Claudio Vitari
email: claudio.vitari@grenoble-em.com

Federico Pigni
email: federico.pigni@grenoble-em.com

DDGS affordances for value creation

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

The world is changing, and is changing thanks to the data. New opportunities for business action and service creation are emerging from the massive availability of real-time digital data. We characterize these opportunities proposing a framework rooted in the Technology Affordance theory studying the dynamic interactions between people and organizations, and the technological artifact. This approach provided encouraging results in understanding the dynamic nature of IT based innovation.

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 real-time 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. Affordances allow us to overcome some of limitations of current management theories [START_REF] Majchrzak | Technology Affordances and Constraints in Management Information Systems (MIS)[END_REF]:

1. that ignore the possibility that humans using technology can enact new practices or outcomes by focusing only on psychological or social behavior; 2. that technology can produce and have unintended uses, thus overcome simplistic or deterministic assumptions about the effects of technology on human and organizational outcomes.

The affordance concept is then 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.

2.1

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 realtime). 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 realtime 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 real-time 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 realtime the current state of a single given entity (human, object, event...). Examples are the location of a single airplane, 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 secondorder 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 secondorder 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. The value of an affordance approach lies clearly in the relational definition of the "affordance" concept linking actors and technology. 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 making managers better "system thinkers" [START_REF] Majchrzak | Technology Affordances and Constraints in Management Information Systems (MIS)[END_REF]. IT and Information systems are at the core of this transformation. Specifically, the crucial role played by datathrough 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.

adfa, p. 1, 2011. © Springer-Verlag Berlin Heidelberg 2011


Kazuyuki Motohashi

Understanding AI Driven Innovation by Linked Database of Scientific Articles and Patents

Keywords: Patent data, AI, General Purpose Technology

The linked dataset of AI research articles and patents reveals that substantial contribution of public sector is found for AI development. In addition, the role of researchers who are involved both in publication and patent activities, particularly in private sector, increased over time. That is, open science, publicly available by research articles and propriety technology, protected by patents are intertwined in AI development. In addition, the impact of AI, combined with big data and IoT, defined "new" IT on innovation is discussed, by comparing with traditional IT, consisted by technological progress of computers and software developments. Both of new and traditional IT could be understood by using the framework of GPT (general purpose technology), while the organization of new IT innovation can be characterized by emergence of multiple eco-systems, instead of the pattern of platform leadership, found in traditional IT.

Introduction p.

2.

Conceptualization of AI driven innovation p.

2.1. Big data p.

2.2. Artificial Intelligence (AI) p.

2.3. Internet of Things (IoT) p.

2.4. Relations of these new IT p.

3.

Measuring science and innovation of AI p.

4. Discussion p.

Conclusion and implications p.

6. References p.

Introduction

There is no doubt that the importance of scientific knowledge for the industrial innovation process is growing. Genome science is significantly changing the pharmaceutical industry's R&D process, while understanding of the physical properties of materials at the nanoscale level has become essential to the miniaturization of the large scale integration circuit (LSI) fabrication process. Information technology has had a huge social and economic impact, and big data analysis has brought deeper insights for business and management activities. In particular, the advance of data science and AI contributes to scientific understanding of business process, which can be applied to whole economy. Therefore, scientific findings used to be applied to only specific sector, such as pharmaceutical industry, while recent trend of economy wide impact of scientification of innovation can be called as the science economy, instead of science based industries [START_REF] Motohashi | The Sun Also Rises High: Regenerating Japan's industrial competitiveness[END_REF].

As a distance between science and innovation becomes shorter, the relationship between these two activities are shifting from the linear model (scientific knowledge is first gained at research institutions such as universities and then used by companies to develop new products)

to co-occurrence model, where scientific and innovation activities occur simultaneously by interacting with each other. With this regards, we have developed a new indicator of science representing the co-occurrence of science and innovation, based on the linked datasets of patents and research articles by the same author/inventor (Ikeuchi et, al, 2016), as a complementary information to the indicator based on research paper citation of patent, a traditional science linkage indicator based on the linear model.

In this paper, the nature of AI (Artificial Intelligence) driven innovation is analyzed, based on the author/inventor linkage data of US patents and research articles. There is a growing attention to AI, not only in scientific sector such as university, but also in industry. AI is perceived as a key technology causing innovation landscape fundamentally by contributing to make what is coming in the future cheaper and more certain [START_REF] Agrawal | Prediction Machines: The Simple Economics of Artificial Intelligence[END_REF]. Therefore, private incentives to capture such potential values are substantial. Another characteristics of AI is that a potential application of AI can be found in wide area of industries. In other words, AI is a killer application of IT as general purpose technology. Furthermore, AI can be called as IMI (the invention as a method of inventing), in a sense that AI can be used in a process of inventing new applications such as autonomous driving, condition based maintenance and new drug discovery [START_REF] Cockburn | The impact of artificial intelligence on innovation[END_REF].

This paper is organized as follows. The next section described the concept of AI driven innovation. Here the interrelationship between AI and the complementary elements to innovation, big data and IoT is discussed. Then, an analytical part by using the linked dataset of research articles and patents is provided. This section is followed by the discussion, focusing on the difference of AI driven innovation, or "new" IT innovation. Finally, this paper concludes with some managerial and policy implications.

CEAFJP DISCUSSION PAPER #18-04

Conceptualization of AI driven innovation

There has been significant progress in the field of AI, such as the construction of new methods of machine learning (e.g., Deep Learning and Generative Adversarial Networks), but the basis of such technologies, neural network, is not new at all. The progress in developing methods has been made possible due to the improved performance and capabilities in computing and the availability of big data. More specifically, the emergence of big data and developments in AI-based science innovation are inseparable. In addition, the concept of "IoT (Internet of Things) is also important for AI technologies that yield results as industrial applications such as AI speakers and automated driving systems. Here, we will discuss the necessary elements for AI-based innovations such as big data, and IoT, and discuss about mutual complementary relationships of these two items and AI.

