregate information from a multitude of DDS sources, including telecom operators, road sensors, and navigation systems readings. The technological capability that supports Mass Sensing is related to the development of rule-based and filtering functionalities and of complex event processing engines. The joint effect of the capability to filter relevant information and process them provides the opportunity to "sense" and evaluate multiple and meaningful concurrent events. Through these technological capabilities, each entity of the population is uniquely identifiable and distinguishable. The availability online of massive information concerning products, services or user behaviors effectively determines the potential for comparing and choosing based on data available for the entire population. The Sensing intent is related to the social actors' intention to track and trace entire entity populations both in time and space. In business this intent is pursued with the aim of optimizing business processes and streamline activities, and of exerting market power. This intent typically resulted in specific strategic initiatives deployed to increase the control of product and services along supply chains or in retail surfaces or for monitoring resources and capacities (empty seats for airliners, number of passengers, flow of people, products on the shelves, etc.).

Experimenting

We define Experimenting as the affordance gained by fast cycling data generation and streaming on the entity with actions on this measured entity or its environment. Experimenting is a second-order affordance based on the Sensing affordance. A/B tests on web pages for selecting a layout, or the massive experimentation ongoing in major websites, are examples of this affordance. The Experimenting affordance makes it possible to test and have immediate feedback on business decisions, from the change of a webpage layout to more complex information. New Brands Analytics provides a service that extracts specific feedback from customers' unstructured mentions on social media channels. Firms can then adjust their behaviors in real time, correct any shortcomings, and monitor the outcomes. At the same time, they can experiment with different configurations of the service and fine-tune it on the basis of customers' mentions. The technological capabilities that support this affordance include, in addition to the capabilities required for Sensing affordance (electronic identifiers, readers, processing capability), the actuators (software as well as hardware: thermostats, motors, electro-active polymers…) located in the sensed environment. The social actors' intents that support this affordance are related to the wish to understand the material reality. This wish to understand takes the specific form of the empirical method that arbitrates between competing theories, models and hypotheses through experimental science. The combination of these technological capabilities and human intents allows the emergence of Experimenting.

Coordinating

We define coordinating as the affordance gained by adjusting a behavior based on fast cycle feedback regarding the current state of other entities.

Coordinating is a second-order affordance based on the Sensing one. This affordance is at the base of services such as Foursquare Radar, which enables users to coordinate spontaneously with friends by "sensing" their presence in the area. The technological capabilities that support this affordance include the diffusion of electronic communication devices (mobile phones, computers…) and the telecommunication infrastructure (Internet network, GSM network…) making the information exchange between these devices possible. Moreover, the availability of virtual networks -both social and machine to machine networks -provides the logical infrastructure for the exchange and the digital coordination to emerge. The social actors' intents that support this affordance are related to the willingness to coordinate with the other social actors as well as artifacts. This peer-to-peer approach spreads out together with the principle of human equality and mutual aid in order to achieve objectives beyond the capability of the single individual. The combination of these technological capabilities and human intents allows the emergence of the Coordinating affordance. An example of this affordance comes from the Arab countries where protesters used electronic communication devices (and applications like Twitter and Facebook) to coordinate and rally their supporters without a real or organized coordination [START_REF] Christensen | Twitter Revolutions? Addressing Social Media and Dissent[END_REF].

Discussion and conclusions

The affordance perspective facilitates the understanding of the interplay of people or organizational intents and technology capabilities in customer value creation and the consequent success of technological innovations and services. In a society where an increasing number of activities of public and private organizations are engineered and managed as services, the affordance perspective can be particularly valuable for innovation and competitiveness. IT and Information systems are at the core of this transformation. Specifically, the crucial role played by data -through the development of pervasive computing, the spreading of sensors and the increase in the creation, storage, communication and processing of information -incites researchers to study DDGS. In particular, we identify central research questions that could be explored to enhance understanding of the levers for improving service development, engineering and management based on DDGS, through the affordance perspective: What are the levers and barriers in the emergence of DDGS affordances? How do individuals and organizations recognize DDGS affordances? How do individuals and organizations select DDGS affordances for services? To what extent do the organization's capabilities influence service development, engineering and management based on DDGS affordances? We will start answering some of these questions by deepening our knowledge on the around 60 business cases we identified for our four service affordances.


Value creation in the era of Big Data.

