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Conducting academic searches within online digital libraries can be a difficult task due to the complexity of the searcher’s information need. The interfaces for such digital libraries commonly use simple search features that provide limited support for the fundamental strategies that academic searchers employ. The authors have developed a novel visualisation interface called Bow Tie Academic Search to address some of these shortcomings, and present in this paper the findings from a user evaluation. The paper aims to discuss these issues.

A controlled laboratory study was conducted to compare a traditional search interface to Bow Tie Academic Search. In total, 24 graduate students were recruited to perform academic searches using the two candidate interfaces, guided by specific sub-tasks that focus on citation and keyword analysis strategies.

Although the use of the core visualisation and exploration features did not reveal differences in retrieval effectiveness or efficiency, the query refinement features were found to be effective. Strongly positive impressions of usefulness and ease of use of all aspects of the system were reported, along with a preference for using Bow Tie Academic Search for academic search tasks.

This study provides insight into the potential value for providing visual and interactive interfaces for supporting academic search tasks and strategies. While the quantitative improvements over the traditional search interface were minimal, the qualitative measures illustrate the value of Bow Tie Academic Search.

This study aimed to examine the similarities and differences between the ability to analyze the environment and exploit new knowledge (absorptive capacity) and the skills to generate value from innovation (appropriation). These fields have similar origins and are sometimes confused by practitioners and academics.

A review was conducted based on a full-text analysis of 681 and 431 papers on appropriation and absorptive capacity, respectively, from Scopus, Science Direct and Lens, using methodologies such as text mining, backward citation analysis, modularity clustering and latent Dirichlet allocation analysis.

In business disciplines, the fields are considered different; however, in other disciplines, it was found that some authors defined them quite similarly. The citation analysis results showed that appropriation was more relevant to absorptive capacity, or vice versa. From the dimension perspective, it was found that although appropriation was considered a relevant element for absorptive capacity, the last models did not include it. Finally, it was found that studies on both topics identified the importance of appropriation and absorptive capacity for innovation performance, knowledge management and technology transfer.

This is one of the first studies to examine in-depth the relationship between appropriation and absorptive capacity, bridging a gap in both fields.

The purpose of this paper is to investigate the diffusion patterns of knowledge in inspiring technological innovations and to enable monitoring development trends of technological innovations based on patent data analysis, thus, to manage knowledge wisely to innovate.

The notion of knowledge innovation potential (KIP) is proposed to measure the innovativeness of knowledge by the cumulative number of patents originated from its inspiration. KIP calculating formula is regressed in forms of two specific diffusion models by conducting a series of empirical studies with the patent-based indicators involving forward and backward citation numbers to reveal knowledge managing strategies regarding innovative activities.

Two specific diffusion models for regressing KIP formula are compared by empirical studies with the result indicating the Gompertz model has higher accuracy than the Logistic model to describe the developing curve of technological innovations. Moreover, the analysis of patent-based indicators over diffusion stages also revealed that patents applied at earlier diffusion stages normally has higher forward citation numbers indicating higher innovativeness meanwhile the patents applied at the latter stages usually requiring more knowledge inflows observed by their larger non-patent citation and backward citation amounts.

Although there is a large body of literature concerning knowledge-based technological innovation, there still room for discussing the mechanism of how knowledge diffuses and inspired knowledge. To the best of authors' knowledge, this study is the first attempt to quantitate the innovativeness of knowledge in technological innovation from the knowledge diffusion perspective with findings to support rational knowledge management related to innovation activities.

This study aims to propose a methodology by integrating three approaches, namely, internal core technology, external knowledge flow and industrial technology development to help companies improve their decision-making quality for technology planning and enhance their research and development (R&D) portfolio efficiency.

The primary focus of this study is thin-film solar technology and patent data is retrieved from the United States Patent and Trademark Office (USPTO) database. This study presents a methodology based on the proposed integrated analysis method, constructed with patent indicators, centrality analysis of social networks and main path analysis.

The results of this study can be itemized as – the core technological competency: companies involved in two specific technology fields have lower strength in R&D portfolio than leading companies with single-core technology. Knowledge flow: most companies in a network are knowledge producers/absorbers and technological development: diverse source and sink nodes were identified in the global main path during 1997-2003, 2004-2010 and 2011-2017.

Latecomer companies can emulate leaders’ innovation and enhance their technological competence to seek niche technology. Using the global main path, companies monitor outdated technologies that can be replaced by new technologies and aid to plan R&D strategy and implement appropriate strategic decisions avoiding path dependency.

The knowledge accumulation process helps in identifying the change of position and the role of companies; understanding the trend of industrial technology knowledge helps companies to develop new technology and direct strategic decisions. The novelty of this research lies in the integrated approach of three methods aiding industries to find their internal core technical competencies and identify the external position in the competitive market.

The paper aims to explore whether topic analysis (identification of the core contents, trends and topic distribution in the target field) can be performed using a more low-cost and easily applicable method that relies on a small dataset, and how we can obtain this small dataset based on the features of the publications.

The paper proposes a topic analysis method based on prolific and authoritative researchers (PARs). First, the authors identify PARs in a specific discipline by considering the number of publications and citations of authors. Based on the research publications of PARs (small dataset), the authors then construct a keyword co-occurrence network and perform a topic analysis. Finally, the authors compare the method with the traditional method.

The authors found that using a small dataset (only 6.47% of the complete dataset in our experiment) for topic analysis yields relatively high-quality and reliable results. The comparison analysis reveals that the proposed method is quite similar to the results of traditional large dataset analysis in terms of publication time distribution, research areas, core keywords and keyword network density.

Expert opinions are needed in determining the parameters of PARs identification algorithm. The proposed method may neglect the publications of junior researchers and its biases should be discussed.

