Assessment of process capabilities in transition to a data-driven organisation: A multidisciplinary approach

The ability to leverage data science can generate valuable insights and actions in organisations by enhancing data-driven decision-making to find optimal solutions based on complex business parameters and data. However, only a small percentage of the organisations can successfully obtain a business value from their investments due to a lack of organisational management, alignment, and culture. Becoming a data-driven organisation requires an organisational change that should be managed and fostered from a holistic multidisciplinary perspective. Accordingly, this study seeks to address these problems by developing the Data Drivenness Process Capability Determination Model (DDPCDM) based on the ISO/IEC 330xx family of standards. The proposed model enables organisations to determine their current management capabilities, derivation of a gap analysis, and the creation of a comprehensive roadmap for improvement in a structured and standardised way. DDPCDM comprises two main dimensions: process and capability. The process dimension consists of five organisational management processes: change management, skill and talent management, strategic alignment, organisational learning, and sponsorship and portfolio management. The capability dimension embraces six levels, from incomplete to innovating. The applicability and usability of DDPCDM are also evaluated by conducting a multiple-case study in two organisations. The results reveal that the proposed model is able to evaluate the strengths and weaknesses of an organisation in adopting, managing, and fostering the transition to a data-driven organisation and providing a roadmap for continuously improving the data-drivenness of organisations

MobileCDP: A mobile framework for the consumer decision process

The consumer decision process is a widely accepted model covering consumer activities, and accordingly contains five interrelated stages: problem recognition, information search, evaluation of alternatives, purchase, and post-purchase evaluation. In order to help consumers deal with challenges associated with all these stages, mobile information systems bring significant capabilities, as in other application domains. However, related prior research is mostly restricted to the individual stages of the process. Since the stages are interrelated, and the data collected in one are also valuable for another, we propose a mobile framework designed to provide assistance in all stages of the Consumer Decision Process, named MobileCDP. A prototype is also implemented and evaluated to show the applicability of the framework. Experiments show that the functions provided by the prototype are useful, well integrated, and easy to use. Moreover, statistical analysis of the results proves that the prototype reduces time, costs, and cognitive effort of the user

Comparison of approaches for mobile document image analysis using server supported smartphones

With the recent advances in mobile technologies, new capabilities are emerging, such as mobile document image analysis. However, mobile phones are still less powerful than servers, and they have some resource limitations. One approach to overcome these limitations is performing resource-intensive processes of the application on remote servers. In mobile document image analysis, the most resource consuming process is the Optical Character Recognition (OCR) process, which is used to extract text in mobile phone captured images. In this study, our goal is to compare the in-phone and the remote server processing approaches for mobile document image analysis in order to explore their trade-offs. For the in-phone approach, all processes required for mobile document image analysis run on the mobile phone. On the other hand, in the remote-server approach, core OCR process runs on the remote server and other processes run on the mobile phone. Results of the experiments show that the remote server approach is considerably faster than the in-phone approach in terms of OCR time, but adds extra delays such as network delay. Since compression and downscaling of images significantly reduce file sizes and extra delays, the remote server approach overall outperforms the in-phone approach in terms of selected speed and correct recognition metrics, if the gain in OCR time compensates for the extra delays. According to the results of the experiments, using the most preferable settings, the remote server approach performs better than the in-phone approach in terms of speed and acceptable correct recognition metrics

Impact of Pervasive Computing on Service Design in Hospitality

The economies of developed countries have significantly transformed from manufacturing based to service based, where the hospitality industry plays an important role. Within this context, new service design and service innovation are vital for maintaining competitive advantage. Recently, pervasive computing has emerged as a key enabler in various industries in support of service innovation by facilitating the design of new services. This study aims to investigate the benefits provided by pervasive computing in the hospitality domain for improved customer satisfaction, profitability, and operational efficiencies. First, an overview of pervasive technologies is presented. When reviewing existing applications in the hospitality domain, it is observed that most of the pervasive applications currently found in the hospitality market are primarily focused on the RFID technology, and managed by disconnected systems. Our aim is to propose an integrated pervasive computing framework supporting innovative services incorporating pervasive technologies for the hotel industry, and not being limited to RFID applications. Via the analysis of the framework, changes in business processes are exposed, and improvements obtained through the collected data on the framework integrated with the enterprise systems are discussed

A context-aware and workflow-based framework for pervasive environments

Using currently available infrastructure in pervasive environments it is possible to provide intelligent mechanisms that offer people help and guidance for organizing their daily activities. In this study, a framework providing such capabilities is proposed. This framework allows users to model their daily activities in the form of workflows, which are adaptable at run-time according to context information collected in pervasive environments. A workflow engine is used for modelling and management of workflows, while a separate rule engine with complex event processing (CEP) capability is incorporated into the framework for enhancing workflow adaptation and execution. The adaptation model in the framework allows for the modelling of activities in a hierarchical manner, from high level abstract activities to more detailed ones. An event-driven architecture (EDA) is utilized for loosely coupled interaction between the workflow engine and the rule engine, allowing these engines and other context sources to exchange data among themselves. Moreover, the EDA allows incorporation of context information into the workflow models without modifying the workflow language. A level of automation higher than the level supported by workflows is proposed by processing events in pervasive environments using CEP. A prototype implementation is developed and the framework is evaluated with some real life examples that demonstrate its applicability