Experimentation plays a vital role in firms’ activities and strategies. Yet, we still have a scant understanding of the processes underpinning experimentation for business modelling in startups. Through a qualitative longitudinal case, we investigate how a startup reshapes its value proposition and engages with external market stakeholders – in this case end customers – when their heterogeneity could not be catered by the first business model designed by the entrepreneur. We look at cases where a business model, originally envisioned with a single value proposition, needs to cater different groups of customers (i.e., product users, buyers, and beneficiaries) with diverse expectations in terms of value and engagement. Our study identifies a ‘selection loop’ process formed of three different experimentation phases – environment-based stress, cognitive-driven adaptation, and stakeholder-led evolution. This process contributes to better understand how managerial cognition drives business model design and experimentation towards a “fit” between the entrepreneur’s cognitive model and the stakeholders in the external environment. We also advance the cognitive perspective on business models as we take a cognitive lens to explore experimentation as a process to identify and adapt a business model to emerging evidence about a startup’s customers.

Firms reconfigure their resources when responding to changes in their external or internal environment, often by incorporating new knowledge and resources in collaboration with external stakeholders. However, the reconfiguration process is challenging, costly, and often fails. The firm’s history plays an important role in the resource reconfiguration process being path dependent. Path dependency, debatably, leads to lock-in effects that prevent reconfiguration. However, we argue that this is a very limited viewpoint. In this paper, drawing empirical evidence from the digital games industry, we contend that path dependency constrains the reconfiguration action space and can potentially improve strategic planning by identifying the paths of least resistance. Depending on the extent of the reconfiguration, we identify two alternatives: direct reconfiguration (horizontal reconfiguration) or increasing the complexity by incorporating additional configurations (vertical reconfiguration).

Purpose This paper aims to study the evolution of definitions of internet of things (IoT) through time, critically assess the knowledge these definitions contain and facilitate sensemaking by providing those unfamiliar with IoT with a theoretical definition and an extended framework. Design/methodology/approach 164 articles published between 2005 and 2019 are collected using snowball sampling. Further, 100 unique definitions are identified in the sample. Definitions are examined using content analysis and applying a theoretical framework of five knowledge dimensions. Findings In declarative/relational dimensions of knowledge, increasing levels of agreement are observed in the sample. Sources of tautological reasoning are identified. In conditional and causal dimensions, definitions of IoT remain underdeveloped. In the former, potential limitations of IoT related to resource scarcity, privacy and security are overlooked. In the latter, three main loci of agreement are identified. Research limitations/implications This study does not cover all published definitions of IoT. Some narratives may be omitted by our selection criteria and process. Practical implications This study supports sensemaking of IoT. Main loci of agreement in definitions of IoT are identified. Avenues for further clarification and consensus are explored. A new framework that can facilitate further investigation and agreement is introduced. Originality/value This is, to the authors' knowledge, the first study that examines the historical evolution of definitions of IoT vis-a-vis its technological features. This study introduces an updated framework to critically assess and compare definitions, identify ambiguities and resolve conflicts among different interpretations. The framework can be used to compare past and future definitions and help actors unfamiliar with IoT to make sense of it in a way to reduce adoption costs. It can also support researchers in studying early discussions of IoT.

Purpose This paper aims to study the evolution of definitions of internet of things (IoT) through time, critically assess the knowledge these definitions contain and facilitate sensemaking by providing those unfamiliar with IoT with a theoretical definition and an extended framework. Design/methodology/approach 164 articles published between 2005 and 2019 are collected using snowball sampling. Further, 100 unique definitions are identified in the sample. Definitions are examined using content analysis and applying a theoretical framework of five knowledge dimensions. Findings In declarative/relational dimensions of knowledge, increasing levels of agreement are observed in the sample. Sources of tautological reasoning are identified. In conditional and causal dimensions, definitions of IoT remain underdeveloped. In the former, potential limitations of IoT related to resource scarcity, privacy and security are overlooked. In the latter, three main loci of agreement are identified. Research limitations/implications This study does not cover all published definitions of IoT. Some narratives may be omitted by our selection criteria and process. Practical implications This study supports sensemaking of IoT. Main loci of agreement in definitions of IoT are identified. Avenues for further clarification and consensus are explored. A new framework that can facilitate further investigation and agreement is introduced. Originality/value This is, to the authors’ knowledge, the first study that examines the historical evolution of definitions of IoT vis-à-vis its technological features. This study introduces an updated framework to critically assess and compare definitions, identify ambiguities and resolve conflicts among different interpretations. The framework can be used to compare past and future definitions and help actors unfamiliar with IoT to make sense of it in a way to reduce adoption costs. It can also support researchers in studying early discussions of IoT.

