Understanding user differences in open-source workflow management system usage intentions

Open-source software systems have become a viable alternative to proprietary systems. We collected data on the usage of an open-source workflow management system developed by a university research group, and examined this data with a focus on how three different user cohorts – students, academics and industry professionals – develop behavioral intentions to use the system. Building upon a framework of motivational components, we examined the group differences in extrinsic versus intrinsic motivations on continued usage intentions. Our study provides a detailed understanding of the use of open-source workflow management systems in different user communities. Moreover, it discusses implications for the provision of workflow management systems, the user-specific management of open-source systems and the development of services in the wider user community.

User-representative feature selection for keystroke dynamics

Continuous user authentication with keystroke dynamics uses characters sequences as features. Since users can type characters in any order, it is imperative to find character sequences (n-graphs) that are representative of user typing behavior. The contemporary feature selection approaches do not guarantee selecting frequently-typed features which may cause less accurate statistical user-representation. Furthermore, the selected features do not inherently reflect user typing behavior. We propose four statistical based feature selection techniques that mitigate limitations of existing approaches. The first technique selects the most frequently occurring features. The other three consider different user typing behaviors by selecting: n-graphs that are typed quickly; n-graphs that are typed with consistent time; and n-graphs that have large time variance among users. We use Gunetti’s keystroke dataset and k-means clustering algorithm for our experiments. The results show that among the proposed techniques, the most-frequent feature selection technique can effectively find user representative features. We further substantiate our results by comparing the most-frequent feature selection technique with three existing approaches (popular Italian words, common n-graphs, and least frequent ngraphs). We find that it performs better than the existing approaches after selecting a certain number of most-frequent n-graphs.

Quality delivery of mobile video : in-depth understanding of user requirements

The increase of powerful mobile devices has accelerated the demand for mobile videos. Previous studies in mobile video have focused on understanding of mobile video usage, improvement of video quality, and user interface design in video browsing. However, research focusing on a deep understanding of users’ needs for a pleasing quality delivery of mobile video is lacking. In particular, what quality-delivery mode users prefer and what information relevant to video quality they need requires attention. This paper presents a qualitative interview study with 38 participants to gain an insight into three aspects: influencing factors of user-desired video quality, user-preferred quality-delivery modes, and user-required interaction information of mobile video. The results show that user requirements for video quality are related to personal preference, technology background and video viewing experience, and the preferred quality-delivery mode and interactive mode are diverse. These complex user requirements call for flexible and personalised quality delivery and interaction of mobile video.

Open source innovation in physical products : advantages and disadvantages, a corporate perspective

A better understanding of Open Source Innovation in Physical Product (OSIP) might allow project managers to mitigate risks associated with this innovation model and process, while developing the right strategies to maximise OSIP outputs. In the software industry, firms have been highly successful using Open Source Innovation (OSI) strategies. However, OSI in the physical world has not been studied leading to the research question: What advantages and disadvantages do organisations incur from using OSI in physical products? An exploratory research methodology supported by thirteen semi-structured interviews helped us build a seven-theme framework to categorise advantages and disadvantages elements linked with the use of OSIP. In addition, factors impacting advantage and disadvantage elements for firms using OSIP were identified as: „h Degree of openness in OSIP projects; „h Time of release of OSIP in the public domain; „h Use of Open Source Innovation in Software (OSIS) in conjunction with OSIP; „h Project management elements (Project oversight, scope and modularity); „h Firms. Corporate Social Responsibility (CSR) values; „h Value of the OSIP project to the community. This thesis makes a contribution to the body of innovation theory by identifying advantages and disadvantages elements of OSIP. Then, from a contingency perspective it identifies factors which enhance or decrease advantages, or mitigate/ or increase disadvantages of OSIP. In the end, the research clarifies the understanding of OSI by clearly setting OSIP apart from OSIS. The main practical contribution of this paper is to provide manager with a framework to better understand OSIP as well as providing a model, which identifies contingency factors increasing advantage and decreasing disadvantage. Overall, the research allows managers to make informed decisions about when they can use OSIP and how they can develop strategies to make OSIP a viable proposition. In addition, this paper demonstrates that advantages identified in OSIS cannot all be transferred to OSIP, thus OSIP decisions should not be based upon OSIS knowledge.