Work Group (please mark your position paper's objective(s) with a X): University/Business driven Applications X University/Public driven Applications Data Challenges Impacts on Organization Design Partnership/Fellowship

State of the art

The concept of big data stems from the evidence that organizations have now the opportunity to collect, process, and store ever-growing and complex datasets to support their decision making process and strategies. These large and complex data bases require specific storage, management, analysis, and visualization technologies to be effectively used, such as: business intelligence and analytics tools to dive and make sense of big data (Chen, Chiang, & Storey, 2012;[START_REF] Pospiech | Big Data -A State-of-the-Art[END_REF]. The first wave of scientific research on big data was mainly technically driven and centered around aspects of data provisioning [START_REF] Pospiech | Big Data -A State-of-the-Art[END_REF] and data management [START_REF] Briody | Big data: Harnessing a game-changing asset[END_REF]. More recently, a new set of studies provided a first assessment of big data impacts and analytics' developments [START_REF] Chen | Business Intelligence and Analytics: From Big Data to Big Impact[END_REF] [START_REF] Abbasi | METAFRAUD: A META-LEARNING FRAMEWORK FOR DETECTING FINANCIAL FRAUD[END_REF][START_REF] Chau | BUSINESS INTELLIGENCE IN BLOGS: UNDERSTANDING CONSUMER INTERACTIONS AND COMMUNITIES[END_REF][START_REF] Hu | NETWORK-BASED MODELING AND ANALYSIS OF SYSTEMIC RISK IN BANKING SYSTEMS[END_REF][START_REF] Lau | WEB 2.0 ENVIRONMENTAL SCANNING AND ADAPTIVE DECISION SUPPORT FOR BUSINESS MERGERS AND ACQUISITIONS[END_REF][START_REF] Sahoo | A HIDDEN MARKOV MODEL FOR COLLABORATIVE FILTERING[END_REF][START_REF] Park | A SOCIAL NETWORK-BASED INFERENCE MODEL FOR VALIDATING CUSTOMER PROFILE DATA[END_REF]. These studies support the observed relationship between company performance and a data driven organizational orientation [START_REF] Mcafee | Big Data: The Management Revolution[END_REF]. Very few studies focus on the strategic opportunities for extracting value from the real-time streams of digital data that are being created in massive quantities (Gabriele [START_REF] Piccoli | Harvesting External Data: The Potential of Digital Data Streams[END_REF] [START_REF] Piccoli | Profit from Customer Data by Identifying Strategic Opportunities and Adopting the "Born Digital" Approach[END_REF]. We think that new form of value creation emerge from the interplay of the three key characteristics of big data -volume, velocity and variety [START_REF] Mcafee | Big Data: The Management Revolution[END_REF]) and firm's complementary resources [START_REF] Piccoli | Harvesting External Data: The Potential of Digital Data Streams[END_REF]. This is the core of our research.

Our current work on big data

We decided to focus our work on the opportunities of value creation emerging from the exploitation of massive quantities of real time data that can be used both for real-time tactics and long-term strategy. We explained how organizations can leverage these digital data streams (DDSs) to increase consumer value and to improve operational efficiency (Gabriele [START_REF] Piccoli | Harvesting External Data: The Potential of Digital Data Streams[END_REF].

Advancing the management field: new opportunities for value creation

With the support of the Advanced Practice Council of the Society for Information Managemnet (http://www.simnet.org/?page=About_APC) we developed new conceptual tools to help managers identify

Authors:

Pigni Federico Piccoli Gabriele Vitari Claudio Floreddu Paola and comprehend the opportunities for innovative value creation. We have shown that there are significant opportunities once organizations have identified digital data streams and treat them as a central and coherent unit of analysis.

Extracting value from DDS

We have identified five value-creation mechanisms or value archetypes, which are essentially "templates" that allow organizations to create value using DDSs (Gabriele [START_REF] Piccoli | Harvesting External Data: The Potential of Digital Data Streams[END_REF]. Each of the five archetypes represents a class of value-creating initiatives that may employ one or more DDSs. What enables these data streams to contribute to value creation is a value driver, which is the unique quality of an activity that is the source of the activity's value contribution. We have identified four value drivers. Value drivers represent the tangible elements of a DDS that enable a firm to achieve the goals of either increasing a customer's willingness to pay or reducing the opportunity cost of existing resources, thus generating strategic value. A value driver is the mediator between the potential for action and an actual business implementation (i.e., a value archetype). Value can then be created through a mechanism rooted in one or more value drivers. The five archetypes and four drivers are depicted as a value tree, with the drivers providing the roots for the archetypes (see Figure 1).

