Drivers and barriers of mobile travel and tourism service adoption : a study of individual perceptions and business model development in a travel and tourism context

The Travel and Tourism field is undergoing changes due to the rapid development of information technology and digital services. Online travel has profoundly changed the way travel and tourism organizations interact with their customers. Mobile technology such as mobile services for pocket devices (e.g. mobile phones) has the potential to take this development even further. Nevertheless, many issues have been highlighted since the early days of mobile services development (e.g. the lack of relevance, ease of use of many services). However, the wide adoption of smartphones and the mobile Internet in many countries as well as the formation of so-called ecosystems between vendors of mobile technology indicate that many of these issues have been overcome. Also when looking at the numbers of downloaded applications related to travel in application stores like Google Play, it seems obvious that mobile travel and tourism services are adopted and used by many individuals. However, as business is expected to start booming in the mobile era, many issues have a tendency to be overlooked. Travelers are generally on the go and thus services that work effectively in mobile settings (e.g. during a trip) are essential. Hence, the individuals’ perceived drivers and barriers to use mobile travel and tourism services in on-site or during trip settings seem particularly valuable to understand; thus this is one primary aim of the thesis. We are, however, also interested in understanding different types of mobile travel service users. Individuals may indeed be very different in their propensity to adopt and use technology based innovations (services). Research is also switching more from investigating issues of mobile service development to understanding individuals’ usage patterns of mobile services. But designing new mobile services may be a complex matter from a service provider perspective. Hence, our secondary aim is to provide insights into drivers and barriers of mobile travel and tourism service development from a holistic business model perspective. To accomplish the research objectives seven different studies have been conducted over a time period from 2002 – 2013. The studies are founded on and contribute to theories within diffusion of innovations, technology acceptance, value creation, user experience and business model development. Several different research methods are utilized: surveys, field and laboratory experiments and action research. The findings suggest that a successful mobile travel and tourism service is a service which supports one or several mobile motives (needs) of individuals such as spontaneous needs, time-critical arrangements, efficiency ambitions, mobility related needs (location features) and entertainment needs. The service could be customized to support travelers’ style of traveling (e.g. organized travel or independent travel) and should be easy to use, especially easy to take into use (access, install and learn) during a trip, without causing security concerns and/or financial risks for the user. In fact, the findings suggest that the most prominent barrier to the use of mobile travel and tourism services during a trip is an individual’s perceived financial cost (entry costs and usage costs). It should, however, be noted that regulations are put in place in the EU regarding data roaming prices between European countries and national telecom operators are starting to see ‘international data subscriptions’ as a sales advantage (e.g. Finnish Sonera provides a data subscription in the Baltic and Nordic region at the same price as in Finland), which will enhance the adoption of mobile travel and tourism services also in international contexts. In order to speed up the adoption rate travel service providers could consider e.g. more local initiatives of free Wi-Fi networks, development of services that can be used, at least to some extent, in an offline mode (do not require costly network access during a trip) and cooperation with telecom operators (e.g. lower usage costs for travelers who use specific mobile services or travel with specific vendors). Furthermore, based on a developed framework for user experience of mobile trip arrangements, the results show that a well-designed mobile site and/or native application, which preferably supports integration with other mobile services, is a must for true mobile presence. In fact, travel service providers who want to build a relationship with their customers need to consider a downloadable native application, but in order to be found through the mobile channel and make contact with potential new customers, a mobile website should be available. Moreover, we have made a first attempt with cluster analysis to identify user categories of mobile services in a travel and tourism context. The following four categories were identified: info-seekers, checkers, bookers and all-rounders. For example “all-rounders”, represented primarily by individuals who use their pocket device for almost any of the investigated mobile travel services, constituted primarily of 23 to 50 year old males with high travel frequency and great online experience. The results also indicate that travel service providers will increasingly become multi-channel providers. To manage multiple online channels, closely integrated and hybrid online platforms for different devices, supporting all steps in a traveler process should be considered. It could be useful for travel service providers to focus more on developing browser-based mobile services (HTML5-solutions) than native applications that work only with specific operating systems and for specific devices. Based on an action research study and utilizing a holistic business model framework called STOF we found that HTML5 as an emerging platform, at least for now, has some limitations regarding the development of the user experience and monetizing the application. In fact, a native application store (e.g. Google Play) may be a key mediator in the adoption of mobile travel and tourism services both from a traveler and a service provider perspective. Moreover, it must be remembered that many device and mobile operating system developers want service providers to specifically create services for their platforms and see native applications as a strategic advantage to sell more devices of a certain kind. The mobile telecom industry has moved into a battle of ecosystems where device makers, developers of operating systems and service developers are to some extent forced to choose their development platforms.