Big data

Managerial issues in companies are being solved by data analysis more than ever before.

What is new in utilization of big data? Companies have conventionally used data accompanying a business system. While corporate financial data are accumulated in a financial accounting system, data such as that related to personal histories and employee salaries are accumulated in human resource systems. On the other hand, a supply chain management (SCM) system manages stock status regarding materials and products and order records as data. Such data are respectively characterized as being made for a specific reason. Since the 1990s, each business system has been increasingly integrated as an optimum resource management (ERP:

Enterprise Resource Planning) system for the whole company. Such systems place an emphasis on improving the operational efficiency of corporate activities.

On the other hand, big data is characterized by not being constructed for a specific use.

Amazon is frequently cited as a company that played a pioneering role in use of big data. In this case, one example of big data is users' purchase records. Using this information, Amazon makes book recommendations matching to the interests of individual users. Purchase record data related to merchandise such as books are automatically accumulated by companies that engage in e-commerce. Such data is not specifically collected to make recommendations to customers regarding books they would be highly likely to purchase. In addition, to make highly accurate recommendations, a certain data size is required (including the number of users and the number of purchase histories). This is because the method of making recommendations is calculated based on a stochastic model for estimating what books the customers would be highly likely to purchase based on the characteristics of their purchase histories. As the number of samples increases, the accuracy of recommendation improves. In other words, the value can be said to vary depending on the data size [START_REF] Mayer-Schonberger | Big Data: A revolution that will transform how we live, work and think[END_REF].

Another leading company in utilizing the big data innovation model is Google, which utilizes search histories as a source of big data. Google's business model involves searching keyword specific advertisement, using their search engine. Data that has been used in IT systems thus far has primarily been numerical data. However, on the internet, there are various forms of data such as audio and visual data, as well as text data. Big data is also characterized by the difference from conventional IT use in the value added by converting data therefrom (Datafication).

CEAFJP DISCUSSION PAPER #18-04

Furthermore, these days, in addition to the development and use of the internet, data is also collected using various kinds of sensor information to add economic value. For example, Komatsu has had its construction machinery equipped with GPS systems and fuel gauges to collect location data and operation status data from their construction machinery. Using such data, they can provide services such as anti-theft functionality and make recommendations for fuel cost savings, which will assist in differentiating their products from those of their competitors.

The sensors for this type of data collection collect data from various places, such as people's travel data from cell phones and onboard GPS systems, for example, as well as the operational status of all types of industrial equipment. The use of such sensors is being actively developed (Kinukawa et. al, 2014).

As described above, with e-commerce purchase records, online information, and various information obtained from sensors in addition to the data produced from business systems within companies, various types of data are available. Big data are characterized by the "3Vs" (i.e., their large data size (Volume), datafication of various information including text, images, and audio (Variety), and the continuous daily inflow of data from the internet and sensors (Velocity)). In addition, as a method of data utilization, it is possible to see various events related to business management, society, and human behavior in a wider and more comprehensive manner, and analyze the mechanisms in detail at a micro level.

Artificial Intelligence (AI)

AI is a concept in which technologies are integrated to exploit beneficial information in business management from big data. Applied AI technologies may be categorized into image or text data recognition technologies (conversion into computer readable data), human interfaces (visualization of data and interactive agents), knowledge discovering technologies related to the diagnosis, monitoring, and datamining of various types of equipment devices, and so on.

In addition, machine learning, fuzzy control, and mathematical models such as genetic models have been implemented on systems as basic technologies to realize those functions.

In particular, due to the existence of big data, significant technological progress has been made in machine learning methodology. Model estimation methods for machine learning are generally grouped into supervised learning and unsupervised learning. In either method, the 3Vs of big data are important. In supervised learning, a large volume of the text and image data accumulated on the internet can be used as training data. For example, Google provides translation services by having their translation system read a large volume of documents written in two or more languages (training data) to construct translation models. Conventional machine translation systems utilize rule-based models, which is based on grammatical structure of sentences and word dictionary. On the other hand, in the models that use machine learning, computers produce rules for translation from a large volume of documents (corresponding documents between English and Japanese for Japanese-English translation, for example), which are provided as inputs. In other words, computers automatically perform language parsing work, which is the basis for translation rules in place of the one developed by linguists.

In this case, since human thinking is replaced by a computer, we can consider this to also be one example of AI.

CEAFJP DISCUSSION PAPER #18-04

In the field of image recognition, the news that "A computer has recognized a cat." quickly circulated around the world based on a paper presented by Google in 2012. When they performed machine learning by randomly extracting 10 million images from videos uploaded on YouTube, the computer was able to automatically select images containing the features of cats. In this case, rather than a collection of images of cats being given to a computer as training data, this is characterized by the fact that only 10 million images were given as inputs to conduct unsupervised learning. To prepare training data, it is necessary to use human recognition ability. Therefore, in the supervised model of machine learning, the model is developed through cooperation between humans and computers. However, Google's unsupervised image recognition model is revolutionary since the model was developed without human input.

Machine learning models for automatic translation and image recognition are constructed using the deep learning method. Deep learning is a method of machine learning that uses a multi-layered neural network. A neural network is a classical mathematical method with decades of history. There have conventional ideas of deep learning to make a multilayered network layer;

however, there was a problem in that it was difficult to estimate parameters, which increase through the creation of a multilayered network. In addition, the abilities and performance of a computer are insufficient. In recent years, deep learning has been re-examined, and AI studies are now hot spots since computer performance has been improved, and a large volume of information has been compiled on the internet, which enables big data to be utilized when estimating models. In recent years, estimation methods have been developed for respective types of data (e.g., image data or text data) and characters, and implemented in various fields, including industrial applications such as industrial robots and autonomous operation technologies, investment decision-making for financial institutions, financial advisory work, and household appliances such as cleaning robots and AI speakers

Internet of Things (IoT)

The sensor information for Komatsu's construction machinery described in the big data example is data originating from an object rather than people. Information on physical things has been exchanged over the internet, which is called the IoT (Internet of Things). Those things used to exchange information include electronic devices, automobiles, production equipment, household appliances, and industrial machinery. The point of IoT is that, once various equipment and devices are connected on the internet, opportunities for new business innovations spread.