The value archetypes

A value archetype is a basic model for value creation that is based on digital data streaming. Value archetypes are not mutually exclusive, and each organization may create value by using more than one approach simultaneously. In our research we identified five value archetypes:

1. DDS Generation. A firm creates value by originating the stream of data itself, either knowingly or as a byproduct of other activities. 2. DDS Aggregation. A firm creates value by focusing on collecting, aggregating and repurposing DDS.

DDS Service.

A firm creates value by using one or more DDSs to provide services to consumers or to improve service quality. 4. DDS Efficiency. A firm create value by using real-time data streams to optimize internal operations or to track business performance (e.g., waste reduction, response speed).

DDS Analytics. A firm creates value by processing DDS information to produce analyses or

improved visualizations with the objective of enabling better decision making and producing superior insight or knowledge (for example, through dashboards and data mining).

Four Value Drivers

Within the framework of the five value archetypes, companies need to focus on and understand the value drivers for each archetype-the enabling factors behind the business opportunity. By identifying value drivers, companies can use DDSs for innovation as they identify new possibilities to create value. In our research, we identified four value drivers: Examples are the location of a plane, the speed of a car or the mood of an individual. We consider the real-time sensing value driver to be the first-order value driver, because it is the basis of all of the new value-creation opportunities 2. Real-time Mass Visibility. This second-order value driver is based on real-time sensing. It represents the ability to identify the state of multiple entities in real time, contextualized by their relationships. For example, if real-time sensing makes it possible to locate one vehicle, it is possible to acquire visibility of all vehicles on a road, thus enabling traffic congestion to be detected. This driver is the ability to quickly adjust behavior based on feedback about the current state of other entities. Perhaps the most important insight regarding value drivers is that a company does not have to actually own the data generation asset (the DDS). Instead, all the firm needs is access to the DDS and a concept for how to use that DDS to add value. The criteria for success are based on an organization's ability to identify valuable DDSs, gain access to them with the appropriate tools, properly orchestrate the available resources and invest in DDS initiatives.

Ongoing research and expected results: from new value to new capabilities

We started to observe recognizable patterns in those companies that were most benefitting from big data exploitation. Moreover, dealing with real-time DDSs required a different set of organizational capabilities than dealing with traditional static Business intelligence datasets. Our early research suggests that, to implement a DDS strategy, a firm must have a sound footing in the following four capabilities:

1. Mindset: A critical component of innovative initiatives is the willingness to invest and face risks in data-based strategic initiatives. A hallmark of the successful DDS initiatives we studied was that the "mental readiness" of organizational members made them ready to embrace change. 2. Skillset: A DDS strategy consists of more than simply collecting DDS data. As the five valuecreation archetypes show, the firm must develop the competencies to orchestrate the complementary resources necessary to deliver value based on the DDS. The main focus is on the managerial actions undertaken to implement DDS initiatives underpinning a specific ability of coordination within and across organizational boundaries. 3. Dataset: A DDS strategy hinges on being able to tap into valuable sources. The dataset refers to the ability to effectively access and use the real time data stream and match it to the organizational needs for value generation. An effective access and use implies, in turn, a good understanding of the data contained in the data streams, and of the business needs of the organization. 4. Toolset: Once a source is identified, the firm must be able to tap into the streaming data. This requires the ability to use appropriate tools to harvest the DDS or multiple DDSs. The toolset refers to the access to and the ability to use the technical components that allows DDS exploitation. In this sense it requires both the technical elements that are a pre-requisite of a real-time analytical system tuned for processing real-time data streams.

We are currently developing a readiness index as a composite measure of these capabilities to assess the capacity of organization to deal with real-time initiatives.

