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Cleverley, P.H.; Burnett, S.: ¬The best of both worlds : highlighting the synergies of combining manual and automatic knowledge organization methods to improve information search and discovery (2015)
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- Abstract
- Research suggests organizations across all sectors waste a significant amount of time looking for information and often fail to leverage the information they have. In response, many organizations have deployed some form of enterprise search to improve the "findability" of information. Debates persist as to whether thesauri and manual indexing or automated machine learning techniques should be used to enhance discovery of information. In addition, the extent to which a knowledge organization system (KOS) enhances discoveries or indeed blinds us to new ones remains a moot point. The oil and gas industry was used as a case study using a representative organization. Drawing on prior research, a theoretical model is presented which aims to overcome the shortcomings of each approach. This synergistic model could help to re-conceptualize the "manual" versus "automatic" debate in many enterprises, accommodating a broader range of information needs. This may enable enterprises to develop more effective information and knowledge management strategies and ease the tension between what are often perceived as mutually exclusive competing approaches. Certain aspects of the theoretical model may be transferable to other industries, which is an area for further research.
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Cleverley, P.H.; Burnett, S.; Muir, L.: Exploratory information searching in the enterprise : a study of user satisfaction and task performance (2017)
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- Abstract
- No prior research has been identified that investigates the causal factors for workplace exploratory search task performance. The impact of user, task, and environmental factors on user satisfaction and task performance was investigated through a mixed methods study with 26 experienced information professionals using enterprise search in an oil and gas enterprise. Some participants found 75% of high-value items, others found none, with an average of 27%. No association was found between self-reported search expertise and task performance, with a tendency for many participants to overestimate their search expertise. Successful searchers may have more accurate mental models of both search systems and the information space. Organizations may not have effective exploratory search task performance feedback loops, a lack of learning. This may be caused by management bias towards technology, not capability, a lack of systems thinking. Furthermore, organizations may not "know" they "don't know" their true level of search expertise, a lack of knowing. A metamodel is presented identifying the causal factors for workplace exploratory search task performance. Semistructured qualitative interviews with search staff from the defense, pharmaceutical, and aerospace sectors indicates the potential transferability of the finding that organizations may not know their search expertise levels.
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Cleverley, P.H.; Muir, L.J.: Using knowledge organization systems to automatically detect forward-looking sentiment in company reports to infer social phenomena (2018)
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- Abstract
- We investigate whether existing knowledge organization systems (KOS) for strong and hesitant forward-looking sentiment could be improved to detect social phenomena. Five judges identified examples of strong/hesitant forward-looking sentiment that were used to compare the KOS developed in the study to existing models. The "composite" KOS was subsequently applied to annual company reports to generate word frequency and biologically inspired diversity ratios. Critical realism was used as a philosophy to interpret word patterns. Results indicate the composite KOS improved on existing models identified in the literature for strong forward-looking sentiment. In one company, a statistically significant association was found between increasing diversity of assertive forward-looking sentiment and subsequent declining relative business performance. This supported the Pollyanna effect: the social phenomena of over-positive business language in that company. Sharp increases in mentions of the "future" and "learnings" were discovered in another company which may be explained by an industrial disaster and subsequent crisis management rhetoric, supporting discourse of renewal theory. This study shows that improvements can be made to existing KOS used to detect forward-looking sentiment in reports. Adopting critical realism as a philosophy when analysing "big data" may lead to improved theory generation and the potential for differentiating insights.
