Making sense of the Internet of Things: A critical review of Internet of Things definitions between 2005 and 2019

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

Estimating the wider value generated by UNESCO’s designations in the United Kingdom

In September 2015, the United Nations General Assembly adopted a set of objectives related to promoting and supporting sustainable development around the globe through education, human knowledge, communication and culture. These objectives are commonly known as the Sustainable Development Goals (SDGs) and are an inter-dependent set of 17 goals that 195 Member States have agreed to achieve by 2030. As a specialised agency of the United Nations, and the global lead on education, UNESCO has a vital role to play in delivering the SDGs. UNESCO’s global network of 'designations', including World Heritage Sites, Biosphere Reserves, UNESCO University Chair Programme, and Global Geoparks, also play an essential role in promoting and supporting local sustainable development and achieving the SDGs. However, the different geographic, cultural and political regimes under which UNESCO designations are called to operate, pose significant challenges for the network to effectively be managed and contribute towards the SDGs. Moreover, the heterogeneity of organisational structures and boundaries in terms of efficiency, power and competence, prevents UNESCO designations "value-added" activities from reaching their full potential. We performed a survey of 74 designations in England, Northern Ireland, Scotland and Wales. Drawing from the business model component framework, our research aims to i) identify value generating configurations of organisational structures that transcend designations’ type, ii) estimate the value generated by the designation and their contribution to UNESCO’s SDGs; and, iii) develop a framework that can be used by national governments to make sense of UNESCO’s value generated activities. The framework can help UNESCO’s National Commissions to improve the efficient management of the designation’s global network and allow countries with different levels of economic and societal development to cooperate to tackle contemporary global challenges

A strategic roadmap for BM change for the video-games industry

The global video games industry has experienced and exponential growth in terms of socioeconomic impact during the last 50 years. Surprisingly, little academic interest is directed towards the industry, particularly in the context of BM Change. As a technologically intensive creative industry, developing studios and publishers experience substantial internal and external forces to identify, and sustain, their competitive advantage. To achieve that, managers are called to systematically explore and exploit, alternative BMs that are compatible with the company’s strategy. We build on empirical analysis of the video-games industry to construct a Toolkit that i) will help practitioners and academics to describe the industrial ecosystem of BMs more accurately, and ii) use it a strategic roadmap for managers to navigate through alternatives for entrepreneurial and growth purposes

Do unicorns exist in China? A study of the Chinese technological start-up ecosystem

Venture capital syndication plays a pivotal role in fuelling the growth of new ventures that subsequently become unicorns, privately owned companies valued above $1bn. China has been very successful in generating new unicorns, surpassed only by the United States. In this paper we examine the impact of the experience of venture capital companies and the strategic choice(s) of the unicorn to grow inorganically through acquisition in two areas: the amount of funding available to unicorns and the size of the syndication network. It is argued in the literature that both of these play a key role in shaping the growth of new enterprises. We find that the experience in lead investments of the first investor of the unicorn has a positive impact in terms of both amount of funding and size of the syndication. We also find that the presence of an experienced investor has a positive effect on the syndication size but a negative effect on the total amount of funding that is raised. Finally, acquisitions have a strong effect on both the amount of funding and the size of the syndication

Using Deep Q-learning to understand the tax evasion behavior of risk-averse firms

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

Μαρκοβιανό μοντέλο στήριξης αποφάσεων για τη φοροδιαφυγή στην Ελλάδα

This thesis proposes a Markov-based decision support model which captures the behaviour of a typical firm in Greece, vis-a-vis tax evasion. To maximize its long-term wealth, the firm has two options at its disposal. First, it can manipulate the percentage of its profits that it will disclose (and consequently be taxed on). Second, there is a type of optional tax amnesty that may be offered to the firm by the Greek government. This option, termed “closure”, allows the firm to pay a lump-sum tax, based on its gross income or stated profits, in exchange for eliminating the possibility of an audit against the firm's past income declarations. We describe a dynamical system, aimed at predicting the actions of the average Greek firm, and at evaluating tax policies before they are implemented. The basic model evolves through several iterations, some computationally challenging, depending on the firm's attitude towards risk and the availability of closure. The model proposed in this thesis represents an innovative step towards bridging the gap between classical macroeconomic and game-theoretic approaches on the subject. It takes into consideration the sequential nature of the firm's decisions regarding tax evasion, together with its attitude towards risk, and allows us to: i) analyze the effectiveness of the closure option, ii) show that in the current environment a rational enterprise has no incentive to disclose its profits, iii) identify ``virtuous'' combinations of tax parameters which lead to full disclosure of profits and iv) estimate a firm's risk-aversion. Our analysis can be used to evaluate the effectiveness of various taxation schemes, potentially benefiting both firms and government

A Markov-based decision model of tax evasion for risk-averse firms in Greece

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

Tax evasion by risk-averse firms in Greece: a discrete Markov-based optimization model

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