Suljettujen online-mainosalustojen strategiat — tapaukset Google ja Facebook

This thesis studies closed ad platforms in the modern online advertising industry. The research in the field is still nascent and the concept of a closed ad platform doesn’t exist. The objective of the research was to discover the main factors determining the revenue of online advertising platforms and to understand why some publishers choose to establish their own closed ad platforms instead of selling their inventory for third-party ad platforms. The concept of a closed ad platform is defined leveraging the existing online advertising literature and the platform governance structure theory. Using the case study method, Google and Facebook were chosen as the cases as they have driven most of the innovation in the field and quickly gained significant market share. In total, 47 people were interviewed for this study, most of them working for advanced online advertisers. Based on the interviews, a microeconomic mathematic formula is created for modeling an ad platform’s net advertising revenue. The formula is used to identify the five main drivers of an ad platform’s revenue an each of them are studied in depth. The results suggest that the most important revenue drivers the ad platforms can affect are access to an active user base, the efficiency of ad serving and the comprehensiveness of measurement. Setting up a closed ad platform requires significant investments from a publisher and should be only done if it can improve the advertisers’ results. After it’s been established, a closed platform can leverage its position to collect user data and structured business data to optimize its performance further. The results provide a structured understanding of the main dynamics in the industry that can be used in decision-making and a basis for future research on closed ad platforms.Tämä diplomityö tutkii suljettuja mainosalustoja nykyaikaisella online-mainonta-alalla. Alan tutkimus on vielä aluillaan ja suljetun mainosalustan konseptia ei ole olemassa. Tämän tutkimuksen tavoitteena oli löytää online-mainosalustojen liikevaihdon määrittävät tekijät ja ymmärtää miksi jotkut julkaisijat valitsevat omien suljettujen mainosalustojen perustamisen mainospaikkojen kolmansien osapuolien mainosalustoille myymisen sijaan. Suljetun mainosalustan konsepti määritellään olemassaolevaa online- mainontakirjallisuutta ja alustojen hallintarakenneteoriaa hyödyntäen. Tapaustutkimusmenetelmää käyttäen, Google ja Facebook valittiin tapauksiksi, sillä ne ovat ajaneet eniten innovaatioita alalla ja nopeasti saavuttaneet merkittävän markkinaosuuden. Yhteensä 47 henkilöä haastateltiin tätä tutkimusta varten, useimmat heistä edistyneiden online-mainostajien työntekijöitä. Haastattelujen perusteella luodaan mikrotaloudellinen matemaattinen kaava mainosalustan nettoliikevaihdon mallintamiseksi. Kaavaa käytetään tunnistamaan mainosalustan liikevaihdon viisi pääkomponenttia, ja kuhunkin niistä perehdytään syvällisemmin. Tulokset viittaavat, että tärkeimmät liikevaihdon ajurit, joihin mainosalustat voivat vaikuttaa ovat pääsy aktiiviseen käyttäjäkantaan, mainosten näyttämisen tehokkuus ja mittaamisen kattavuus. Suljetun mainosalustan perustaminen vaatii merkittäviä investointeja julkaisijalta ja tulisi tehdä ainoastaan, jos sillä voidaan parantaa mainostajien tuloksia. Suljetun alustan perustamisen jälkeen sen positiota voidaan hyödyntää käyttäjädatan ja strukturoidun liiketoimintadatan keräämiseksi suorituskyvyn edelleen optimoimiseksi. Tulokset tarjoavat toimialan päädynamiikkojen ymmärryksen, jota voidaan käyttää päätöksenteossa sekä pohjana suljettujen mainosalustojen edelleen tutkimiseksi tulevaisuudessa

Adaptive Workflow Design Based on Blockchain

Increasingly, organizational processes have become more complex. There is a need for the design of workflows to focus on how organizations adapt to emergent processes while balancing the need for decentralization and centralization goal. The advancement in new technologies especially blockchain provides organizations with the opportunity to achieve the goal. Using blockchain technology (i.e. smart contract and blocks of specified consensus for deferred action), we leverage the theory of deferred action and a coordination framework to conceptually design a workflow management system that addresses organizational emergence (e-WfMS). Our artifact helps managers to predict and store the impact of deferred actions. We evaluated the effectiveness of our system against a complex adaptive system for utility assessment