A platform for assessing the current state of data repositories in Korea, Japan and China

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

On advancing business intelligence in the electricity retail market

In recent decades, business intelligence (BI) has gained momentum in real-world practice. At the same time, business intelligence has evolved as an important research subject of Information Systems (IS) within the decision support domain. Today’s growing competitive pressure in business has led to increased needs for real-time analytics, i.e., so called real-time BI or operational BI. This is especially true with respect to the electricity production, transmission, distribution, and retail business since the law of physics determines that electricity as a commodity is nearly impossible to be stored economically, and therefore demand-supply needs to be constantly in balance. The current power sector is subject to complex changes, innovation opportunities, and technical and regulatory constraints. These range from low carbon transition, renewable energy sources (RES) development, market design to new technologies (e.g., smart metering, smart grids, electric vehicles, etc.), and new independent power producers (e.g., commercial buildings or households with rooftop solar panel installments, a.k.a. Distributed Generation). Among them, the ongoing deployment of Advanced Metering Infrastructure (AMI) has profound impacts on the electricity retail market. From the view point of BI research, the AMI is enabling real-time or near real-time analytics in the electricity retail business. Following Design Science Research (DSR) paradigm in the IS field, this research presents four aspects of BI for efficient pricing in a competitive electricity retail market: (i) visual data-mining based descriptive analytics, namely electricity consumption profiling, for pricing decision-making support; (ii) real-time BI enterprise architecture for enhancing management’s capacity on real-time decision-making; (iii) prescriptive analytics through agent-based modeling for price-responsive demand simulation; (iv) visual data-mining application for electricity distribution benchmarking. Even though this study is from the perspective of the European electricity industry, particularly focused on Finland and Estonia, the BI approaches investigated can: (i) provide managerial implications to support the utility’s pricing decision-making; (ii) add empirical knowledge to the landscape of BI research; (iii) be transferred to a wide body of practice in the power sector and BI research community.

The role of collective intelligence in crowdsourcing innovation

Social insects are known for their ability to display swarm intelligence, where the cognitive capabilities of the collective surpass those of the individuals forming it by orders of magnitude. The rise of crowdsourcing in recent years has sparked speculation as to whether something similar might be taking place on crowdsourcing sites, where hundreds or thousands of people interact with each other. The phenomenon has been dubbed collective intelligence. This thesis focuses on exploring the role of collective intelligence in crowdsourcing innovations. The task is approached through three research questions: 1) what is collective intelligence; 2) how is collective intelligence manifested in websites involved in crowdsourcing innovation; and 3) how important is collective intelligence for the functioning of the crowdsourcing sites. After developing a theoretical framework for collective intelligence, a multiple case study is conducted using an ethnographic data collection approach for the most part. A variety of qualitative, quantitative and simulation modelling methods are used to analyse the complex phenomenon from several theoretical viewpoints or ‘lenses’. Two possible manifestations of collective intelligence are identified: discussion, typical of web forums; and the wisdom of crowds in evaluating crowd submissions to websites. However, neither of these appears to be specific to crowdsourcing or critical for the functioning of the sites. Collective intelligence appears to play only a minor role in the cases investigated here. In addition, this thesis shows that feedback loops, which are found in all the cases investigated, reduce the accuracy of the crowd’s evaluations when a count of votes is used for aggregation.

Data analysis tools and methods for DNA microarray and high-throughput sequencing data

The recent rapid development of biotechnological approaches has enabled the production of large whole genome level biological data sets. In order to handle thesedata sets, reliable and efficient automated tools and methods for data processingand result interpretation are required. Bioinformatics, as the field of studying andprocessing biological data, tries to answer this need by combining methods and approaches across computer science, statistics, mathematics and engineering to studyand process biological data. The need is also increasing for tools that can be used by the biological researchers themselves who may not have a strong statistical or computational background, which requires creating tools and pipelines with intuitive user interfaces, robust analysis workflows and strong emphasis on result reportingand visualization. Within this thesis, several data analysis tools and methods have been developed for analyzing high-throughput biological data sets. These approaches, coveringseveral aspects of high-throughput data analysis, are specifically aimed for gene expression and genotyping data although in principle they are suitable for analyzing other data types as well. Coherent handling of the data across the various data analysis steps is highly important in order to ensure robust and reliable results. Thus,robust data analysis workflows are also described, putting the developed tools andmethods into a wider context. The choice of the correct analysis method may also depend on the properties of the specific data setandthereforeguidelinesforchoosing an optimal method are given. The data analysis tools, methods and workflows developed within this thesis have been applied to several research studies, of which two representative examplesare included in the thesis. The first study focuses on spermatogenesis in murinetestis and the second one examines cell lineage specification in mouse embryonicstem cells.