Using visuals to explore the contextual aspects of user-product interactions

Experience underlies all kinds of human knowledge and it is dependent on context. People’s experience within a particular context-of-use determines how they interact with products. Methods employed in this research to elicit human experience have included the use of visuals. This paper describes two empirical studies that employed visual representation of concepts as a means to explore the experiential and contextual component of user- product interactions. One study employed visuals that the participants produced during the study. The other employed visuals that the researcher used as prompts during a focus group session. This paper demonstrates that using visuals in design research is valuable for exploring and understanding the contextual aspects of human experience and its influence on people’s concepts of product use.

General characteristics of anticipated user experience (AUX) with interactive products

Providing a positive user experience (UX) has become the key differentiator for products to win a competition in mature markets. To ensure that a product will support enjoyable experiences for its users, assessment of UX should be conducted early during the design and development process. However, most UX frameworks and evaluation techniques focus on understanding and assessing user’s experience with functional prototypes or existing products. This situation delays UX assessment until the late phases of product development which may result in costly design modifications and less desirable products. A qualitative study was conducted to investigate anticipated user experience (AUX) to address this issue. Twenty pairs of participants were asked to imagine an interactive product, draw their product concept, and anticipate their interactions and experiences with it. The data was analyzed to identify general characteristics of AUX. We found that while positive AUX was mostly related to an imagined/desired product, negative AUX was mainly associated with existing products. It was evident that the pragmatic quality of product was fundamental, and significantly influenced user’s anticipated experiences. Furthermore, the hedonic quality of product received more focus in positive than negative AUX. The results also showed that context, user profile, experiential knowledge, and anticipated emotion could be reflected in AUX. The understanding of AUX will help product designers to better foresee the users’ underlying needs and to focus on the most important aspects of their positive experiences, which in turn facilitates the designers to ensure pleasurable UX from the start of the design process.

Informed learning : a catalyst for change in theological libraries

This paper discusses the potential interest of informed learning as a catalyst for change in theological libraries. Informed learning is a label for the relational approach to information literacy and information literacy education. It was created to highlight the importance of simultaneous attention to both information and learning when we consider peoples’ experiences in their information rich lives. The paper explores the idea of informed learning, suggesting that serious attention to informed learning experiences may challenge our thinking about our role as information professionals and the ways in which we serve our clients. The paper then moves to explore our current understandings of informed learning in faith communities and suggests some ways in which theological librarians can work to build informed communities.

Risk-informed thinking in airports: the application of business continuity management in airport infrastructure

Airports worldwide represent key forms of critical infrastructure in addition to serving as nodes in the international aviation network. While the continued operation of airports is critical to the functioning of reliable air passenger and freight transportation, these infrastructure systems face a number of sources of disturbance that threaten their operational viability. Recent examples of high magnitude events include the eruption of Iceland’s Eyjafjallajokull volcano eruption (Folattau and Schofield 2010), the failure of multiple systems at the opening of Heathrow’s Terminal 5 (Brady and Davies 2010) and the Glasgow airport 2007 terrorist attack (Crichton 2008). While these newsworthy events do occur, a multitude of lower-level more common disturbances also have the potential to cause significant discontinuity to airport operations. Regional airports face a unique set of challenges, particularly in a nation like Australia where they serve to link otherwise remote and isolated communities to metropolitan hubs (Wheeler 2005), often without the resources and political attention received by larger capital city airports. This paper discusses conceptual relationships between Business Continuity Management (BCM) and High Reliability Theory, and proposes BCM as an appropriate risk-based management process to ensure continued airport operation in the face of uncertainty. In addition, it argues that that correctly implemented BCM can lead to highly reliable organisations. This is framed within the broader context of critical infrastructures and the need for adequate crisis management approaches suited to their unique requirements (Boin and McConnell 2007).