The realization of IoT requires elements such as the identification of each item with an ID like an IP address (Identification), measurement and datafication of the item (Sensing), data communication (Communication), data analysis related to the item (Computation), and implementation as a specific service (e.g., maintenance and operation of industrial machinery and energy management systems for buildings) (Al-Faqaha et al., 2015). Big data is a concept focusing on the data of things exchanged over the internet, and AI can be considered to be a key component of technologies used for the aforementioned data analysis. Accordingly, the concept IoT can be considered to be a more comprehensive concept including those technical elements and, furthermore, including implementation in society as services.

It has been demonstrated that IoT can provide various solutions with information via the internet by connecting various things respectively through a sensor network. It is believed that CEAFJP DISCUSSION PAPER #18-04 one trillion things (100 times the human population) could be connected by the year 2020. As networking expands from people to "things," the data volume will also dramatically increase. It is not realistic to exchange all information through the internet, so edge computing, which forms local networks and performs distributed processing, has also attracted attention. Here, through the aggregation of a certain level of information from those local networks and the connection of "things" in wider areas through the internet, even more expanded applications can be enabled.

As a result, the world will become a place where information of all kinds will be connected via the internet.

The network is comprised of systems, including wide-area networks such as mobile nets, local area networks, various hardware including devices that are to be the cores of those communication devices, and software. Furthermore, regarding communications infrastructure, there is a service layer for each device, such as automobiles, household appliances, and industrial machinery. Innovations using AI are primarily advancing in terms of this service layer.

While the technical standardization of communications infrastructures has been advanced

by standardization organizations such as ESTI and IEC, the service layer has primarily been developed through the activities of a consortium of private companies (collective entities formed through collaboration among companies). For example, in the field of household appliances, Apple Computers has created the HomeKit standard and engages in collaborative activities with manufacturers related to home automation and network devices. Such activities are based on the idea of using their product, the company's iPhone, as a control hub for illumination at home, for management of home appliances, and as a home security system.

For industrial machinery such as aircraft jet engines and power-generating gas turbines, manufacturers are the ones that are actively engage in such activities. Among them, the General Electric Company (GE) launched GE-Data as a cross-sectional organization for their business departments such as jet engines, energy (wind power generator), healthcare, and railroad systems, and develops and provides a platform for an IoT system, which is called "PREDIX". The company also started up the IIC (Industrial Internet Consortium) with information communication companies such as IBM, Cisco, Intel, and ATT, and has developed activities to expand their IoT platform to fields other than their business fields.

Relations of these new IT

AI and IoT, as well as big data as discussed above, are in a mutually complimentary relationship. In addition to the huge volume of information (image data, text data, etc.) that has originated from humans and been compiled on the internet, data originating from things due to the advancement of IoT sensor network technology has also been compiled. This has markedly enhanced the 3Vs of big data (i.e., Volume, Velocity, and Variety). For example, the data volume per day generated during the production process at a factory can be calculated as (number of products in a production line) * (number of production steps) * (data granularity).

Here, data granularity refers to the data acquisition frequency per unit of time (e.g., per minute).

Accordingly, the IoT service produces data with Volume and Velocity, exceeding the physical capabilities of humans. In addition, from surveillance cameras for security services and selfdriving cars, various data are generated such as audio and image data.

CEAFJP DISCUSSION PAPER #18-04

As previously mentioned, the expansion of potential uses for such big data has contributed significantly to the development of different varieties of AI technology. By gathering various types of data that are not suitable for information processing as is, such as audio, image, and text data, perceptual and recognition technologies, including audio/image recognition and natural language processing, have been improved. In addition, by using a large volume of data, there have been remarkable advancements in various AI-related basic technologies for knowledge/ discovery techniques and deep learning, such as information searching and data mining. More specifically, it will not only make routine tasks more efficient, which is the strength of IT, but also enable the performance of non-routine applications. For example, for IT applications in corporate finance/accounting and human resources, the primary tasks of their IT systems typically involve financial/accounting computations and human resource databases. However, in the future, such finance/accounting computations may be able to be performed according to revised accounting standards and a recommendation system for personnel allocation according to characteristics of positions in the companies. Moreover, in sales departments, IT has been increasingly used in customer information management. In this case, with the development of new customers and determination of customer segments, IT would support more strategic marketing activities. Furthermore, efforts made for IoT services such as smart household appliances and smart cities can be considered to be the sprouts of service innovations beyond CEAFJP DISCUSSION PAPER #18-04 existing business models. Upon improving productivity (output per unit input), conventional IT is often considered to be a tool for reducing input (making the existing work more efficient).

However, with technological advancements in big data AI and IoT, IT should be recognized as an enabling technology for expanding output.

Next, it will enable us to grow beyond the business-by-business structure, and realize innovation involving a wide range of players, who can work across industries. Although it is still in the empirical study stage, smart cities represent a typical example, toward which automobile, home appliance manufacturers, and power companies, etc., are jointly working. Examples of practical application include the Nest thermostat, which learns the living patterns of residents and automatically controls the ON/OFF functionality of household appliances/equipment, as well as Google's Waymo self-driving car project. Accordingly, IoT services can be achieved by mutually connecting "things" that currently exist independently. Therefore, manufacturing companies should work together with IT companies, and venture companies that specialize in specific technical fields such as artificial intelligence technology. As a result, it will bring new innovations such as those described above, and may also destroy the conventional manufactures' business models, in which profits are made by producing and selling things. Through household appliances, such as the Nest thermostat, being connected to a controller, the manufactures of those household appliances will lose contact points with their end customers. In other words, those manufacturers' statuses will fall to that of suppliers of smart household appliances services (i.e., some parts to a whole system). The status of automobile manufacturers will be the same in regard to becoming a transportation service due to self-driving cars. It is highly likely that the IoT wave will have a significant impact on the business structures of manufacturers that handle "things".

