Analytics, Innovation, and Excellence-Driven Enterprise Sustainability

This present book offers a unique view on innovativeness and competitiveness that improve when organizations establish alliances with partners who have strong capabilities and broad social capital, allowing them to create value and growth as well as technological knowledge and legitimacy through new knowledge resources. Additionally, the value of digital technology, at both personal and industrial levels, leads to new opportunities that emerge for creating experiences, processes, and organizational forms that fundamentally reshape organizations. For example, organizational intelligence systems have become versatile to accumulate internal information and environmental changes, utilizing the insights that emerge by the transformation of data with the knowledge of the strategic value. Moreover, this book aims to show that organizational resilience is linked to organizational competitiveness and robustness via organizational intelligence and knowledge, information and data analytics for organizational intelligence competences and capabilities.

The amount of data collected by modern industrial, government, and academic organizations has been increasing exponentially and will continue to grow at an accelerating rate for the foreseeable future. At companies across all industries, servers are overflowing with usage logs, message streams, transaction records, sensor data, business operations records, and mobile device data. Effectively analyzing these massive collections of data (“big data”) can create significant value for the world economy by enhancing productivity, increasing efficiency, and delivering more value to consumers. The need to convert raw data into useful information has led to the development of advanced and unique data storage, management, analysis, and visualization technologies, especially over the last decade. This monograph is an attempt to cover the design principles and core features of systems for analyzing very large datasets for business purposes. In particular, we organize systems into four main categories based on major and distinctive technological innovations. Parallel databases dating back to 1980s have added techniques like columnar data storage and processing, while new distributed platforms like MapReduce have been developed. Other innovations aimed at creating alternative system architectures for more generalized dataflow applications. Finally, the growing demand for interactive analytics has led to the emergence of a new class of systems that combine analytical and transactional capabilities.

Various data sources are available in the era of Big Data to increase business understanding. For example, price predictions based on news ticker offers a broad range of valuable information. An automatic analysis is able to support traders in their daily business to maximize profits. But, only little is known about this topic, yet. In cooperation with a globally acting company, we developed a generalizable approach to use news tickers for price trend forecasts. First, we realized that the effect on prices by news tickers is complex to identify. Second, irrelevant tickers decrease the performance. Several approaches are evaluated to identify relevant articles in an automatic fashion, whereby the functionality is demonstrated in two different case studies. The results are practicable. Our research contributes to the discussion about business analytics, business cases, and their realization. It can be applied in any domain where important events have to be considered instantly.

The purpose of this study is to examine marketing analytics with female-owned business enterprises (FBEs). The researcher wanted to measure both internal firm behavior (customer influence) and external firm behavior (market influence) in this study. The primary focus of the research is to examine customer behavior and market behavior patterns in FBEs. We wanted to see if we predict firm behavior with FBEs based on four analytics. We tested a theoretical model based on the four different marketing analytics: (a) Customer Turnover Analytic, (b) Customer Credit Analytic, (c) Market Potential Analytic, and (d) Competition and Economics Analytic. A quantitative methodology was used to examine the data collected from the businesses. The findings of the study revealed the analytics: (a) Customer Turnover, (b) Customer Credit, and (c) Market Potential proved to be significant in both customer behavior and marketing behavior in the female-owned business ventures.

In this chapter, we revisit some of the foundations of the strategy formulation process. Specifically, we concentrate on the information acquisition and processing process. This chapter looks at the strategic behavior of internationalized enterprises on three fronts: on the kind of information they select about the foreign market, on the sources of information they use, and on the use of relevant software to organize and exploit these information. By revisiting one by one the different stages of strategy formulation for this process, we understand whether and how much strategy development has been reshaped due to contemporary technological and contextual evolutions. The results presented, representing a pragmatic rather than theoretic approach, are expected to decrease the existing vagueness in the field and to provide more clear answers on the real practices used from managers and entrepreneurs so as to develop successful strategies in foreign markets.