Big data and the managerial education

The four capabilities identified in our research provide useful guidelines for defining the educational objectives on big data. Clearly the domains of expertise involved are multiple both of a technical and managerial aspects, hence adequate managerial and technical skills are required. If specific programs putting an emphasis on analytics, business intelligence, big data management, post-relational databases could provide the specific competences to face the analytics challenges, a more cross-competence profile is probably necessary to develop the above-mentioned capabilities. 1. Mindset. Future managers should be aware of the potentials of leveraging digital data both for supporting decision making and creating new strategic value for their organization. The actions are threefold: a. Develop manager's awareness and conscious strategic thinking on top of data assets and resources. Increase the capacity of managers to leverage complementary resources -both internally and externally -to create new value. b. Develop adequate analytical skills in managers to support their decision making process, as the increasing volume of accessible information requires mastering modeling and analytical abilities rooted in business statistics. 2. Skillset. Future managers will be required to advance unique configurations of resources to deliver value with data in the form of new products, processes, decision-making routines, etc. They will be required to configure and deploy process to coordinate co-specialized assets, providing a vision for those assets, and nurturing innovation, underpinning a specific capability of coordination within and across organizational boundaries. Communication skills will be required both to properly interact and communicate with the different domain experts and stakeholders internally and externally. 3. Dataset. Future managers will be invited to understand the business context in which the data is used, and its specific characteristics. These aspects are generally reflected in a specific data and information governance configuration that has to be interpreted. The use of low quality data for critical decision making process could have disastrous consequences, as high quality ones are associated to firms success. Managers will need appropriate skills to deal with this issue. 4. Toolset. The future manager will need to properly make sense of the technical capabilities of the firm in facing the big data challenge. Several are the examples in which new architectures and software have emerged once the fulfillement of the business objective required to overcome currents systems' limits .

Foreseen Challenges and Recommendations

The challenge that we are called to face consists in helping organization to profit from the opportunities emerging form big data. We suggested that this challenge can only be win by developing new skills and through a different approach to data that has to become pervasive in organizations. These changes would not happen overnight, and specific actions have to be undertaken. Our research suggests focusing on the four major organizational capabilities we identified. Furthermore, organizations should start considering process and resources trespassing organizational boundaries. More and more valuable data and complementary resources lay behind the traditional firms' perimeters. Consequently, the acquisition (e.g. harvesting tweets data stream) or the creation of new data sources (e.g. example deploying a sensor network) should be proactively examined.

Figure 1 :

 1 Figure 1: The value tree

3. Real-time Experimentation.

  This second-order value driver also relies on real-time sensing. It consists of the possibility to fast cycle reliable data generation and gathering. Comparisons of a control sample to other samples (e.g., A/B tests) on web pages for selecting a layout, or the massive experimentation ongoing in major websites, or business experiments are examples of this value driver. 4. Real-time Coordination. The third second-order value driver is also based on real-time sensing.


Claudio Vitari
email: vitaric@iae.grenoble.fr

Aurelio Ravarini
email: aravarini@luicc.it

Validation of IS positivist research: an application and discussion of the Straub, Boudreau and Gefen's guidelines

Straub, Boudreau and Gefen gave an important contribution in the critical scientific issue of the rigor in IS positivist research. They published an analysis of the state of the art of the research validation methods and heuristics. In an attempt to contribute to the debate on the rigor of IS positivist research, an application of their validation guidelines is practiced and discussed. The validation guidelines are applied to test IS success of a specific type of Information System: the Expert Recommender Service. The application point out some relevant issues regarding the method, such as: the importance of the practice of the expert panels and judges, the quality of the findings of a statistical analysis when the discriminant validity is doubtful, the relevance of the application of different methods altogether, the usefulness of complementary or alternative methods and heuristics and the interpretability of a model when one hypothesis lacks statistical significance.

Introduction

Straub, Boudreau and Gefen gave an important contribution to the discussion on the critical scientific issue of the rigor in IS positivist research. They stated that: "Without solid validation of the instruments that are used to gather data on which findings and interpretations are based, the very scientific basis of the profession is threatened". They published an analysis of the state of the art of the validation methods and heuristics. Their list of validation techniques should be applied to any positivist research instrument in order to improve its validity and to ensure a scientific basis of the findings. In an attempt to contribute to the debate on the rigor of IS positivist research, their validation guidelines is applied and its outcomes are discussed. This application shows some of the obstacles that the researcher faces in practice and their possible bypasses. Moreover, this article casts light on the divergences between the guidelines proposed by Straub et al. and their actual application. The author tries to question to what extent an IS positivist research can diverge from the proposed validation guidelines without lacking in rigor and without compromising its acceptance at the top IS journals.

The context of application of the validation guidelines

The validation guidelines are applied to test IS success of a specific type of Information System: the Expert Recommender Information System (ERS). The DeLone and McLean model of IS success, proposed in 1992 and updated in 2003 , has been adapted to the specific context of the research and extended to include an external variable, the Knowledge of the Other members of the Community of Practice, which emerged as the most important independent variable during the preliminary explorative phase.