Research and Education in Computational Science and Engineering

Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers of all persuasions with algorithmic inventions and software systems that transcend disciplines and scales. Carried on a wave of digital technology, CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society; and the CSE community is at the core of this transformation. However, a combination of disruptive developments---including the architectural complexity of extreme-scale computing, the data revolution that engulfs the planet, and the specialization required to follow the applications to new frontiers---is redefining the scope and reach of the CSE endeavor. This report describes the rapid expansion of CSE and the challenges to sustaining its bold advances. The report also presents strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie

Intelligent purchasing : How artificial intelligence can redefine the purchasing function

Artificial intelligence (AI) can affect all of a company's functions, not least the purchasing department. In addition to automating and optimizing existing processes, AI opens up new opportunities for purchasers to undertake new, strategic, collaborative, enduring missions. AI enables complex, strategic decision-making in an unpredictable, hostile environment. This article analyzes to what extent AI can improve the performance of the purchasing department. First, a review is undertaken of how AI is used in purchasing. Thereafter, the research follows an exploratory, inductive, and qualitative approach based on a multiple case study of the following technologies: (1) the Synertrade automated international purchasing system; (2) the Silex matching system; (3) SAP Ariba decision support; (4) Jaggaer supplier relations management; and (5) the Ideapoke collaborative ideation and innovative project management platform. The present study's contributions lie in its redefinition of the purchasing function, of the purchaser's role, of supplier relationship management policy, and of interdepartmental collaboration, involving, for example, Marketing and R

Hey Google: The Business Case of Environmental Sustainability in Developing Corporate Social Responsibility

What Google wants is to power the world with technology, namely their technology, and in doing so make the world’s information universally accessible and useful (Google, 2017). With a lofty goal of empowering four billion global citizens (adding to the existing three billion) with the benefits of information, their business case strategy has become one of sustainability (Google, 2017). The company realizes that the necessary resources for their technology, energy and water, are becoming increasingly scarce (Google, 2017; Hensel, 2011). Additionally, Google acknowledges the impacts and externalities of the creation of technologies on the environment, specifically its contributions to of climate change. By entrenching major sustainability practices within their corporate structure, Google increases their triple bottom line while satisfying shareholders and stakeholders simultaneously (Slaper and Hall, 2011). This is seen through food waste reductions, renewable energies, and investment in efficient infrastructures (Google, 2017; Richey and Taylor, 2018; Gunders, 2012). Their initiatives do not go uncriticized, pegging Corporate Social Responsibility (CSR) as a self-interested marketing tool and corporate monopoly, which affects market prices and individual agency. However, the aim of this work is not to focus on whether Google is just “another bad corporation,” as critics emphasize, but rather to delve deeper into the good they leverage into daily accountability. Therefore, using direct examples of Google’s sustainability initiatives and drawing upon theory, the capacity of CSR to foster ingenuity and innovation growth within multinational corporations is shown, highlighting the benefits to profits, people, and the planet

Unlocking value from machines: business models and the industrial internet of things

In this article we argue that the Industrial Internet of Things (IIoT) offers new opportunities and harbors threats that companies are not able to address with existing business models. Entrepreneurship and Transaction Cost Theories are used to explore the conditions for designing nonownership business models for the emerging IIoT with its implications for sharing uncertain opportunities and downsides, and for transforming these uncertainties into business opportunities. Nonownership contracts are introduced as the basis for business model design and are proposed as an architecture for the productive sharing of uncertainties in IIoT manufacturing networks. The following three main types of IIoT-enabled business models were identified: (1) Provision of manufacturing assets, maintenance and repair, and their operation, (2) innovative information and analytical services that help manufacturing (e.g., based on artificial intelligence, big data, and analytics), and (3) new services targeted at end-users (e.g., offering efficient customization by integrating end-users into the manufacturing and supply chain ecosystem)