This paper gives a practical way on how to implement disciplinary analysis based on a small dataset, and how to identify this dataset by proposing a PARs-based topic analysis method. The proposed method presents a useful view of the data based on PARs that can produce results comparable to traditional method, and thus will improve the effectiveness and cost of interdisciplinary topic analysis.

This paper proposes a PARs-based topic analysis method and verifies that topic analysis can be performed using a small dataset.

The purpose of this paper is to develop a global ranking of knowledge management and intellectual capital academic journals.

An online questionnaire was completed by 233 active knowledge management and intellectual capital researchers from 41 countries. Two different approaches: journal rank‐order and journal scoring method were utilized and produced similar results.

It was found that the top five academic journals in the field are: Journal of Knowledge Management, Journal of Intellectual Capital, Knowledge Management Research and Practice, International Journal of Knowledge Management, and The Learning Organization. It was also concluded that the major factors affecting perceptions of quality of academic journals are editor and review board reputation, inclusion in citation indexes, opinion of leading researchers, appearance in ranking lists, and citation impact.

This study was the first of its kind to develop a ranking system for academic journals in the field. Such a list will be very useful for academic recruitment, as well as tenure and promotion decisions.

The findings from this study may be utilized by various practitioners including knowledge management professionals, university administrators, review committees and corporate librarians.

This paper represents the first documented attempt to develop a ranking of knowledge management and intellectual capital academic journals through a survey of field contributors.

The convergence of industries exposes the involved firms to various challenges. In such a setting, a firm's response time becomes key to its future success. Hence, different approaches to anticipating convergence have been developed in the recent past. So far, especially IPC co-classification patent analyses have been successfully applied in different industry settings to anticipate convergence on a broader industry/technology level. Here, the aim is to develop a concept to anticipate convergence even in small samples, simultaneously providing more detailed information on its origin and direction.

The authors assigned 326 US-patents on phytosterols to four different technological fields and measured the semantic similarity of the patents from the different technological fields. Finally, they compared these results to those of an IPC co-classification analysis of the same patent sample.

An increasing semantic similarity of food and pharmaceutical patents and personal care and pharmaceutical patents over time could be regarded as an indicator of convergence. The IPC co-classification analyses proved to be unsuitable for finding evidence for convergence here.

Semantic analyses provide the opportunity to analyze convergence processes in greater detail, even if only limited data are available. However, IPC co-classification analyses are still relevant in analyzing large amounts of data. The appropriateness of the semantic similarity approach requires verification, e.g. by applying it to other convergence settings.

This study aims to propose a novel approach based on utility mining to find the associations among wind energy technologies.

The proposed approach uses patent documents and utility mining. Associations among wind energy technologies have been evaluated to show how the proposed approach works in practice.

Determining the relationships between wind energy technologies provide essential information to investors and decision-makers. Therefore, a real-life case study of wind energy technology is performed to show how the proposed approach works in practice. The proposed approach founds technology classes associated with wind energy technology. Furthermore, the strongest associations among technologies are also obtained by the proposed approach. The results of the case study show that the proposed approach can be easily used in practice. The maximum size of itemsets is 18-level itemsets. Y02E and F03D cooperative patent classification (CPC) codes appear on all itemsets. As the technologies of Y02E and F03D are directly correlated, they will be mutually developed in the future. Additionally, the number of patent corresponding to Y02E and F03D CPC codes are 7,494 and 6,577, respectively.

This is the first study that applies the utility mining-based approach to patent documents. Different levels of importance among technologies based on patent citations and the number of repetitions of each technology class are considered in the proposed approach.

Emerging market multinational enterprises (EMNEs) have consolidated their global presence recently, challenging existing international business (IB) theories. One of their most significant characteristics has been the prevalence of strategic asset-seeking (SAS) mergers and acquisitions (M&As) targeting firms in developed countries. Such SAS M&As have been ascribed to the aim of acquiring or augmenting firm-specific advantages, rather than exploiting existing advantages. A literature review is needed to synthesize the growing number of academic studies and to contribute to ongoing theoretical developments on EMNEs' catch-up strategies.

The authors follow a standard systematic literature review approach. The authors collate academic studies on EMNEs' SAS M&As in developed markets published between 2000 and mid-2020, structuring the analysis using the logic of antecedent, process and performance outcomes.

The authors present recent research trends in terms of year, journal, theories and methods. The authors synthesize and analyze existing knowledge on EMNEs' SAS M&As and identify remaining gaps to suggest future research directions.

The review contributes by focusing on the key argument of current EMNE research – SAS M&As. By providing the first focused review on this topic, it provides a basis for further research on EMNEs' SAS M&As.

Given the public availability of secondary data on investments in events such as the Olympics, FIFA World Cup and professional sports, event studies that measure stock market response to these investments have grown. Previous findings are mixed, however, with some studies suggesting that the announcement of sponsorship contracts is a positive event and others finding detrimental effects of the announcement on shareholder value. This study aims to analyze the mixed findings from event studies in sport sponsorship to determine if sponsorship announcements influence stock market response.

The meta-analysis examines more than 20 years of research on event studies in sponsorship (34 studies).

The overall results show a positive, but non-significant effect of partnership deal announcements on shareholder wealth. Further analysis considers the effects of sponsorship announcements by each type of event window to see the impact of the announcement relative to time (pre-announcement, announcement day, post-announcement and pre- to post-announcement). This closer examination of the event window shows that stock prices of sponsoring organizations increased in the pre-announcement window.

Quantitative meta-analytic findings indicate that information about sponsorship deals appears to leak to share markets and positively influence share price. This finding suggests that sponsoring the sports and events found in these event studies is seen as value enhancing for sponsoring firms.