A Review of Relevant Literature
Using the case study allocated to you, critically discuss aspects in the case study organisation such as the KM tools and KM techniques, KM models and KM processes.
To start with, online forums have been widely adopted within several organizational KM (knowledge management) procedures along with virtual societies for the purpose of sharing knowledge as well as perspectives (Wu et. al., 2006). Recognizing professionals within particular sphere is vital for enhancing knowledge sharing along with availability via online forums. Present expert recognition approaches could be broadly grouped into two key concepts i.e. link-based and content-based (Nonaka et. al., 2016). Even though, the link-based concept has illustrated its preeminence over content-based concept, it involves few restrictions at the time when applied for recognizing specialists within online forums (Wang et. al., 2006). Likewise, this particular study attempts to put forward an expert documentation approach, which greatly depends upon the judgment ratings of the members, present within an online forum. Further, in specific, this particular paper extends PageRank and proposes the ExpRank algorithm, which considers both constructive along with undesirable agreement relationships amongst online forum members.
There is no doubt in the fact that effective Knowledge Management has become most important task for every company (Sanchez, 2016). The companies today should hold complete knowledge about KM tools, KM techniques, KM models, KM processes, knowledge sharing and trust, applying knowledge for innovation, new knowledge creation and KM governance (Alavi and Leidner, 2001). In this particular paper emphasis has been laid upon KM processes, KM techniques, new knowledge creation and KM models (Wu et. al., 2006). In order to compete effectively within emerging knowledge-directed economy, companies worldwide have taken up a number of initiatives, which aim at managing their highly valued still impulsive asset i.e. knowledge. KM (Knowledge management) signifies towards the methodical approach for generating, retaining, arranging, reusing, sharing and lastly, assimilating explicit and/or tacit knowledge for the purpose of supporting the learning procedures in the companies, thus resulting in enhanced organizational adaptability as well as outcome (Wright, 2015). Additionally, the KM initiatives could be grouped into four different sections i.e. developing knowledge repositories, enhancing access to knowledge, improving knowledge atmosphere and lastly, dealing with knowledge like some asset (Davenport & Prusak, 1998). Moreover, amongst them, knowledge access enhancement lays emphasis upon enabling knowledge sharing amongst people, particularly from well-informed people to others, for the reason that well-informed people could frequently provide answer to questions and lastly, carry out required tasks needing exceptional understanding, experiences and abilities (Wang et. al., 2013). Nevertheless, locating people having expertise or knowledge (i.e., experts) for some particular requirement is frequently a complex job (Davenport et al., 1998). For enhancing the knowledge accessibility, proficient recognition systems, which could automatically recognize experts for some specific area are important for knowledge admittance enhancement efforts (Wang et al., 2013).
A Review of the KMS
Moving ahead, online forums are being widely utilized within several organizational KM procedures along with virtual societies for the purpose of effectively sharing knowledge along with thoughts (Nonaka, 2011). An online forum member could effectively share his/her ideas in form of posts within the forum. Within few online forums, members could effectively comment or respond to the posts put across through other members or also, could rate as positive or negative to posts of other members. Therefore, along with the posts made through other members, the connections amongst individuals also offer significant data for effective recognition tasks (this involves recognizing specialists within specific areas) in online forums (Wu et. al., 2006). Taking a step ahead, in reaction to the restrictions of the prevailing link-based expert recognition tools, this particular paper attempts to propose an expert recognition approach grounded upon opinion ratings provided through members within online forums. In specific, the paper extends the PageRank algorithm, which is a graph-built ranking tool frequently adopted within prevailing link-grounded expert recognition methods, for developing an ExpRank algorithm that taken into consideration both the positive as well as undesirable opinion ratings normally seen within online forums.
There is no doubt in the fact that the present link-based expert recognition methods could be operative at the time when adopted for the examination of email communications for the reason that email exchanges within a company normally are conscious conducts impacted through organizational standards as well as social links (Wang et. al., 2013). Thus, a person who holds knowledge about a particular subject is likely to receive and respond to several emails relating to the main theme (Hayes and Walsham, 2013). Therefore, edges modeling email communications are considered through the prevailing ranking means as being positive to the proficiency scores of people engaged. Nevertheless, within an online forum, connection among people might not be positive always. For instance, in case if a person posts his/her thoughts related to a particular subject on online forum. Other people might not show consent with his/her views and respond with disagreement or provide negative ratings to the post. The present link-based expert recognition methods don’t model negative edges within their ranking devices and therefore, might not be efficient for expert recognition within online forums. Additionally, in reaction to such issues, this particular paper proposes an expert recognition approach based upon the opinion ratings amongst the people in online forums. Further, in doing this, the actuality that PageRank exceeds or matches the HITS (authority) algorithm in accurateness for expert recognition tasks on email communications is leveraged (Dom et al., 2003). Thus, the PageRank algorithm is extended in this paper through enabling it to take into consideration both negative and positive outlook ratings within online forums.
In reaction to the restrictions of the prevailing link-based expert recognition tools, this particular paper attempts to propose an expert recognition approach grounded upon opinion ratings provided through members within online forums. In specific, the paper extends the PageRank algorithm, which is a graph-built ranking tool frequently adopted within prevailing link-grounded expert recognition methods, for developing an ExpRank algorithm that taken into consideration both the positive as well as undesirable opinion ratings normally seen within online forums. The core of the suggested approach is the ExpRank algorithm that considers the reply model (i.e., who retorts to whose posts) along with opinion ratings being its inputs and creates a proficiency score for every person taking part in online forum. Moreover, the opinion rating is emotion that differs greatly in extent and could be negative or positive, of a person toward some particular opinion (or post) posted through other person (Wu et. al., 2006). For instance, provided a particular rating scale (for example: strongly agree, agree, somewhat agree, and disagree) a person who reacts to the opinion of some other person with ‘‘strongly agrees’’ develops an opinion rating (in terms of this specific view).
