Measuring export readiness using a multiple-item index

Since the 1970s the internationalisation process of firms has attracted wide research interest. One of the dominant explanations of firm internationalisation resulting from this research activity is the Uppsala stages model. In this paper, a pre-internationalisation phase is incorporated into the traditional Uppsala model to address the question: What are the antecedents of this model? Four concepts are proposed as the key components that define the experiential learning process underlying a firm’s pre-export phase: export stimuli, attitudinal/psychological commitment, resources and lateral rigidity. Through a survey of 290 Australian exporting and non-exporting small-medium sized firms, data relating to the four pre-internationalisation concepts is collected and an Export Readiness Index (ERI) is constructed through factor analysis. Using logistic regression, the ERI is tested as a tool for analysing export readiness among Australian SMEs.

A comparison of the 27-item and 12-item intolerance of uncertainty scales

The 27-item Intolerance of Uncertainty Scale (IUS) has become one of the most frequently used measure of Intolerance of Uncertainty. More recently, an abridged, 12-item version of the IUS has been developed. The current research used clinical (n = 50) and non-clinical (n = 56) samples to examine and compare the psychometric properties of both versions of the IUS. The two scales showed good internal consistency at both the total and subscale level and had satisfactory test-retest reliability. Both versions were correlated with worry and trait anxiety and had satisfactory concurrent validity. Significant differences between the scores of the clinical and non-clinical sample supported discriminant validity. Predictive validity was also supported for the two scales. Total scores, in the case of the clinical sample, and a subscale, in the case of the non-clinical sample, significantly predicted pathological worry and trait anxiety. Overall, the clinicians and researchers can use either version of the IUS with confidence, due to their sound psychometric properties.

Personalized recommender system based on item taxonomy and folksonomy

Item folksonomy or tag information is popularly available on the web now. However, since tags are arbitrary words given by users, they contain a lot of noise such as tag synonyms, semantic ambiguities and personal tags. Such noise brings difficulties to improve the accuracy of item recommendations. In this paper, we propose to combine item taxonomy and folksonomy to reduce the noise of tags and make personalized item recommendations. The experiments conducted on the dataset collected from Amazon.com demonstrated the effectiveness of the proposed approaches. The results suggested that the recommendation accuracy can be further improved if we consider the viewpoints and the vocabularies of both experts and users.

Connecting users and items with weighted tags for personalized item recommendations

Social tags are an important information source in Web 2.0. They can be used to describe users’ topic preferences as well as the content of items to make personalized recommendations. However, since tags are arbitrary words given by users, they contain a lot of noise such as tag synonyms, semantic ambiguities and personal tags. Such noise brings difficulties to improve the accuracy of item recommendations. To eliminate the noise of tags, in this paper we propose to use the multiple relationships among users, items and tags to find the semantic meaning of each tag for each user individually. With the proposed approach, the relevant tags of each item and the tag preferences of each user are determined. In addition, the user and item-based collaborative filtering combined with the content filtering approach are explored. The effectiveness of the proposed approaches is demonstrated in the experiments conducted on real world datasets collected from Amazon.com and citeULike website.

Mining users’ opinions based on item folksonomy and taxonomy for personalized recommender systems

Item folksonomy or tag information is a kind of typical and prevalent web 2.0 information. Item folksonmy contains rich opinion information of users on item classifications and descriptions. It can be used as another important information source to conduct opinion mining. On the other hand, each item is associated with taxonomy information that reflects the viewpoints of experts. In this paper, we propose to mine for users’ opinions on items based on item taxonomy developed by experts and folksonomy contributed by users. In addition, we explore how to make personalized item recommendations based on users’ opinions. The experiments conducted on real word datasets collected from Amazon.com and CiteULike demonstrated the effectiveness of the proposed approaches.

Personalized recommender systems integrating tags and item taxonomy

The social tags in Web 2.0 are becoming another important information source to profile users' interests and preferences to make personalized recommendations. To solve the problem of low information sharing caused by the free-style vocabulary of tags and the long tails of the distribution of tags and items, this paper proposes an approach to integrate the social tags given by users and the item taxonomy with standard vocabulary and hierarchical structure provided by experts to make personalized recommendations. The experimental results show that the proposed approach can effectively improve the information sharing and recommendation accuracy.

Empirical Support For an Item and Relational Conceptualization of Sponsorship

This paper outlines how commercial sponsorship can be conceptualized using an item and relational information framework, and supports this with empirical data. The model presented allows for predictions about consumer memory for sponsorship information, and hence has both theoretical and practical value. Data are reported which show that sponsors considered congruent with an event benefit by providing consumers with sponsor-specific item information, while sponsors considered incongruent benefit by providing sponsor-event relational information. Overall the provision of sponsor-event relational information is shown to result in superior memory to the provision of sponsor-specific item information, which is superior to basic sponsor mentions.