The ERS

The choice of the type of IS has fallen on a type of Knowledge Management Systems, the ERS, as they present several limitations negatively impacting their success . The ERS specific area of application is the identification and display of the individuals who could be potential source of specialized knowledge, which -otherwise -would result hardly accessible . This type of computerbased systems are commonly addressed as ERS because they identify and display the individuals supposed to be experts and in the position to help the users to solve problems of business process breakdown. In order to avoid information overflow, this Information System should provide information only about a precisely selected subset of all the individuals, who could likely have the knowledge that should most likely satisfy the users' requests

The adaptation and extensions of the D&M IS Success Model

The adaptation of the D&M IS Success Model has been required to fit the specific context of application: the ERS, through three main variations:

• Service Quality will be the only dimension of the ERS success that represents all the characteristics of the ERS.

• Individual Impact variable will be declined into the variable Perceived Usefulness for the user

• Organizational Impact variable will be declined into the variable Information Access Benefit.

Moreover, the D&M IS Success Model has been extended to include the influence of the Community of Practice through the variable Knowledge of the Others. This inclusion contributes to the relevance of this study. In fact, it is not an exclusive application of an IS success model, as it proposes its extension and adaptation.

The Validation Guidelines: application and discussion

Straub's et al. contribution to the development of the methodology of IS research is undebatable. Nevertheless, Straub et al. recognize that "some of these heuristics will, no doubt, be controversial" and "that it is time for the IS academic profession to bring such issues into the open for community debate". This article tries to contribute to the ongoing debate on the validation methods and heuristics, through a case of application of their guidelines in an empirical study on ERS. In this case, the validation was supported by packaged software: SPSS and AMOS. 265 valid responses to a questionnaire were collected, with a response rate of 31%. Straub's et al. guidelines propose to ensure: the content validity, the construct validity, the reliability, the manipulation validity and the statistical conclusion validity through a set of 17 different validation tests. All these tests were applied to the empirical study, but given the constraints on the length of the article only those that led the most relevant issues to emerge will be discussed in the next paragraphs.

Content validity

Content validity concerns the representation, by the instrument, of the content of a given construct, in terms of measurement, substance, and straightforward definition of the construct.

In this study the literature review and the personal opinion of some experts have been used to assess the content validity. The literature review has been the primary source. Concomitant with the literature review, the author asked several experts, colleagues, and users of the ERS to screen the items to find possible inconsistencies. This step brought to a refinement of the instrument in terms of the kind and the number of questions. The original scale used by Pinsonneault for the construct Knowledge of the Others was judged redundant. Seven items seemed excessive and the revision brought to the inclusion of only four items each of one measuring a specific aspect of the individual perception of the awareness on the knowledge domains of the others members of the Community of Practice. The original scale used by Bhattacherjee for the construct Perceived Usefulness for the User revealed an ambiguity on an item that resulted misleading in its attempt to synthesize the "usefulness" concept. The other three items, on the contrary, seemed well explaining and completely covering the concept. So the fourth item was excluded. These modifications could make the instrument too much fitted with the Information System experienced by the judges, therefore endangering its applicability in other contexts. Moreover, the content validity ratios and Qsorting (the other techniques proposed by Straub et al.), would have not solved the risk of over fitting. A possible by-pass could be the systematization and the compulsory use of the technique of expert panels and judges at each new application of the construct, in order to be sure of the content validity of the instrument in the specific context of application.

Discriminant validity

The discriminant validity distinguishes the reflection of the items to a specific construct from the items that reflect the other constructs. In our study, this test, through confirmatory factor analysis, was performed with SPSS and AMOS.

The outcomes from AMOS comforted on the discriminant validity of the entire model, while the result of the confirmatory factor analysis in SPSS showed that ERS Quality construct and User Satisfaction construct dangerously loaded on the same factor. A second order confirmatory factor analysis on these two constructs highlighted an unexpected loading on the second factor of an item of the ERS Quality instrument. This item appeared as the potential source of the failed test of the discriminant validity. The test of the Cronbach's Alpha shows that this item is inadequate, maybe due to some misunderstandings for the negative formulation of the sentence. The deletion of the item would improve the Cronbach'Alpha, higher up to acceptable thresholds. So, the item was deleted and confirmatory factor analysis run again, but ERS Quality scale and User Satisfaction still loaded on the same factor.

A second order confirmatory factor analysis on the two constructs highlighted the following critical situation: the ERS Quality scale was now composed of only two items loaded on the two factors. Deleting the item loading on the second factor would have transformed the ERS Quality construct into an instrument with only one item. The wish to avoid it, in order to prevent monooperation bias, led to the decision to keep the ERS Quality scale as it was.