Measuring science and innovation of AI

In this section, I would provide some statistical evidences of interactions between science and innovation, focusing on AI. It is clear that new way of using big data, such as deep learning, changes the way that business innovation is organized. In addition, deep learning techniques is used for a tool to invent new products or innovation, as is seen in the case of IBM Watson's application to new drug development [START_REF] Nayak | Exploring Data-Driven Innovation as a New Source of Growth: Mapping the Policy Issues Raised by Big Data[END_REF]. In this sense, AI can serve not only for new business applications but as an invention for method of invention [START_REF] Cockburn | The impact of artificial intelligence on innovation[END_REF].

Another special feature of AI is found in the style of its development, where public science sectors are involved in such development. For example, the idea of deep learning (deep neural network) is not new, but actual implementation with new methodology and powerful computer powers was made by academia. Subsequently, a series of development of deep learning algorithms, suited for various kinds of datasets (such as CNN for image data and RNN for text data) are developed by computer science scholars. In addition, a substantial contribution to AI development by private sector is also found. A typical case example is Google Brain's publication of "alpha go" [START_REF] Silver | Does 'Driving Range' Really Matter? The Hidden Cost of Internal Combustion Engine Vehicles and the Potentially New Value Proposition of Electric Vehicles: Two Cases from Countryside and Urban Side of Japan[END_REF]. A team at Deep Mind, currently under the Google AI department (Google Brain), not only developed the software to beat the world go (Chinese chess) champion, but also made it in a public as a research paper. At the same time, innovations, i.e., economic valuation of such public technology, are flourishing as is seen in the previous

CEAFJP DISCUSSION PAPER #18-04

chapter for IoT applications (smart XXX), as well as mushrooming of startup firms providing a specific AI technology with fee. Due to a tremendous speed of technological progress and business development, the public sector and the private sector are co-mingled in this process.

Therefore, AI is a typical example of science based economy, in a sense that scientific findings (published for free) and innovation (commercial activities) are co-occurring, by the cross over style of public sector such as universities and public research institutions and private sector formed by firms.

In this paper, I take the methodology of linking research article and patent, more specifically, finding identical author/inventor in the database of research articles and patents (Ikeuchi et. al, 2016), close to the idea of finding the paper patent pair with the similar contents [START_REF] Lissoni | Inventorship and authorship as attribution rights: an enquiry into the economics of scientific credit[END_REF]. Traditionally, the degree of scientific basis, or 'science intensity' of industry has been measured using non-patent literature (research article) citations made by patents [START_REF] Narin | Is technology becoming science?[END_REF]Noma 1985, Schmoch 1997). Non-patent literature citations show the degree of disembodied scientific knowledge that flows into patents, while the patent-publication pair can capture the state of co-occurrence of scientific and invention activities within the same researchers, i.e., interplay of science and technology embodied in human capital.

I use the SCOPUS data from Elsevier for research articles and US patent data from USPTO for patents. In both datasets, I select researchers working for the organizations located in the United States. There are around 8 million papers from SCOPUS and 3 million patents from USPTO data. These two data are linked by author/inventor names as well as his/her affiliates, and about 5% of all authors from SCOPUS and about 13.3% of inventors from USPTO data can be linked [START_REF] Motohashi | Co-occurrence of science and innovation in AI : Empirical analysis of paperpatent linked dataset in the United States[END_REF].

From this linked dataset of research articles and patents, AI papers and patents are identified. For AI papers, ASJC (All Science Journal Classification), provided by Elsevier, is used. Concretely, ASJC 1702 is labeled as "Artificial Intelligence" under a broad category of "computer science", which is used in this paper as AI paper identification. In terms of patents, IPC code, G06N is used, along the line of JPO publication of technology trend survey (JPO 2014). G06N stands for the technology of "computer systems based on specific computational models, such as neural network, inference machine and fuzzy logic". This definition narrowly defines the concept of AI, in a sense that only basic methodologies for analytics and models are included. 1

Figure 2 shows the trend of the shares of AI papers and patents to the all. Both of them have a upward trend until 2010, but stable afterwards. It should be noted USPTO discloses the information of only granted patents. Therefore, only data applied until 2011 can be used even the datasets are obtained from USPTO data download site, called patentview.org in 2016. In Figure 1, the trend of AI patent applications, whose truncation bias is relatively small, are also presented. The surge of AI patent applications after 2010 is found. It should be also noted that the shares of AI papers and patents are very small, like less than 1% of total papers and patents, since only core technology of AI is included in both definitions.

1. Another approach is taking a broad category such as G07F, including general purpose computer software and applications (OECD, 2013)

CEAFJP DISCUSSION PAPER #18-04 Next, I break down author/inventor by her affiliation, private sector (for profit firm) or public one (non profit research organization such as universities and government laboratories). Figure 2 shows the share of private paper authors. The majority of paper authors are affiliated to public sector, so that the private shares for all papers are less than 10%. In addition, the pattern of private sector's retreating from basic science for private sector is found by its decreasing trend [START_REF] Arora | Killing the Golden Goose? The Decline of Science in Corporate R&D[END_REF]. The share of private publications of AI papers is almost same as that of all papers, suggesting that the public sector is a major contributor to basic science of AI. When we look at the papers written by the author who also has some patents as an inventor (an author/ inventor researcher), the share of private sector becomes higher (around 15% for all discipline), since the patenting activities are driven by private sector. It should be noted that the share of private sector for AI papers by an author/inventor researcher is not declined over time, while the private share of AI papers decreases. Therefore, more and more firm researchers are involved with both publication and patenting activities.