Empirical support for an item and relational conceptualization of sponsorship

This paper outlines how commercial sponsorship can be conceptualized using an item and relational information framework, and supports this with empirical data. The model presented allows for predictions about consumer memory for sponsorship information, and hence has both theoretical and practical value. Data are reported which show that sponsors considered congruent with an event benefit by providing consumers with sponsor-specific item information, while sponsors considered incongruent benefit by providing sponsor-event relational information. Overall the provision of sponsor-event relational information is shown to result in superior memory to the provision of sponsor-specific item information, which is superior to basic sponsor mentions.

Tensor-based item recommendation using probabilistic ranking in social tagging systems

A common problem with the use of tensor modeling in generating quality recommendations for large datasets is scalability. In this paper, we propose the Tensor-based Recommendation using Probabilistic Ranking method that generates the reconstructed tensor using block-striped parallel matrix multiplication and then probabilistically calculates the preferences of user to rank the recommended items. Empirical analysis on two real-world datasets shows that the proposed method is scalable for large tensor datasets and is able to outperform the benchmarking methods in terms of accuracy.

A two-stage item recommendation method using probabilistic ranking with reconstructed tensor model

In a tag-based recommender system, the multi-dimensional item, tag> correlation should be modeled effectively for finding quality recommendations. Recently, few researchers have used tensor models in recommendation to represent and analyze latent relationships inherent in multi-dimensions data. A common approach is to build the tensor model, decompose it and, then, directly use the reconstructed tensor to generate the recommendation based on the maximum values of tensor elements. In order to improve the accuracy and scalability, we propose an implementation of the -mode block-striped (matrix) product for scalable tensor reconstruction and probabilistically ranking the candidate items generated from the reconstructed tensor. With testing on real-world datasets, we demonstrate that the proposed method outperforms the benchmarking methods in terms of recommendation accuracy and scalability.

A tag-based personalized item recommendation system using tensor modeling and topic model approaches

This research falls in the area of enhancing the quality of tag-based item recommendation systems. It aims to achieve this by employing a multi-dimensional user profile approach and by analyzing the semantic aspects of tags. Tag-based recommender systems have two characteristics that need to be carefully studied in order to build a reliable system. Firstly, the multi-dimensional correlation, called as tag assignment item, tag>, should be appropriately modelled in order to create the user profiles [1]. Secondly, the semantics behind the tags should be considered properly as the flexibility with their design can cause semantic problems such as synonymy and polysemy [2]. This research proposes to address these two challenges for building a tag-based item recommendation system by employing tensor modeling as the multi-dimensional user profile approach, and the topic model as the semantic analysis approach. The first objective is to optimize the tensor model reconstruction and to improve the model performance in generating quality rec-ommendation. A novel Tensor-based Recommendation using Probabilistic Ranking (TRPR) method [3] has been developed. Results show this method to be scalable for large datasets and outperforming the benchmarking methods in terms of accuracy. The memory efficient loop implements the n-mode block-striped (matrix) product for tensor reconstruction as an approximation of the initial tensor. The probabilistic ranking calculates the probabil-ity of users to select candidate items using their tag preference list based on the entries generated from the reconstructed tensor. The second objective is to analyse the tag semantics and utilize the outcome in building the tensor model. This research proposes to investigate the problem using topic model approach to keep the tags nature as the “social vocabulary” [4]. For the tag assignment data, topics can be generated from the occurrences of tags given for an item. However there is only limited amount of tags availa-ble to represent items as collection of topics, since an item might have only been tagged by using several tags. Consequently, the generated topics might not able to represent the items appropriately. Furthermore, given that each tag can belong to any topics with various probability scores, the occurrence of tags cannot simply be mapped by the topics to build the tensor model. A standard weighting technique will not appropriately calculate the value of tagging activity since it will define the context of an item using a tag instead of a topic.

Item reputation-aware recommender systems

Recommender systems provide personalized advice for customers online based on their own preferences, while reputation systems generate a community advice on the quality of items on the Web. Both systems use users’ ratings to generate their output. In this paper, we propose to combine reputation models with recommender systems to enhance the accuracy of recommendations. The main contributions include two methods for merging two ranked item lists which are generated based on recommendation scores and reputation scores, respectively, and a personalized reputation method to generate item reputations based on users’ interests. The proposed merging methods can be applicable to any recommendation methods and reputation methods, i.e., they are independent from generating recommendation scores and reputation scores. The experiments we conducted showed that the proposed methods could enhance the accuracy of existing recommender systems.