Individual user behaviour modelling for effective web recommendation

With the growth of the Web, E-commerce activities are also becoming popular. Product recommendation is an effective way of marketing a product to potential customers. Based on a user’s previous searches, most recommendation methods employ two dimensional models to find relevant items. Such items are then recommended to a user. Further too many irrelevant recommendations worsen the information overload problem for a user. This happens because such models based on vectors and matrices are unable to find the latent relationships that exist between users and searches. Identifying user behaviour is a complex process, and usually involves comparing searches made by him. In most of the cases traditional vector and matrix based methods are used to find prominent features as searched by a user. In this research we employ tensors to find relevant features as searched by users. Such relevant features are then used for making recommendations. Evaluation on real datasets show the effectiveness of such recommendations over vector and matrix based methods.

Classifying the user intent of web queries using k-means clustering

Purpose: Web search engines are frequently used by people to locate information on the Internet. However, not all queries have an informational goal. Instead of information, some people may be looking for specific web sites or may wish to conduct transactions with web services. This paper aims to focus on automatically classifying the different user intents behind web queries. Design/methodology/approach: For the research reported in this paper, 130,000 web search engine queries are categorized as informational, navigational, or transactional using a k-means clustering approach based on a variety of query traits. Findings: The research findings show that more than 75 percent of web queries (clustered into eight classifications) are informational in nature, with about 12 percent each for navigational and transactional. Results also show that web queries fall into eight clusters, six primarily informational, and one each of primarily transactional and navigational. Research limitations/implications: This study provides an important contribution to web search literature because it provides information about the goals of searchers and a method for automatically classifying the intents of the user queries. Automatic classification of user intent can lead to improved web search engines by tailoring results to specific user needs. Practical implications: The paper discusses how web search engines can use automatically classified user queries to provide more targeted and relevant results in web searching by implementing a real time classification method as presented in this research. Originality/value: This research investigates a new application of a method for automatically classifying the intent of user queries. There has been limited research to date on automatically classifying the user intent of web queries, even though the pay-off for web search engines can be quite beneficial. © Emerald Group Publishing Limited.

An ontology-based mining approach for user search intent discovery

Discovering proper search intents is a vi- tal process to return desired results. It is constantly a hot research topic regarding information retrieval in recent years. Existing methods are mainly limited by utilizing context-based mining, query expansion, and user profiling techniques, which are still suffering from the issue of ambiguity in search queries. In this pa- per, we introduce a novel ontology-based approach in terms of a world knowledge base in order to construct personalized ontologies for identifying adequate con- cept levels for matching user search intents. An iter- ative mining algorithm is designed for evaluating po- tential intents level by level until meeting the best re- sult. The propose-to-attempt approach is evaluated in a large volume RCV1 data set, and experimental results indicate a distinct improvement on top precision after compared with baseline models.

User behaviour modelling in a multi-dimensional environment for personalization and recommendation