Figure 4 shows the similar trend from the patent data side, but here the share of public patenting, instead of the private share, is displayed. Since patenting activities are driven by private sector, the share of public sector patenting for all technology fields is around 1.5%. The public share of AI patents is higher, such as around 3.0%, suggesting more academic patenting is found in this technology field. This looks natural, since we take the definition of AI as a basic methodology for models and analytics. It is found that the share of public patenting by those who is an author of research articles is decreasing for both all fields and AI. It should be also noted that the public share of author/inventor patents for AI is lower than that of that of all AI patents, even though the public sector is supposed to be a major contributor to publication.

These findings is consistent with Figure 3, suggesting that private researchers become to be involved with both publication and patenting activities. Table 1 provides the share of AI patents/papers by type of researcher affiliation and by author/inventor or not (only author or only inventor). This table reflects the relative importance of researchers by types above for AI patents and papers, normalized by general trends of patents and papers over our observation period. It should be noted that the researchers can be categorized into three types, (1) inventor only, (2) both inventor and author and (3) author only. In terms of private sector, the share of AI patents are higher for the category 1 (0.15%) , as compared to the category 2 (0.11%). When we look at the AI papers share, the larger number is found in category 2 (0.91%), as compared to the category 3 (0.55%). Therefore, inventor only researchers (category 3) is the most significant contributor to AI related outputs for private sector. The same exercise for the public sector reveals that the category 2 researchers (author/ inventor) is the highest, as compared to the category 1 (inventor only) and the category 3 (author only). Finally, the impact of AI publication and patenting to other fields is investigated. Figure 5 shows the share of patents by technology field by AI inventors and AI authors. The category of "computer technology", including G06N, is excluded from this chart, to see how AI technology is used widely across other technologies.2 It should be noted that the patents invented by AI authors have wider applications across technology field, as compared to AI inventors. The difference between two figures are found particularly in "measurement", "medical technology", "transport" and "organic fine chemistry". These findings reflect that the publication of AI plays an important role in its wide applications by various technology fields.

CEAFJP DISCUSSION PAPER #18-04

Discussion

The innovation that information technology brings is seen across business sectors and in various business areas. The application also extends from a system in supporting functions at firm such as personnel and financial accounting to a wide range of operations including production management, customer management and supply chain management.

Artificial intelligence is one of the core technologies that supports applications of these various information technologies. Characteristics as general purpose technology (GPT: General Purpose Technology) of such information technology has been analyzed by economists over 20 years Bresnahan and Greenstein, 1996;Helpmann, 1998). For example, backbone systems related to factory operations in the manufacturing industry and banking financial transactions are totally different tasks performed by the information system. However, since improvement of computer performance can realize more advanced tasks in both, progress of information technology that is geometrically progressed by Moore's Law will bring great benefit to the entire economy.

(
The new methodology in computer modeling and analytics, called AI, obviously brings new possibilities for business applications, but what is new as compared to information technology in general?

First, let us review the literature discussing about the GPT feature of information technology. The General Purpose Technology is characterized as its "speed", "pervasiveness"

and "downside applications" (Helpmann, 1998). A driver of speed of IT is Moore's law in CEAFJP DISCUSSION PAPER #18-04

semiconductor, and development of operation and application software makes pervasive use of computer in various industries possible. However, IT investment does not leads to improvement of business performance and productivity automatically. It requires organizational innovations, such as business process changes (Bresnahan et. al, 2002). Therefore, IT is just an "enabling"

technology to fruit as innovation that is applied to business and bring about management effect.

For example, innovations using general-purpose technology is achieved by a manufacturing user's application to highly controlled production process in its factory and by a bank to use biometric authentication system to improve in security in ATM transactions. Basically, the "new IT", such as AI, Big data and IoT, discussed in the section 2, is on the extension of information technology which is a general purpose technology. In other words, these are the enablers for realizing innovation, not innovation itself. In order to realize innovation, business innovation is needed to assemble it into activities with business value on the user side.

However, there are some differences in its innovation organizations. In traditional IT (left panel of Figure 6), the key technology to link computer technology to user innovation is the bundle of software, and the operation software plays a particularly important role to make various application software developments possible. The propriety operations software constitutes a platform, and the upstream platform leaders, for example, Intel and Microsoft in the case of PC, drive a whole system of innovation [START_REF] Gawer | Industry Platforms and Ecosystem Innovation[END_REF]. As a results, the division of innovative labor is found between platform leaders and downstream user innovation players [START_REF] Gambardella | Business-model innovation: General purpose technologies and their implications for industry structure[END_REF]. Off course, there are some firms acting like a platformer, such as Google and Amazon.

However, while Google dominates public internet information, an expansion of IoT makes it difficult for them to play as a platform leader. In IoT business innovation, the critical component is domain specific propriety data, such as driving record for autonomous driving, and the domain expands to variety of field, like consumer electronics, medical treatment, smart factory, financial services etc. In this sense, the value of user innovation comes from inter-connections of open and propriety technology, data and business know-hows. As a whole structure of innovation becomes complex, various eco-systems, instead of platform business, are created as ventures to fill the gaps emerged among incumbent firms' capabilities.

Conclusion and implications

In this paper, AI driven innovation is analyzed. The linked dataset of AI research articles and patents reveals that substantial contribution of public sector is found for AI development.