In this paper, we analyze the gameplay data of three popular customizable card games where players build decks prior to gameplay. We analyze the data from a player engagement perspective, how the business model affects players, how players influence the business model and provide strategic insights for players themselves. Sifa et al. found a lack of crass-game analytics, whereas Marchand and Hennig-Thurau identified a lack of understanding of how a game's business model and strategies affect players. We address both issues. The three games have similar business models but differ in one aspect: the distribution model for the cards used in the game. Our longitudinal analysis highlights this variation's impact. A uniform distribution creates a spread of decks with slowly emerging trends while a random distribution creates stripes of deck building activity that switch suddenly each update. Our method is simple, easily understandable, independent of the specific game's structure, and able to compare multiple games. It is applicable to games that release updates and enables comparison across games. Optimizing a game's updates strategy is the key, as it affects player engagement and retention, which directly influence businesses' revenues and profitability in the $95 billion global games market.

Designing tax policies that are effective in curbing tax evasion and maximize state revenues requires a rigorous understanding of taxpayer behavior. This work explores the problem of determining the strategy a self-interested, risk-averse tax entity is expected to follow, as it navigates - in the context of a Markov Decision Process - a government-controlled tax environment that includes random audits, penalties and occasional tax amnesties. Although simplified versions of this problem have been previously explored, the mere assumption of risk-aversion (as opposed to risk-neutrality) raises the complexity of finding the optimal policy well beyond the reach of analytical techniques. Here, we obtain approximate solutions via a combination of Q-learning and recent advances in Deep Reinforcement Learning. By doing so, we i) determine the tax evasion behavior expected of the taxpayer entity, ii) calculate the degree of risk aversion of the average entity given empirical estimates of tax evasion, and iii) evaluate sample tax policies, in terms of expected revenues. Our model can be useful as a testbed for in-vitro testing of tax policies, while our results lead to various policy recommendations.

This study contributes to understanding the effects of crowdfunding on the value creation process in the digital game industry. Specifically, it integrates the value chain logic with the platform logic to examine collaborative value creation enabled by opening up the business models of game developers to the crowd. Through a multiple case design this research shows that the benefit of using crowdfunding goes well beyond fundraising. As an implementation of open innovation, crowdfunding unifies the channels that bring capital, technology and market knowledge from the crowd into the game. This finding leads to the exploration of a new complex system of interactions between game developers and value chain stakeholders, and invokes the analysis of crowdfunding as a form of technological platform to identify and analyze new types of collaboration and competition. This research limits its findings to the effects of reward-based crowdfunding. Other forms of crowdfunding require further investigations. The paper also aims to help practitioners understand how crowdfunding is transforming the game industry.

The study presented in this paper investigates companies operating in the UK video-game industry with regard to their levels of survivability. Using a unique dataset of companies founded between 2009 and 2014, and combining elements and theories from the fields of Organisational Ecology and Industrial Organisation, the authors develop a set of hierarchical logistic regressions to explore and examine the effects of a range of variables such as industry concentration, market size and density on companies' survival rates. The analysis addresses locational dimension of the video-game industry is considered by introducing an extra regionally-related variable into the models, associated with the number of video-game university programmes locally available. In addition, companies are investigated with regard to their organisational type in order to identify potential effects associated with their intrinsic organisational structures.Findings from the analysis confirm that UK video-game companies operate in an increasingly globalised market, limiting the effects related to any operation conducted at a local level. For instance, a higher supply of specialised graduates within spatial proximity does not contributt significantly to increase the chances of survivability of video-game companies, although different locations seem to provide better conditions and higher life expectancy, mainly due to positive network effects occurring at a local level. Results seem also to suggest that investing in managerial resources increases businesses' survival rates, corroborating evidence about the significant role entrepreneurs have for companies operating within innovative and technologically intensive industries. (C) 2016 Published by Elsevier Inc.

Game Intelligence is knowledge gained by the player or by analysing the data players generate by playing digital games. Serious games for education, raising public awarenesss or changing the players' behaviour are well established and have provided Game Intelligence for decades [1]. However, more recently a trend has begun, inspired by the success of FoldIt [2], [3], of developing games for scientific discovery. These games lower the barrier of entry to complex scientific topics, allowing gamers to contribute to cutting edge research. We argue that this approach is currently underutilized and explore a vision where these games have wider impact. Furthermore, we will discuss the potential of extracting Game Intelligence from games designed originally for entertainment, potentially making all games into scientific discovery games.

The digital games industry - along with the music, lm and book industries - is commonly referred to as part of the creative industry. However, although they can all be grouped under the same label, the digital game industry is the only one that is natively digital. During the past decade, the industry experienced a phenomenal growth in terms of social and economic significance. However, the impact of the industry, in socioeconomic terms, has remained unexplored in academic literature. Our project, NEMOG1, leverages on the impact that the high degree of innovation has on all the stakeholders along the industry value chain, it addresses the changes enabled by technology in terms of business models and industrial organizational structure. To do so, the first year of research has focused on three research issues: the analysis of the impact of technology on business models innovation in the digital game industry, the mapping of the evolutionary trajectory of the industry's business model in-novation process [8], the innovation mechanisms that fuelled this particular path and growing areas of potential uses of digital games outside of purely entertainment purposes.