While open data as a phenomenon is rapidly growing up, innovation through open data is still less than expected. Research has shown that in spite of emerging new businesses models, private sector stakeholders are struggling to generate monetary income from open data. This is worrying as open data initiatives might not be sustained if there no evidence of value generation through external use of the data. We suggest that insights from two established theories, real options theory and theory of two-sided markets, might help us create a more coherent picture of the complex relationships between innovation and value generation in the open data ecosystem, and even resolve what we call the open data value paradox. We propose that governments, which openly publish data, are providing private sector stakeholders with the equivalent of a real option. By conceptualizing the uncertain or serendipitous value of open government data as option value, we might be able to stimulate activity and investment in the open data ecosystem. Moreover, we propose that by utilizing two-sided markets type of business models, private companies can use the data as a resource to provide free information and by capitalizing on the resulting positive network externalities, generate monetary income as well. Finally, we propose that governments should provide the necessary nourishment to this ecosystem in order to stimulate the generation of sustainable value.

Whether and how “sustainable business models” effectively support sustainable development is not just a matter of design but also of the measurability and manageability of business model effects. While the interrelations between organisations’ sustainability performance and their business models is discussed in an increasing number of academic and practice publications, appropriate management approaches for the deliberate assessment and management of business models and their expected contributions to a sustainable development of the natural environment and human society are currently not available. Therefore, this chapter discusses this research gap and proposes a conceptual framework for sustainability oriented business model assessments.

In today’s “Big Data” era, data-driven decisions can create and sustain competitive advantage. The travel and tourism sector with over a billion International Visitor Arrivals annually spending over $1 trillion generates a tremendous amount of data. PATAmPOWER, a Data as a Service (DaaS) software platform, was created to aggregate data about the Asia Pacific visitor economy, which includes indicators about visitor arrivals, origin markets, expenditure, accommodation, aviation, digital engagement and forecasts. Developed as a membership benefit by the Pacific Asia Travel Association (PATA), PATAmPOWER makes data available on demand, 24/7 in a one-stop-shop environment, obtained from credible and trusted sources, to help improve productivity, provide immediate insights and ultimately enable faster and smarter decisions. PATAmPOWER is interactive and user-friendly enabling the dynamic selection of indicators that users can download, export and share. The future value can be scaled to Software as a Service (SaaS) by creating customized data platforms.

This chapter explores critical insights into the process of change in today’s fast-paced competitive environment. The chapter focuses on significant drivers of change and assesses their impact on the external environment of modern companies. How companies will react to all these changes imposed by a number of factors is a subject that is going to be discussed in the following lines. The market reaction to these changes is examined in an attempt to predict future implications and highlight the importance of planning for modern organizations in order to remain competitive in today’s globalized, highly competitive environment. The Structure-Conduct-Performance model, which is a road map for identifying the factors that determine the competitiveness of a market, is applied in order to analyze the behavior of firms and their reactions to changes. Furthermore, Porter’s five forces model is discussed. This model helps to define strategic segment boundaries and reveal insights into the key forces operating in the competitive marketplace. Finally, different market structures are analyzed in order to assess the impact of changes on these markets and the way that the markets are capable of correcting any failures or misallocations of resources available. In this respect, the strategies needed for driving organizational change require an essential set of skills and competencies at the core of the business function.

The purpose of this chapter contains three main points: (1) to present the relation between sustainability and excellence of the higher education institutions (HEIs), (2) to show their impact on the sustainability of enterprise excellence, and (3) to outline the role of the EU Operational Program ‘Education for Competitiveness’ in Czech Republic concerning (1) and (2). The chapter consists of several sections. The first section outlines the substance of sustainable development. The second section deals with sustainable development of higher education in general and in the Czech Republic in particular. The third section illustrates this using the example of the University of Economics in Prague. The fourth section presents the European Union’s Operational Program ‘Education for Competitiveness’ and the final fifth section adds several examples of HEI innovation process.

There is an irreversible trend toward the criticality of big data analytics’ capability and exercise thereof so that rather than exclusive use of traditional ‘data-driven decision-making’ approaches, sustainable—organizational—excellence will often demand focus on more computationally intensive data and information generation, collection, extraction, and interpretive procedures that—when added to traditional data-driven methods—yield the area of sustainable enterprise excellence referred to as enterprise intelligence and analytics.