In addition, the role of researchers who are involved both in publication and patent activities, I would conclude this paper by providing some implications. As for management implications, the ecosystem strategy should be emphasized. The business ecosystem consists of "keystone" which plays a central role in the ecosystem (business inter-company relation network) and other niche players [START_REF] Iansiti | The Keystone Advantage: What the New Dynamics of Business Ecosystems Mean for Strategy, Innovation, and Sustainability[END_REF]. The role of "keystone" is to attract many niche players and to spread the whole ecosystem. On the other hand, "niche players" are with each original technology, contributing to the diversity of the entire ecosystem. With this mutually complementary relationship, the ecosystem holds. For example, the Apple computer has an up store on the iphone and is developing a service that meets the diverse needs of consumer needs. Combined with the common platform (platform) of various services provided by individual application providers (niche players) and Apple computers (Keystone), they will have overall value. Keystone's role is to improve the business value of the whole ecosystem, and it is important to build a win-win relationship with niche players. Strengthening control over niche players and continuing exploitation of value will ultimately destroy the ecosystem. In order to increase the value of the entire ecosystem, in order to attract diverse niche players, it is necessary to make attractive management resources for niche players.

Along this line, the most important strategic question is which way, key stone or niche, to go in new IT ecosystem. A New IT user tends to be a niche player, based on its own business knowledge and propriety data. However, a user firm can be a key stone player, by providing CEAFJP DISCUSSION PAPER #18-04 common resources to other firms within a certain business domain. For example, GE chooses the key stone strategy for IoT business in manufacturing industry, by offering PREDIX platform which serves as a basis of IoT industrial applications such as condition based maintenance of large industrial equipment. An IT technology firm typically seeks for the key stone strategy, instead of platformers, by building up win-win relationships with niche players and nurturing an ecosystem as a whole.

A policy maker should be aware of the interplay between open science and propriety technology in AI field. Therefore, a policy support to AI innovation is not just providing a fund to basic science, but a cross over model of public and private sector should be investigated. For example, promoting university startup is more suitable, as compared to the policy instruments based on traditional linear model, such as licensing of university patents. In addition, a non-profit public research institution can be a place where new eco-system development, since such body cannot be a direct commercial competitor to private firms seeking for mutual collaborations.

In this regards, the role of university should be expanded from the place for research and education, to the place for entrepreneurial activities and innovation experimentations.

Figure 1 :

 1 Figure 1: Inter-relationships of AI, IoT and Bigdata

Figure 2 :Figure 3 :

 23 Figure 2: Share of AI papers and patents

Figure 4 :

 4 Figure 4: Share of public sector inventors

Figure 5 :

 5 Figure 5: AI author and inventor patents by technology (except for computer technology)

Figure 6 :

 6 Figure 6 : Traditonal IT vs AI/BD/IoT

  particularly in private sector, increased over time. This is, open science, publicly available by research articles and propriety technology, protected by patents are intertwined in AI development. In addition, the impact of AI, combined with big data and IoT, defined "new" IT on innovation is discussed, by comparing with traditional IT, consisted by technological progress of computers and software developments. Both of new and traditional IT could be understood by using the framework of GPT (general purpose technology), while the organization of new IT innovation can be characterized by emergence of multiple eco-systems, instead of the pattern of platform leadership, found in traditional IT.

Table 1 : Share of AI patents and papers by type

 1

The share of computer technology is 53.2% for AI inventors and 41.6% for AI authors.


Jean-Charles Pillet
email: jean-charles.pillet@grenoble-em.com

Kevin Carillo
email: k.carillo@tbs-education.fr

Federico Pigni
email: federico.pigni@grenoble-em.com

Claudio Vitari
email: vitari.iae@univ-paris1.fr

Measuring Perceived IT Ambiguity Perceived IT Ambiguity: Development of a Measurement Instrument Emergent Research Forum (ERF)

Keywords: Survey instrument development, technology adoption, theory of planned behavior, consumer, social media, ambiguity

Information technologies (IT) have reached such degrees of functional richness that forming a complete, coherent, and stable understanding of a given IT product may be challenging for some users. The need to theorize this phenomena and to measure its effect on IT adoption empirically is rife. This paper introduces the construct of perceived IT ambiguity (PITA), which captures the extent to which a user has difficulties making sense of an IT artifact. A multi-item measurement scale is developed and its validity and reliability pre-tested on a pilot sample. The effect of the focal variable on technology adoption is tested using covariance-based SEM. Preliminary results indicate that ambiguity is a double-edged sword that simultaneously boosts and impede IT adoption.

Introduction

Information technologies (IT) that are intentionally incomplete and constantly in the making are compelling alternatives to traditional technologies that have clearly delineated and stable boundaries [START_REF] Garud | Incomplete by design and designing for incompleteness[END_REF]. Indeed, having no fixed functional limits makes them particularly suited to an environment that requires dynamic adaption and constant innovation [START_REF] Avital | From generative fit to generative capacity: Exploring an emerging dimension of information systems design and task performance[END_REF]. Although there may be more apparent motives than ever before to adopt these technologies [START_REF] Lowry | Proposing the Multimotive Information Systems Continuance Model ( MISC ) to Better Explain End-User System Evaluations and Continuance Intentions[END_REF], their versatility can directly hinder the ability of users to understand them fully. In a Jan. 2017 article1 , Quartz questioned the very purpose of the iPad seven years after its launch, concluding that "The iPad seems stuck in the same existential quandary about what it actually is that it did when it was first released." Similarly, developing a clear and stable understanding of software technology (e.g. social media, open platforms, enterprise 2.0 technology, etc.) that are constantly in the making is a challenge. The measurement instrument we present in this paper aims at capturing the extent with which an individual user has difficulties making sense of an IT, and assessing the impact of ambiguity on user's beliefs and attitudes.

In this research in progress paper, we introduce the construct of perceived IT ambiguity and its conceptual roots. We then explain what steps we followed to develop the measures and to verify that they tap into the appropriate conceptual domain. Finally, we present the results of a pilot study in which 192 individuals were asked about their beliefs and use of the Facebook online application.