Police Knowledge Exchange: Full Report 2018

[Executive Summary] This report was commissioned to explore the enablers and barriers to sharing within and between police forces and between police forces and partners, including the public. This was completed from an interdisciplinary review of international literature covering sharing, knowledge exchange, learning and organisational learning. The literature broke down into four main factors; who, why, what and how. An introduction to the literature is presented with ‘Who’ is sharing which considers both personal identity and different institutional issues. The ‘Why’ literature covers issues of cultural and community motivators and barriers. The ‘What’ segment reviews concepts of data, information and knowledge and related legislative issues. Finally, the ‘how’ section spans face to face sharing approaches to technologies that produce both enablers and barriers. A series of 42 in-depth interviews and focus groups were completed and combined with 47 survey responses . The aim of the interviews, focus groups and survey was to show perceptions and beliefs around knowledge sharing from a small sample across policing in order to complement the findings from the literature review. The survey was adapted from a standardised questionnaire (Biggs, 1987). The Biggs questionnaire focused on what motivated students to learn and how they approached their learning. Our adapted survey looked at what motivated police to share, and how they approached sharing. The responses showed a trend, across the police, towards a motivation for sharing to develop a deeper understanding of issues. However, the approaches and the strategies they used to share with others, which were primarily driven by achieving and surface approaches (to get promoted and get the job done). According to Biggs (1987) this could leave them discontented as they never progress to a deeper understanding of issues. Scaffolding sharing within the police through processes that are clearly defined, effective and valued could help to overcome these issues. Within the interviews and focus group findings a similar structured approach to sharing was adopted. Within the ‘who’ section some key aspects around personal relationships, reciprocity and reputation were identified. The ‘why’ the police share was one of the largest discussion points. Not only was there a deep motivation to solve key policing issues there was an approach of reciprocity. Police sharing was deeply motivated to support ‘good practice’ in the prevention and detection of crime. However, a sharing barrier was identified in the parity of value given to different types of knowledge for example between professional judgement and research evidence knowledge. Sharing was achieved when there were reciprocal benefits, in particular with personal networks or face to face sharing which was noted as ‘safe’. Again, this was inhibited by misunderstandings around the ‘risks’ of sharing, frequently attributed to data protection legislation; producing cautious reactions and as an avoidance tactic to save time and effort sharing. However, a divide was noted between technical users and those who avoided any online systems for sharing; often due to poorly designed systems and a lack of confidence in how to use systems. The police culture was identified as being risk-adverse, and competitive due to multiple factors, a lack of supported time to share, Her Majesty’s Inspectorate of Constabulary (HMIC) reviews and promotion criteria. The result was perceived to be a poor cultural ability to learn from mistakes and a likelihood to repeat errors. A set of strategic recommendations are given and include the use of a sharing authorised professional practice for HMIC reviews, sharing networks and training. A further set of operational recommendations are given such as; sharing impact cases for evidence based practice, data sharing officers and evaluating mechanisms for sharing. This full report is supported by the Police Knowledge Exchange Summary Report 2018 which gives an overview of the findings and recommendations

Investigating Potential Interventions on disruptive impacts of Industry 4.0 technologies in Circular Supply chains: Evidence from SMEs of an Emerging Economy

As a transversal theme, the intertwining of digitalization and sustainability has crossed all Supply Chains (SCs) levels dealing with widespread environmental and societal concerns. This paper investigates the potential interventions and disruptive impacts that Industry 4.0 technologies may have on pharmaceutical Circular SCs (CSCs). To accomplish this, a novel method involving a literature review and Pythagorean fuzzy-Delphi has initially been employed to identify and screen categorized lists of Industry 4.0 Disruptive Technologies (IDTs) and their impacts on pharmaceutical CSC. Subsequently, the weight of finalized impacts and the performance score of finalized IDTs have simultaneously been measured via a novel version of Pythagorean fuzzy SECA (Simultaneously Evaluation of Criteria and Alternatives). Then, the priority of each intervention for disruptive impacts of Industry 4.0 has been determined via the Hanlon method. This is one of the first papers to provide in-depth insights into advancing the study of the disruptive action of Industry 4.0 technologies cross-fertilizing CE throughout pharmaceutical SCs in the emerging economy of Iran. The results indicate that digital technologies such as Big Data Analytics, Global Positioning Systems, Enterprise Resource Planning, and Digital Platforms are quite available in the Irans' pharmaceutical industry. These technologies, along with four available interventions, e.g., environmental regulations, subsidy, fine, and reward, would facilitate moving towards a lean, agile, resilient, and sustainable supply chain through the efficient utilization of resources, optimized waste management, and substituting the human workforce by machines