Integration of innovative users as source of service innovations

In this study we research user integration in the German service industry. Goal is to explore the industrial practice with regard to four dimensions of user integration, i.e., why, whom, how, and how successful the service industry integrates to find novel service ideas. Data is collected from a large-scale survey sent out to 2,905 service companies and posted in various user groups related to service innovations. Drawing from data gathered from 301 respondents in our study, we present explorative findings for each distinct dimension of user integration. To better understand the interrelation of these dimensions, we also create a structural equation model using partial least square for estimation of direction and strength of relationships between those. Results show that service companies like companies from other industries actively pursue the development of radical innovations. We find that service companies do not integrate users by random. Instead a service company's level of importance for radical innovation significantly determines both, choice of users integrated as well as choice of integration instruments deployed. In our study we can also show, that many of the beliefs brought forward by service companies for not integrating users cannot be sustained in the light of our findings. We can demonstrate that user contributions provide true value to those companies integrating the latter, and also that using tools which are considered easy and versatile to apply can still have a significant impact on the attractiveness of user ideas. --

Customer Complaints Auto-assignment using Machine Learning Algorithms

Managing customer complaints is a very challenging activity, especially when it comes to a government entity like Roads a Transport Authority (RTA) that manages the roads infrastructure and services. RTA for the management of the customer complaints requires a sophisticated and intelligent responsive system for the large volume of cases and calls, which result in needs to a lot of human resources to receive, record and handle customer calls and cases. The key customer-save course of action is the complaints-handling process. Customers who complain to service providers and are well treated by the process are less likely to churn than customers who have no cause for complaint. In other words, a well-designed, easy-to-engage and the responsive complaints-handling process can build loyalty. (Buttle, 2016) Knowing how essential to have a well-designed complaint management system, organizations work to leverage the advantages of technologies enhancement to efficiently manage customer cases with minimal resources utilization. The critical success for that is the utilization of the most valuable asset to the organization (customer complaint data). The data gets its increasing values with the advancement of data analytics and its application in recent year. For many organizations, the data analytics usage does not go beyond the traditional descriptive analysis that describes what happened and take the necessary corrective action. Although there were a lot of attempts and research to utilize machine learning algorithms to classify customer complaints, most of falls in the area of sentiments analysis or high level topic molding identifying customer feelings or deciding what topic he/she is talking or complaining about. Actually, organization such RTA needs more that, it is the time to optimize the benefit of using Artificial Intelligence power in operational system beyond the high level text classification. The real need for RTA is to equip complaint management system with AI algorithms that help in classifying and auto-assigning the complaint to the respective department based directly without the need for human intervention. The advantage RTA has is that, it has implemented an important change in complaint management system by classifying (labeling) most of the common scenarios of complaints based on the historical data which paves the way to the use of AI-Text Classification algorithms. This project is an attempt to extend the benefits of data analytics to help not only in understanding the customer\u27s pain points but also to help in managing customer complaints end to end using the application of machine learning and artificial intelligence

Market-Driven Management and Global Economies of Scale

In market-driven management, dominated by competitive customer value, economies of intensity of sharing, or synergies, can be linked to global economies of scale. A market-driven management strategy radically alters the interpretation perspective of the issue of synergies. In market-driven management, synergies or economies of intensity of sharing do not derive from 'pooling resources' in order to saturate manufacturing capacity better, but from exploiting a store of skills to support different businesses. The cases presented (Geox and Yamamay) regard companies that can be defined as competitive customer value oriented, partly as a result of their capacity to exploit economies of intensity of sharing, by synergetic recourse to their basic skills.Market-Driven Management; Global Economies of Scale; Over- Supply; Competitive Customer Value; The Yamamay Case; The Geox Case

Mergers and Innovation in the Pharmaceutical Market

The U.S. pharmaceutical industry has experienced in recent years two dramatic changes: stagnation in the growth of new molecular entities approved for marketing, and a wave of mergers linking inter alia some of the largest companies. This paper explores possible links between these two phenomena and proposes alternative approach to merger policy. It points to the high degree of uncertainty encountered in the discovery and development of new pharmaceutical entities and shows how optimal strategies entail the pursue of parallel research and development paths. Uncertainties afflict both success rates and financial gains contingent upon success. A new model simulating optimal strategies given prevalent market uncertainties is presented. Parallelism can be sustained both within individual companies' R&D programs and across competing companies. The paper points to data showing little parallelism of programs within companies and argues that inter-company mergers jeopardize desirable parallelism across companies.

Eliciting Expertise

Since the last edition of this book there have been rapid developments in the use and exploitation of formally elicited knowledge. Previously, (Shadbolt and Burton, 1995) the emphasis was on eliciting knowledge for the purpose of building expert or knowledge-based systems. These systems are computer programs intended to solve real-world problems, achieving the same level of accuracy as human experts. Knowledge engineering is the discipline that has evolved to support the whole process of specifying, developing and deploying knowledge-based systems (Schreiber et al., 2000) This chapter will discuss the problem of knowledge elicitation for knowledge intensive systems in general

Text Analytics: the convergence of Big Data and Artificial Intelligence

The analysis of the text content in emails, blogs, tweets, forums and other forms of textual communication constitutes what we call text analytics. Text analytics is applicable to most industries: it can help analyze millions of emails; you can analyze customers’ comments and questions in forums; you can perform sentiment analysis using text analytics by measuring positive or negative perceptions of a company, brand, or product. Text Analytics has also been called text mining, and is a subcategory of the Natural Language Processing (NLP) field, which is one of the founding branches of Artificial Intelligence, back in the 1950s, when an interest in understanding text originally developed. Currently Text Analytics is often considered as the next step in Big Data analysis. Text Analytics has a number of subdivisions: Information Extraction, Named Entity Recognition, Semantic Web annotated domain’s representation, and many more. Several techniques are currently used and some of them have gained a lot of attention, such as Machine Learning, to show a semisupervised enhancement of systems, but they also present a number of limitations which make them not always the only or the best choice. We conclude with current and near future applications of Text Analytics

Creating Business and Social Value: The Asian Way to Integrate CSR into Business Strategies

Launched in July 2000, the United Nations Global Compact is both a policy platform and a practical framework for companies that are committed to sustainability and responsible business practices. The Global Compact is a strategic policy initiative for businesses that are committed to aligning their operations and strategies with ten universally accepted principles in the areas of human rights, labour, environment, and anti-corruption. The Global Compact has gained great attraction around the world, helping it to become the largest global corporate citizenship initiative with 6,985 signatories- 5,206 from business and 1,779 from civil society and other non-business organizations - based in over 135 countries.United Nations, Global compact, human rights, labour, environment, anti-corruption

The changing roles and identities of library and information services staff

A review of the changing roles of library, IT and e-learning staff from 1960 to date. Examines convergence and blurring of roles and what constitutes professional identity

Visas, Inc: Corporate Control and Policy Incoherence in the U.S. Temporary Foreign Labor System

This report provides the first comprehensive analysis of the many visas that employers use and misuse to bring foreign workers into the U.S. in every field, from low-wage jobs in agriculture and domestic work, to specialty occupations in health care, education or information technology. The system is vulnerable to misuse by employers who use foreign labor to undermine established wages and working conditions in the U.S. The result is that U.S. workers are losing out on opportunities, and foreign workers have almost no protection from exploitation, unpaid wages, unsafe conditions and even trafficking and other abuses