Conceptual Background

A fundamental observation from the discipline of psychology is that concepts function as filters through which we make sense of the external world [START_REF] Goldstone | Concepts and Categorization[END_REF][START_REF] Medin | Concepts and Categorization[END_REF][START_REF] Smith | Categories and concepts[END_REF]. A central tenet of this perspective is that individuals naturally divide the world of objects into categories to make their environment intelligible. In the case of man-made objects such as technology, having a clear understanding of its function is essential [START_REF] Smith | Categories and concepts[END_REF]. When an IT has a relatively univocal function, it fits nicely in the individual's knowledge structure, and is thus perceived clearly. On the contrary, when an IT lacks clearly bounded and stable functional attributes, it is perceived as ambiguous, leading to a "what is it?" type of questions [START_REF] Moreau | What is it?" categorization flexibility and consumers' responses to really new products[END_REF]. Ultimately, ambiguity has to relate with the high-level cognitive processing involved in the development of meanings [START_REF] Winkielman | The hedonic marking of processing fluency: Implications for evaluative judgment[END_REF]. Given that purposively incomplete technologies are characterized by imprecise and evolving functional boundaries, we expect that users are likely to perceive them as ambiguous to varying degrees.

Measurement Development

To develop the measures that would tap into this conceptual domain, we followed the development method proposed by [START_REF] Mackenzie | Construct measurement and validation procedures in MIS and behavioral research: Integrating new and existing techniques[END_REF]. We started by developing a pool of items that is about five times larger than the target scale, including numerous redundant items [START_REF] Devellis | Scale Development: Theory and Applications[END_REF]. The items were presented to 28 individuals (13 faculty members and 15 master students) who agreed to provide feedback on their wording. This led to either deletion or adjustment of the initial list of items. Throughout the item development phase, particular attention was paid to item bias (i.e. ambiguity, leading items, social desirable items) because of the artefactual covariance it might create in the final results [START_REF] Podsakoff | Common method biases in behavioral research: a critical review of the literature and recommended remedies[END_REF] . We conducted a content validity assessment using [START_REF] Hinkin | An Analysis of Variance Approach to Content Validation[END_REF]) procedure, as recommended (MacKenzie et al., 2011). The study participants evaluated the extent to which a given item is representative of each dimension of the construct. The results of the repeated ANOVA (N = 191) helped identify the candidate items that are consistent with their posited domain.

For this study, we used items that aim to capture the overall understanding of an IT by a user rather than specific dimensions of ambiguity: "Generally speaking, Facebook does not make clear sense to me." (PITA_1), "In general, it is not clear what this device is and is not." (PITA_2), "Generally speaking, I find it hard to define Facebook." (PITA_3), "Overall, I find it difficult to make sense of Facebook." (PITA_4), and "All in all, Facebook is hard to fully comprehend." (PITA_5).

Nomological Network and Data Collection Method

Our theoretical model is rooted in the literature on attitudes and behavior [START_REF] Fishbein | Belief, attitude, intention and behavior: an introduction to theory and research[END_REF], and the associated attitudinal models developed in IS [START_REF] Davis | Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology[END_REF][START_REF] Venkatesh | User Acceptance of Information Technology: Toward a Unified View[END_REF][START_REF] Venkatesh | Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology[END_REF]. Indeed, there are extensive evidence that the perception of ambiguity exerts effects on attitudes and judgments. However, these effects are seemingly contradictory. On the one hand, previous research suggests that individuals are likely to negatively evaluate ambiguous objects [START_REF] Noseworthy | Looks Interesting, but What Does It Do? Evaluation of Incongruent Product Form Depends on Positioning[END_REF][START_REF] Uekermann | The Influence of Stimulus Ambiguity on Category and Attitude Formation[END_REF]. This stems from the fact that individuals may have a negative disposition towards things that they do not clearly understand [START_REF] Cacioppo | Central and peripheral routes to persuasion: An individual difference perspective[END_REF], or to ambiguous situations in general [START_REF] Frenkel-Brunswik | Intolerance of ambiguity as an emotional and perceptual personality variable[END_REF][START_REF] Furnham | Tolerance of ambiguity: A review of the concept, its measurement and applications[END_REF]. On the other hand, numerous examples of successful ambiguous IT can be found. A seminal study from [START_REF] Meyers-Levy | Schema congruity as a basis for product evaluation[END_REF] indicates that moderately ambiguous products are more favorably evaluated than non-ambiguous products. Indeed, it appears that ambiguity can signal novelty, leading to more favorable judgments [START_REF] Goode | Innovation aesthetics: The relationship between category cues, categorization certainty, and newness perceptions[END_REF]. Taken together, these results indicate that perceived ambiguity might be a double-edge sword: it can lead to more positive evaluations when present in reasonable proportion, but can also backfire and lead to negative evaluations when it is excessively present.

Our study seeks to tease out these simultaneous mechanisms in the context of IT adoption and use (i.e., TAM or UTAUT models). To test the nomological validity of Perceived IT Ambiguity (PITA), our model includes three other constructs (Usage, Effort Expectancy and Performance Expectancy) from seminal research on consumer technology adoption [START_REF] Venkatesh | Predicting different conceptualizations of system use: the competing roles of behavioral intention, facilitating conditions, and behavioral expectation[END_REF][START_REF] Venkatesh | Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology[END_REF] and three control variables (age, gender and education level). All variables except the control variables are measured using a 5-points Likert scale. We sampled 192 individuals aged between 18 and 73 (M = 36) to collect their view of the Facebook online application. All the participants (70% female) were paid and recruited via the crowdsourcing platform Prolific Academic. Our model is tested using covariance-based SEM in Stata v. 14.2. In the model assessment process, we first analyze the measurement model and then the structural relationships among constructs [START_REF] Anderson | Structural equation modeling in practice: A review and recommended two-step approach[END_REF].

