This study aims to investigate the short-run and long-run relationship between economic variables and the unemployment rate in South Asian countries. A panel vector error correction model is used to establish the long-run and the short-run relationship between the unemployment rate and the selected economic variables. Data were collected from WDI, WGI, and FDSD for the years 1994–2016. In order to determine the direction of the relationship, the Granger causality test was used. Impulse response functions (IRFs) and forecast error variance decomposition were used to assess the stability of the relationship between the unemployment rate and economic variables over time. The finding of the study showed a negative and significant relationship at the 5% level of significance between governance, internet users, mobile cellular subscriptions, fixed broadband subscriptions, and human capital and the unemployment rate of South Asian economies. On the other hand, financial activity (credit) and population growth had a positive and significant relationship with the unemployment rate. Finally, the Granger causality test showed bidirectional causality between governance and unemployment rate, while internet users and fixed broadband subscriptions showed unidirectional causality with the unemployment rate; furthermore, population growth, financial activity (credit), mobile cellular subscriptions, and human capital showed no causality in the short run.
This study aims at presenting the legally, technically, and economically empowered suggestion for a clear definition of a competitive market of gas fuels and electricity of a Member State in order to be utilized within trans-border trade of these utilities, as required by the European Union (EU) legislation. Thus, this study addresses, first of all, the issue of the division of the national gas fuel and electricity market into sections and separating these market segments that are more susceptible to the existence of competition in the trans-border dimension. This division is a model that reflects every internal market that is self-sufficient and distinguished in technical terms which has been established and is functioning within one or more Member States. The suggested structural, subject-related division of the market into sections, a competitive one (with its segments), a balancing one, and a technical one, makes it possible to determine which fragments of the market prevail over merely the technical security of ensuring continuity and quality of electricity supplies at the national level. Public forms of electricity and gas fuel trading take first place. Thus, second, the issues of legal and business conditions for operation in the energy section of the commodity exchange, regulated market, or open tenders for purchase of energy and interdependence between public forms of electricity or fuel gas trading and standards in the common electricity market have become the subject of this study. The advantage of a commodity exchange that establishes transparent conditions for public trading transactions involving these goods and provides pricing information for actors in the market cannot be overestimated. A commodity exchange enhances competition and is instrumental in the reduction of prices for ultimate clients. The completed analysis aims at reviewing public forms of trading as the instruments for achievement of the objectives of the national energy law and a component for a common energy market in the perspective of development of trans-border transmission capabilities. Legal multi-centricity and multi-aspectual nature of the addressed issues form a structure of relations that has affected the selection of the research methodology. Three research methods were adopted as the main principles that, bearing in mind a different context in which they are used, are treated to be complementary. The first one is an interdisciplinary research analysis, taking account of the context of functioning in the EU law environment in the interpretation of the national law provisions and technical sciences (and thus, e.g., laws of physics, properties of energy, technical aspects of functioning of the power industry as a system of interdependent relations of installations and grids) and economic sciences (e.g., a concept of the market, competition, operation of the commodity exchange). References to technical or economic sciences allowed to maintain the clarity of the above considerations and render the addressed issues better in practice. The legal and dogmatic method is an indispensable supplement of the above method; in this method, the process of interpretation of legal regulations is based on the jurisprudence and case law which should be referred, in particular, to the national law; it is made complete by the analysis of the economic practice. The selection of the concept analysis method (a linguistic one) as the third method should be justified by the undertaken attempts to define in a precise manner the content and the scope of meaning of general, generic concepts making references, as a rule, to a broad spectrum of business operations, the application of which in the EU legislation is a feature of this legal order established on the basis of the elements of the continental (established, statute) law and flexible common law.
The implementation of bot interfaces varies tremendously in current industry practice. They range from the human-like to those that merely present a brand logo or a digital avatar. Some applications provide a maximum amount of information with limited turn-taking between the user and the interface; others offer only short pieces of information and require more turn-taking. Instead of simply implementing the default option provided by chatbot providers and platforms, companies should consider very carefully how the specifics of the chatbot interface might affect the user experience. Simple mechanics such as increasing the frequency of interactions leads to greater trust and a more enjoyable user experience. Also, personalizing chatbots with basic consumer characteristics such as gender increases trust and improves the perceived closeness between the customer and the chatbot – and ultimately the brand. Brand managers should therefore consider chatbots not as merely another digital marketing fad or a way to save costs through service automation. When implemented wisely, they are even able to increase a company’s upselling potential.