Handling information overload online, from the user's point of view is a big challenge, especially when the number of websites is growing rapidly due to growth in e-commerce and other related activities. Personalization based on user needs is the key to solving the problem of information overload. Personalization methods help in identifying relevant information, which may be liked by a user. User profile and object profile are the important elements of a personalization system. When creating user and object profiles, most of the existing methods adopt two-dimensional similarity methods based on vector or matrix models in order to find inter-user and inter-object similarity. Moreover, for recommending similar objects to users, personalization systems use the users-users, items-items and users-items similarity measures. In most cases similarity measures such as Euclidian, Manhattan, cosine and many others based on vector or matrix methods are used to find the similarities. Web logs are high-dimensional datasets, consisting of multiple users, multiple searches with many attributes to each. Two-dimensional data analysis methods may often overlook latent relationships that may exist between users and items. In contrast to other studies, this thesis utilises tensors, the high-dimensional data models, to build user and object profiles and to find the inter-relationships between users-users and users-items. To create an improved personalized Web system, this thesis proposes to build three types of profiles: individual user, group users and object profiles utilising decomposition factors of tensor data models. A hybrid recommendation approach utilising group profiles (forming the basis of a collaborative filtering method) and object profiles (forming the basis of a content-based method) in conjunction with individual user profiles (forming the basis of a model based approach) is proposed for making effective recommendations. A tensor-based clustering method is proposed that utilises the outcomes of popular tensor decomposition techniques such as PARAFAC, Tucker and HOSVD to group similar instances. An individual user profile, showing the user's highest interest, is represented by the top dimension values, extracted from the component matrix obtained after tensor decomposition. A group profile, showing similar users and their highest interest, is built by clustering similar users based on tensor decomposed values. A group profile is represented by the top association rules (containing various unique object combinations) that are derived from the searches made by the users of the cluster. An object profile is created to represent similar objects clustered on the basis of their similarity of features. Depending on the category of a user (known, anonymous or frequent visitor to the website), any of the profiles or their combinations is used for making personalized recommendations. A ranking algorithm is also proposed that utilizes the personalized information to order and rank the recommendations. The proposed methodology is evaluated on data collected from a real life car website. Empirical analysis confirms the effectiveness of recommendations made by the proposed approach over other collaborative filtering and content-based recommendation approaches based on two-dimensional data analysis methods.

Mobilizing the brand experience: The adoption and use of mobile media in building consumer relationships in China

This research examines why and how brand owners in China adopt and use mobile media in marketing campaigns to deliver co-creation brand experiences and build consumer relationships. China represents an interesting case to study as it has leapfrogged into the age of consumer society and mobile media adoption. As the largest mobile market globally, it has experienced the intensity of mobile technology diffusion; and with it the rise of mobile consumer culture and participatory culture. Further, the rising individualism and the socio-cultural heritage in collectivism serve as a structuring tension in how mobile media is leveraged in marketing to cater to consumers' desires for individuality and social interaction. First, through expert interviews guided by the technology-organization-environment (TOE) framework (Tornatzky & Fleischer, 1990) as well as integrating innovation diffusion theory (E. Rogers, 2003), this research attempts to fill the gap of theoretical application in mobile marketing adoption at the firm level in China, and unravel the adoption factors of mobile marketing by brand owners in China. In total, 27 semi-structured interviews were conducted with key industry informants from mobile agencies, traditional agencies, venture capital firms, mobile content and service providers, mobile portals, and marketing management at brand owners. Second, based on case studies in China, this research investigates the use of mobile marketing to facilitate innovative co-creation of brand experience to cater to both individualistic as well as collective tendencies and desires amongst Chinese consumers. Through multiple case studies of the campaigns conducted by Nokia, Clean & Clear, and The North Face, and informed by in-depth interviews and document analysis, this research analyses the role of mobile media in marketing campaigns along three dimensions: the role of mobile media in content generation and consumption, the centrality of mobile media as text, tools or platforms; and the interactive environment. Specifically, the cases are organized along the spectrum from user-generated content to corporate-generated content, mobile media's role from being supplementary to it being central, and from a virtual environment to a hybrid environment. Overall, these cases demonstrate how brand owners adapt mobile media as text, tools, platforms, and environments to deliver co-creation brand experiences exploiting both individualistic as well as collective tendencies and desires amongst Chinese consumers. This research contributes to the literature on firm adoption of mobile marketing, and the role of the mobile media in facilitating co-creation experiences for Chinese consumers. It develops a model of the technological, organizational and environmental factors influencing mobile marketing adoption by firms, and provides a model explaining the role of mobile media in facilitating brand experience co-creation. The findings also demonstrate that mobile media can be leveraged to facilitate co-creation brand experience to generate added value; and meanwhile cater to both the rising individualism and the deep-seated collectivism of Chinese consumers. Empirically, it assists industry practitioners in understanding the adoption of mobile marketing in China, especially those on the supply side in order to improve their offerings and propositions. It also assists brand owners and agencies in designing their mobile marketing strategies to build consumer relationships in China.