The aim of this paper is to examine challenges that organizations face when they start to deal with quality of customer data more seriously in order to manage their customer relationships better. Research extracted from the literature review has identified some problems with the quality of customer data as well as suggestions for their solutions. The author found that challenges regarding the quality of data used in customer relationship management are reflected in: decentralized data storage, inconsistencies in input and storage, inadequate integration of different data sources, different data defects, and their tendency in quality deterioration over time. In addition, problems have been identified in the high costs of maintaining data quality, as well as new challenges in the form of big data and open data. Possible improvement solutions have been suggested through a number of tools and frameworks by different authors

Benchmarking is a strategic management tool that can help to gain competitive advantage, but the question is how to decide the relevant practice exemplars to be used as role models. Data Envelopment Analysis (DEA) is a very helpful method for tracking corresponding benchmarks, but the question remains of how to record them when performance is fluctuating and unstable, as is the case in a transition period to an open market. To address this issue a new DEA-based tool is proposed, the Corresponding Benchmark Matrix (CBM), which helps to reveal ?leader? countries and the most suitable benchmarks for less successful countries. The approach is illustrated for telecommunications in 22 European Bank for Reconstruction and Development (EBRD) countries.

Car sharing is a specific business model that allows a new form of personal mobility. University students, generally very receptive to the concept of a sharing economy, are recognized as a prospective customer group for car sharing operators. This paper proposes an ex ante analysis that aims to reveal how students from an area where car sharing is underdeveloped perceive this mobility option. University students in Belgrade were asked to state their preferences regarding a mix of attributes and levels replicating service design from current practice. Preferences for particular service attributes were explored using stated preference survey and Choice-Based Conjoint analysis, while further preference-based segmentation was obtained using the Partitioning Around Medoids method. The contribution of this work is that it delivers findings on an emerging car-sharing market where there is very little research on user profiles. From a methodological point of view, we form distinctive customer clusters based on the uniformity of their preferences. By being aware of users’ prior expectations, service providers can determine their operational priorities more easily when unlocking the market. The paper outlines both the similarities and differences between students in an emerging market and their counterparts in more developed countries. Our findings reveal that the student population is homogeneous regarding critical aspects of service adoption like cost, distance to vehicles, and parking convenience. Specific service attributes such as the pricing scheme and keeping vehicles clean are found to be issues of peculiar interest in our study market. Although our proposed approach to shaping user preferences was developed for car sharing analysis it is applicable to other service-oriented businesses in the initiation phase.

The continuous development of information and telecommunication technologies has led to the possibility of transferring data and information between people in real time, in just a few seconds, which has led to the emergence of new approaches to data collection. An example is crowdsourcing (networked mass of people), which involves collecting a large amount of defined data from a large number (mass) of people through the Internet, that is, embedded sensors in smart devices. These are most often mobile phones and then it is about the concept of mobile crowdsourcing – which is more widely accepted under the term crowdsensing. This paper shows how smart devices (mobile phones or tablets) can collect vibration data that occur while driving in road traffic – and still be used to detect irregularities in road infrastructure (potholes, bumps, etc.) in real conditions.

The aim of this paper is to highlight the importance knowledge management and business processes management has in the modern economy. The paper reflects on the impact of knowledge management on business processes and on the improvement of the management thereof, on the basis of the relevant literature. The related research shows a mutual connection between these two concepts and points to the potential benefits of their successful integration. This paper is a framework overview of all the positive effects of knowledge management on business process management in the organization, which allows for better understanding and use of knowledge for more effective business process management.