Since 1990, Business Models emerged as a new unit of interest among both academics and practitioners. An emerging theme in the growing academic literature is focused on developing a system that employs business models as a focal point of enterprise classification. In this paper we attempt a historical analysis of the video game industry business model evolution and examine the process through the prism of two-sided market economics. Based on the biological school of phylogenetic classification, we develop a cladogram that captures the evolution process and classifies the industry's business models. The classification system is regarded as a first attempt to provide an exploratory and descriptive research of the video game industry, before attempting an explanatory and predictive analysis, and introduces a system that is not governed by the industry's specific characteristics and can be universally applied, providing a map for researchers and practitioners to test organisational differences and contribute further to the business model knowledge.

We present a Markov-based model of the process via which a representative' Greek risk-averse firm decides the degree to which it should engage in tax evasion. The model is constructed around a simplified version of the Greek tax system which includes random audits and penalties for under-reporting profits. For its part, the firm is allowed to manipulate its stated profits, potentially exposing itself to future penalty payments, in an attempt to maximize the expected utility of its after-tax wealth. Using our model, we determine the optimal behaviour expected of the firm as a function of the parameters of the tax system, and identify subsets of the audit probability - tax penalty space which remove' the inventive for tax evasion. This allows us to - among other things - evaluate the effectiveness of the parameter values currently in use and determine the implied level of risk-aversion for the average Greek firm.

We develop a Markov-based optimization model that captures the process via which a risk-averse firm in Greece decides whether to engage in tax evasion. The firm seeks to maximize the expected utility of its wealth, the latter viewed as a function of the portion of profits which the firm attempts to conceal from the government. Our model takes into account the basic features of the Greek tax system, including random audits and tax penalties applied when the audit reveals any wrongdoing. The proposed model is used to (1) show that the parameters currently in place are conducive to tax evasion and (2) “chart” the problem’s parameter space in order to identify “virtuous” combinations (from the point of view of the government), and obtain a relationship between audit probability, tax penalty and likelihood of the firm engaging in tax evasion.

In the midst of the financial crisis currently unfolding in Greece, tax revenue collection is considered a top priority. This work describes a dynamic, Markov-based decision support model, aimed at predicting the behavior of a risk-neutral enterprise in Greece, and at evaluating tax policies before they are implemented. We use our model to i) analyze the effectiveness of an alternative taxation option periodically offered by the Greek government, ii) show that in the current environment, a rational enterprise has no incentive to disclose its profits, and iii) identify "virtuous" combinations of parameters which lead to full disclosure of profits. Highlights We propose a parametric Markov-based DSS model for tax evasion by Greek firms. An alternate tax option used by the government raises the incentive for tax evasion. We compute the firm's optimal behavior given the parameters of the tax system. Our model is used to identify tax parameters which are effective and fair. In today's setting, it is optimal for a risk-neutral rational firm to evade taxes.

In this paper we introduce an environmentally driven conceptual framework of Business Model change. Business models acquired substantial momentum in academic literature during the past decade. Several studies focused on what exactly constitutes a Business Model (role model, recipe, architecture etc.) triggering a theoretical debate about the Business Model’s components and their corresponding dynamics and relationships. In this paper, we argue that for Business Models as cognitive structures, are highly influenced in terms of relevance by the context of application, which consequently enriches its functionality. As a result, the Business Model can be used either as a role model (benchmarking) or a recipe (strategy). For that purpose, we assume that the Business Model is embedded within the economic (task) environment, and consequently affected by it. Through a typology of the environmental impact on the Business Model productivity, we introduce a conceptual framework that aims to capture the salient features of Business Model emergent resilience as reaction to two types impact: productivity constraining and disturbing.

This report builds on previous work aligned with the North East Economic Review (Adonis, 2013), and more specifically the North East Local Enterprise Partnership Economic Strategy (2017)1 that acts as the most recent benchmark of the earlier Economic Review, as well as reports aimed at stimulating North East2 economic growth through initiatives such as promoting the region as an economic corridor3. Using an analysis of wide-scale secondary data, North East firms show proficiency in innovation in some respects, mostly depending upon specific sector and organisational contexts. These innovation indicators are presented in more detail later in the report. Mainly, it is expected that these indicators do not function solely in isolation, but rather in combination will lead to a better predictor of innovation success. An integration of the 2017 Gartner hype cycle of emerging technologies with the competencies and industries of the North East is also presented, to look ahead to opportunities and threats to the region’s companies from a rapidly developing, increasingly tech-focused dynamic world marketplace. Findings show that some sectors within the North East are much more affected by this development than others. Where some will be quick to adopt to change, others may need increased support in taking up new technologies in order to survive.