Thanks to the rapid progress in the field of artificial intelligence algorithms are able to accomplish an increasingly comprehensive list of tasks, and often they achieve better results than human experts. Nevertheless, many consumers have ambivalent feelings towards algorithms and tend to trust humans more than they trust machines. Especially when tasks are perceived as subjective, consumers often assume that algorithms will be less effective, even if this belief is getting more and more inaccurate.
To encourage algorithm adoption, managers should provide empirical evidence of the algorithm’s superior performance relative to humans. Given that consumers trust in the cognitive capabilities of algorithms, another way to increase trust is to demonstrate that these capabilities are relevant for the task in question. Further, explaining that algorithms can detect and understand human emotions can enhance adoption of algorithms for subjective tasks.
Whatever your perception of AI is, the machine age of marketing has arrived. To fully grasp how AI is changing every fabric of both our professional and private lives, we need to abstract beyond the presence of autonomous cars, digital voice assistants, or using machines to translate some text for us. AI is creating new forms of competition, value chains, and novel ways of orchestrating economies around the world. AI is more than just technology, it’s creating a new economy. The fuel that runs this economy is the combination of computational processing power, data, and the algorithms that process this data.
AI has the potential to make our life easier, but this convenience might come at a price which we have to pay such as biases directly built-in to the algorithms we use, data privacy issues or failed AI projects in business practice. But without testing, failing, and learning from our failures, there
Interview with Jan Neumann, Senior Director, Applied AI, Comcast Cable, Philadelphia, USA
While many customers are still reluctant to entrust themselves to Alexa, Cortona or Siri in their homes, they seem to be less worried about controlling their TV sets via voice control. Comcast started offering a voice-based remote control in 2015 and has extended this service continuously. In the vast world of home entertainment, it seems that voice has come just in time to help consumers navigate and control their ever-increasing home entertainment options. Jan Neumann explains how Comcast enables its customers to comfortably boil down a huge entertainment portfolio to personally relevant content on the TV screen, and how the company remains successful in the highly competitive home entertainment market.
Edmond Awad, Jean-François Bonnefon, Azim Shariff and Iyad Rahwan
The algorithms that control AVs will need to embed moral principles guiding their decisions in situations of unavoidable harm. Manufacturers and regulators are confronted with three potentially incompatible objectives: being consistent, not causing public outrage, and not discouraging buyers. The presented moral machine study is a step towards solving this problem as it tries to learn how people all over the world feel about the alternative decisions the AI of self-driving vehicles might have to make. The global study displayed broad agreement across regions regarding how to handle unavoidable accidents. To master the moral challenges, all stakeholders should embrace the topic of machine ethics: this is a unique opportunity to decide as a community what we believe to be right or wrong, and to make sure that machines, unlike humans, unerringly follow the agreed-upon moral preferences. The integration of autonomous cars will require a new social contract that provides clear guidelines about who is responsible for different kinds of accidents, how monitoring and enforcement will be performed, and how trust among all stakeholders can be engendered.
Consumers produce enormous amounts of textual data of product reviews online. Artificial intelligence (AI) can help analyze this data and generate insights about consumer preferences and decision-making. A GfK research project tested how we can use AI to learn consumer preferences and predict choices from publicly available social media and review data. The common AI tool “Word Embeddings” was used and has shown to be a powerful way to analyze the words people use. It helped reveal consumers’ preferred brands, favorite features and main benefits. Language biases uncovered by the analysis can indicate preferences. Compared to actual sales data from GfK panels, they fit reasonably within various categories. Especially when data volumes were large, the method produced very accurate results. By using free, widespread online data it is completely passive, without affecting respondents or leading them into ranking or answering questions they would otherwise not even have thought of. The analysis is fast to run and no fancy processing power is needed.