Findings

Assessment of the Measurement Model

Problematic indicators were eliminated on the basis of their relationships with the posited latent construct. A common rule of thumb is to retain the items which loadings are greater than 0.707 [START_REF] Straub | Validation Guidelines for Is Positivist[END_REF]. This analysis led to the deletion of one item for performance expectancy (λ = .67), and the deletion of the indicator of intensity of usage (λ = .56). Table 1 [START_REF] Nunnally | Psychometric theory[END_REF]. All constructs' Average Extracted Variance (AVE) were highly above 0.5 indicating good levels of construct convergent validity [START_REF] Fornell | Evaluating structural equation models with unobservable variables and measurement error[END_REF]. The assessment of construct discriminant validity did not raise any concern since all the item loadings were higher in their respective construct than with any of the other constructs [START_REF] Campbell | Convergent and discriminant validation by the multitrait-multimethod matrix[END_REF]. Meanwhile, the square root of the AVE of each construct was found to be greater than the correlations of the construct with the other constructs [START_REF] Fornell | Evaluating structural equation models with unobservable variables and measurement error[END_REF].

Assessment of the Structural Model

We specify a recursive structural model in which PITA is modeled as an antecedent of both PE and EE, who are themselves predictors of USE. In order to test whether the effect of PITA on use is partially or fully mediated, we also modeled a direct path from PITA to USE.

In the model, we allowed the error terms of PE and EE to covary. After controlling for the relevant variables, the resulting model's fit indices are close from the cutoff values for fit indices in confirmatory factor analysis using a maximum likelihood estimation algorithm (RMSEA ≤ .06; CFI ≥ .95; TLI ≥ .95; SRMR ≤ .08), indicating that the latent model fits the data moderately well [START_REF] Hu | Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives[END_REF]) (Table 4).

Table 4. Model Fit Indices Table 5. Structural Model (direct and indirect effects)

The research model is analyzed to simultaneously assess direct and indirect effects of perceived IT ambiguity on the use of Facebook. An analysis of the indirect effects of PITA on USE reveals that perceived IT ambiguity influences usage through its effect on beliefs about whether Facebook is useful and easy to use (β=-.29, p=.010). This indirect effect is a combination of the specific indirect effect exerted trough the belief that Facebook is easy to use (-.41*.65=-.27) and, to a much lesser extent, via the belief that Facebook is useful (-.13*.17=-.02). We also found that, all other things being held constant, PITA has a significant and positive direct influence on the adoption of Facebook (β=.18, p=.010). These results suggest that although perceived IT ambiguity has an adverse impact on adoption because it weakens the belief that the IT is useful and easy to use, it positively affects IT use when these negative effects are controlled (a potential case of competitive mediation [START_REF] Zhao | Reconsidering Baron and Kenny: Myths and Truths about Mediation Analysis[END_REF].

Conclusion

In this paper, we provide preliminary empirical evidence that the concept of perceived IT ambiguity is an important mechanism in the adoption of IT, especially those that are designed for incompleteness [START_REF] Garud | Incomplete by design and designing for incompleteness[END_REF]. The early results show that perceived IT ambiguity may be modeled as an antecedent of effort and performance expectancy [START_REF] Venkatesh | User Acceptance of Information Technology: Toward a Unified View[END_REF], and exerts an indirect effect on use through these beliefs.

However, these early results must be interpreted with the caution necessary when conducting mediation analysis using latent variables without correcting the confidence intervals with bootstrapping [START_REF] Mackinnon | A comparison of methods to test mediation and other intervening variable effects[END_REF][START_REF] Mackinnon | Confidence limits for the indirect effect: Distribution of the product and resampling methods[END_REF].

Instances of technology use in which ambiguity is unlikely to manifest with adequate variability across users are limited to specific tools that offer a narrowly delineated, unique, and consistent user experience. Very specific software applications may fit into this definition, such as games with simple rules (e.g. Solitaire card game) or weather forecast apps. However, some have argued that incompleteness -and by extension ambiguity -is constitutive of digital artifacts (e.g., [START_REF] Kallinikos | The Ambivalent Ontology of Digital Artifacts[END_REF], and the construct may have wide-ranging applications in both hardware and software contexts of use.

Future research would seek to expand the nomological network of the construct using insights from cognitive psychology. For example, differences in cognitive style can explain why some individuals are more inclined towards ambiguous situations than others [START_REF] Frenkel-Brunswik | Intolerance of ambiguity as an emotional and perceptual personality variable[END_REF][START_REF] Furnham | Tolerance of ambiguity: A review of the concept, its measurement and applications[END_REF]. Individual tolerance to ambiguity may exert a conditional effect on the process of adopting IT with blurry functional boundaries. Besides, perception of ambiguity has been associated with attitude strength [START_REF] Krosnick | Attitude strength: One construct or many related constructs[END_REF][START_REF] Petrocelli | Unpacking Attitude Certainty : Attitude Clarity and Attitude Correctness[END_REF]. We can expect that users that are unsure about their understanding of a given IT are likely to experience greater levels of attitudinal uncertainties.

Table 1 . Measurement Model (after purification)

 1 provides evidence of the reliability of the measurement model. Both Cronbach's Alpha and Composite Reliability coefficients are above the recommended 0.70 threshold, which indicates satisfactory internal reliability of the instrument

	Constructs	Cronbach's Alpha	Composite reliability	Average Variance Extracted (AVE)
	Perceived IT Ambiguity (PITA)	0.952	0.953	0.802
	Effort Expectancy (EE)	0.885	0.888	0.665
	Performance Expectancy (PE)	0.910	0.912	0.777
	Use (USE)	0.900	0.905	0.763

https://qz.com/896561/seven-years-after-its-launch-its-still-not-entirely-clear-what-an-ipadis-for/ 1 Twenty-fourth Americas Conference on Information Systems, New Orleans

, 2018

Twenty-fourth Americas Conference on Information Systems, New Orleans, 2018