The loading of two constructs on the same factor is an evident signal of lack of discriminant validity and the deletion of one item of the scale did not entirely solve the problem. The willingness to avoid mono-operation bias prevents the further deletion of another item of the same scale. This arguable choice, between discriminant validity and mono-operation bias prevention, opens the issue of the importance of the statistical demonstration of the discriminant validity versus the importance of avoiding mono-operation bias. A further investigation to understand the prevalence of a criterion on the other is prospected.

A more critical issue arises from this discussion: the reasonability of proceeding further with the statistical analyses. Can the other validities be measured and assessed in the absence of discriminant validity? And if the analyses are carried out, what are the additional limitations of the results? These are all questions that need a further investigation.

Common methods bias control

The common methods bias is also known as "method halo" or "methods effects" and it occurs when data is collected via only one method, because the different pieces of data share part of the variance that the items have in common with each other due to the data collection method rather than to hypothesized relationships. In order to reduce it, questions' distribution, in the questionnaire, was randomized. Nevertheless, only one method has been used, and the randomization does not guarantee against the risk of common method bias. Therefore different methods should be put in place to avoid the bias, but the use of multiple methods increases the complexity and difficulty of data collection. With only one method, a significant incertitude on the importance of the common method bias remains, limiting the quality of results. It seems therefore important to insist to find ways to employing different methods altogether, besides randomizing the questions in the questionnaires, balancing it with the cost of these additional methods.

Statistical conclusion validity

The statistical conclusion validity assesses the mathematical relationships between the variables of a model and it infers whether the statistical formulation correctly expresses the true covariation. Fig. 1 The model at the quantitative phase (Standardized regression weights. "***" significant at the 0,001%; "*" significant at the 0,05%; "not sig." not significant at 0,05%)

The critical issue at this step is about the significance of the entire model when a hypothesis is not statistically significant. The covariance-based SEM can be employed only in confirmatory research, therefore the lack of statistical significance of a hypothesis should lead to reject the theory that this application was trying to confirm. Finally, not only one hypothesis is put in doubt, but the entire model could be discredited. The convenience of the post-hoc analysis is also doubtful, as the confirmatory research does not include the exploration of new relationships between variables.

Conclusion

Straub et al. gave an important contribution in the critical scientific issue of the rigor in IS positivist research. With this article, the author tried to continue the discussion on this issue, through the application of their guidelines and the discussion of the main results (a detailed analysis of the application and discussion of the guidelines ca be provided on request). This experience highlighted some possible difficulties that the researcher faces in practice and the effects on the quality of the findings and interpretations and finally their publication.

A number of aspects emerged as critical. First, in order to ensure the content validity a regularization of practice of the expert panels and judges is recommended in order to obtain instruments that really fit with the actual application context. Second, the author questioned the reasonability of the continuation of the statistical analysis when the discriminant validity is doubtful. Third, it seems important to insist on the application of different methods altogether to avoid the common method bias. Fourthly, Straub et al.

  proposes only one method to test the unidimensional reliability, hence the development of complementary or alternative methods and heuristics could improve the assessment of the validity of the instruments. Fifthly and finally, the lack of a statistical significance in one hypothesis when realizing the statistical conclusion validity in a confirmatory research opens the issue of the validity of the other and significant hypothesis and of the entire model. An application and discussion of the guidelines of Straub et al. have bee presented in this article, which aimed at sustaining the debate on the rigor of IS positivist research. Nevertheless, the relevance of the rigor of IS positivist research should not undermine the relevance of the research in it self.

The dimension of the sample, the number of the questioned items, and the number of the variables of the analysis limit the suitable statistical methods. The sample of 265 answers and the 18 items measured on the 6 variables make the SEM a suitable and preferred method able to ensure trustworthy and sophisticated results. Moreover, the covariance-based SEM is the only method which shows that the null hypothesis of the entire model is plausible, rejecting the path-specific null hypotheses of no effect. In this study, the covariance-based SEM technique has been employed by the AMOS 6 application. The overall fitness of the model was good, with all the indices over the respective threshold. Moreover, the Post-hoc analysis appeared even superfluous and hence not applied, because the Modification Indexes were not large enough to suggest the ad hoc modification of the model. In terms of the single hypotheses, all the nine hypotheses are statistically significant except the causal relationship of Use on User Satisfaction, which is therefore not validated.