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It seems that the Internet boom, which started at the end of the 1990s and finished with the spectacular collapse of the so-called dotcoms, is probably over. We are currently enjoying a period of fast and stable growth. This is manifested by the growing number of both Internet users and companies which – to an ever-increasing extent – use the Internet as a form of communication (both internal and external), promotion, sales etc. Expenditures on Internet advertising are growing continuously and now constitute more than 25% of all advertising expenditure. A natural consequence of this development is the need for the standardisation and organisation of the world of the Internet. These activities will result in a greater awareness of the benefits which this medium provides, increasing the possibilities of its use, and – most importantly – the opportunity to evaluate the return on investments made on the Internet. Nowadays, it is clear that many companies are striving to increase the quality of their activities on the Internet or to improve the effectiveness of such activities. As a consequence, the number of companies that look for indices which would enable the making of more precise and effective decisions in the scope of online operations is growing.

This chapter is dedicated to the phenomenon of the increasing role of the Internet in business, including the scale of its use by Polish and international companies. We present the most commonly used measures of marketing activities on the Internet and in social media. This group includes the indices which make it possible to determine whether a company actually needs a website. Other measures allow for the improvement in the effectiveness of the activity on the Internet, whereas others specify the costs of activities on the Internet and often serve as the basis for settlements between a company and advertising agencies or companies specialising in website design. It is worth emphasising that the Paid, Earned, Shared, Owned (PESO) model, worked out by Don Bartholomew,¹ is the basis for creation and description of indices concerning social media. This model has gained certain popularity in the social media industry. It does not, however, specify how individual indices should be named and calculated. It maps already existing indices and adapts them to specific levels of marketing communication measurement. All the measures indicated by the author of the model have been grouped into five major areas: exposure, engagement, brand awareness, action and recommendations. This model– similarly to all models of performance measurement – inspired by the sales funnel concept, adjusts certain standard indices and proposals of measurements for specific levels. Additionally, the measures are divided into four types, depending on who the owner of the content is: Paid (P) – refers to all forms of paid content; Owned (O) – all websites and web properties controlled by a company or brand; Earned (E) – the contents about a given brand created spontaneously by Internet users; and Shared (S) – the contents shared by Internet users.

Many recent chapters have investigated whether data from internet search engines such as Google can help improve nowcasts or short-term forecasts of macroeconomic variables. These chapters construct variables based on Google searches and use them as explanatory variables in regression models. We add to this literature by nowcasting using dynamic model selection (DMS) methods which allow for model switching between time-varying parameter regression models. This is potentially useful in an environment of coefficient instability and over-parameterization which can arise when forecasting with Google variables. We extend the DMS methodology by allowing for the model switching to be controlled by the Google variables through what we call “Google probabilities”: instead of using Google variables as regressors, we allow them to determine which nowcasting model should be used at each point in time. In an empirical exercise involving nine major monthly US macroeconomic variables, we find DMS methods to provide large improvements in nowcasting. Our use of Google model probabilities within DMS often performs better than conventional DMS methods.

Geotechnologies have a long tradition in several areas of society and research. The recent development of the ‘Internet of Everything’ (IoE) and Geographic Information Systems (GIS) technologies opened several doors to the contribution of tourism. Emergent technologies contributions to tourism and planning such as web mapping, augmented reality (AR), crowdsourcing and crowdsensing are relatively recent, and there is a lack of research around their potential for Creative Tourism enhancement. For example, combining web mapping with AR or storytelling can be an excellent contribution to operators, planners and tourists. For research purposes, new opportunities are open, particularly by integrating community-shared data. It is well known for the popularity of social networks, the exponential growth of photo sharing, but few studies have been implemented to understand their contribution to research. This chapter focuses on emerging geotechnologies concerning cultural mapping, Creative Tourism and sustainability. Since it is a new growing niche, more research is needed to develop and understand the potential of new approaches. Besides traditional techniques such as quantitative (e.g. surveys) and qualitative ones (e.g. interviews, focus groups and world café), it revises the role of geotechnologies on Creative Tourism development and growing activities. Results from case studies from Europe are analysed.

This chapter presents a summary of the literature on the influence of the Internet and other digital innovations on markets, consumers, and firms. The review leads to a list of topics in need of research in the general areas of big data, online and mobile advertising, consumer search, online privacy, online reviews, social networks, platforms for online transactions, and the impact of the Internet on retail markets, including multi-channel and omni-channel retailing. We discuss the big data approaches that have been applied to problems of targeting and positioning and suggest areas for further development of these approaches. We also discuss the emerging area of mobile advertising, which can further enhance targeting. On the consumer side, the evidence indicates that the Internet has greatly lowered the costs of search and access to retailers. Much of the consumer data are transmitted to sellers, and much of the online advertising is transmitted to consumers, through platforms, such as Google. We conclude that better models of competition among these platform firms are needed and that they need to be examined for anti-trust violations. While online retailing has grown rapidly, it still has a relatively small share of retail sales. Since sellers can combine the advantages of online and offline channels, it has been common for sellers to branch into multi-channel retailing. Given the increased availability of detailed consumer data, omni-channel selling, which emphasizes strategies for the various touchpoints that lead to a transaction, is an area for further development.

With the rise of alternate discovery services, such as Google Scholar, in conjunction with the increase in open access content, researchers have the option to bypass academic libraries when they search for and retrieve scholarly information. This state of affairs implies that academic libraries exist in competition with these alternate services and with the patrons who use them, and as a result, may be disintermediated from the scholarly information seeking and retrieval process. Drawing from decision and game theory, bounded rationality, information seeking theory, citation theory, and social computing theory, this study investigates how academic librarians are responding as competitors to changing scholarly information seeking and collecting practices. Bibliographic data was collected in 2010 from a systematic random sample of references on CiteULike.org and analyzed with three years of bibliometric data collected from Google Scholar. Findings suggest that although scholars may choose to bypass libraries when they seek scholarly information, academic libraries continue to provide a majority of scholarly documentation needs through open access and institutional repositories. Overall, the results indicate that academic librarians are playing the scholarly communication game competitively.