Based upon the information present on the online forum, the suggested approach might include an extra preprocessing stage, i.e., semantic relationship annotation. For instance, few online forums just permit individuals to post views and respond to opinions of other participants, thus being deficient of explicit opinion ratings. Moreover, within these forums, opinion ratings may be understood, rooted within the answered messages. During such situations, the proposed approach needs the semantic relationship annotation stage for semantically annotating likely sentiment from the reaction to some particular post (Wu et. al., 2006). In contrary, within other online forums (such as Yahoo! Answers), contributors could show their emotions explicitly through voting on opinion posts submitted through other individuals. During these situations, opinion ratings are available willingly and semantic relationship annotation isn’t required. Few semantic relationship annotation approaches have been devised and a large number of online forums are taking up voting mechanisms or opinion rating because of the propagation of Web 2.0.
The results of evaluation also propose the usefulness of undesirable agreement relationships for sound expert-documentation. Moreover, this particular paper involved various limitations, which require high research focus. First of all, for the purpose of evaluating the efficiency of the suggested ExpRank algorithm as well as the benchmark approach (i.e., PageRank), one must have a list of ‘true experts’ for individual dataset. Nevertheless, the majority of online forums, does not choose and broadcast an expert list for every domain, making information gathering highly limited.
For this reason, this paper employed datasets from one online forum (i.e., an acknowledged product-review online site) for purposes of empirical evaluation. For enhancing the outside generalizability of the results gathered during the study, it’s vital as well as preferred to examine the suggested ExpRank algorithm by making use of datasets gathered from extra online forums of different or similar kinds (for instance, discussion forums in companies). Secondly, the paper extends PageRank to devise the ExpRank algorithm. During the coming times, it will be quite fascinating to take on other ranking means (such as HITS authority) for developing an expert recognition algorithm along with empirically match its efficiency with the suggested ExpRank algorithm. Thirdly, it is assumed that the obtainability of opinion ratings on online forums and as a result, fails to consider the semantic relation annotation within the suggested approach. Additional research could involve developing a sound means for semantic relationship annotation to ensure that the suggested ExpRank algorithm could be adopted for online forums wherein explicit viewpoint ratings are unavailable.
Lastly, few members within an online forum might make an effort of inflating their proficiency scores unprofessionally through making ‘‘fake’’ members as well as hiding such pseudonyms for producing constructive opinion ratings for the posts (Ku et al., 2012). Additionally, the presence of such deceitful members is expected to dampen the involvement of normal individuals in online forums. As a result, detection of spam members becomes a vital concern for online forums. Further, as with the suggested ExpRank algorithm, a likely resolution for detecting spam member is by exploiting undesirable agreements. Even though, ExpRank is developed for the purpose of recognizing experts, further study could extend ExpRank for developing sound ways for detecting spam members.
Conclusion
To conclude, it can be clearly stated from the above discussion that online forums have been widely adopted within KM procedures along with virtual groups for the purpose of sharing opinions and knowledge (Wu et. al., 2006). Recognizing specialists within specific areas is highly important for the sound knowledge sharing amongst the members if the forum. Likewise, this particular paper proposed an expert recognition approach based upon the opinion ratings provided through online forums members. In particular, the paper extended PageRank and proposed the ExpRank algorithm, which takes into account both constructive as well as undesirable viewpoint ratings. Moreover, by making use of two datasets associated with distinct product groups (i.e. Music and Books) gathered from Epinions.com, the empirical assessment outcomes exhibit that the projected ExpRank algorithm outdoes PageRank.
References:
Alavi, M. and Leidner, D. E. (2001). Review: Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues. MIS Quarterly, 25 (1): 107–136.
Davenport, T. H. and Prusak, L. (1998). Working knowledge: How organizations manage what they know. Boston: Harvard Business School Press
Davenport, T. H., DeLong, D. W. and Beers, M. C. (1998). Successful knowledge management projects. Sloan Management Review, 39(2), 43–57.
Dom, B., Eiron, I., Cozzi, A. and Zhang, Y. (2003). Graph-based ranking algorithms for e-mail expertise analysis. In Proceedings of the 8th ACM SIGMOD workshop on research issues in data mining and knowledge discovery (pp. 42–48). San Diego, CA.
Hayes, M. and Walsham, G. (2013). Knowledge sharing and ICTs: A relational perspective”. In Easterby-Smith, M.; Lyles, M.A. The Blackwell Handbook of Organizational Learning and Knowledge Management. Malden, MA: Blackwell. pp. 54–77.
Ku, Y. C., Wei, C. P., & Hsiao, H. W. (2012). To whom should I listen? Finding reputable reviewers in opinion-sharing communities. Decision Support Systems, 53(3), 534–542
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Nonaka, I. (2011). The knowledge creating company. Harvard Business Review, 69 (6): 96–104.
Nonaka, I., Krogh, G. and Voelpel, S. (2016). Organizational knowledge creation theory: Evolutionary paths and future advances. Organization Studies, 27 (8): 1179–1208.
Rao, M. (2005). Knowledge Management Tools and Techniques. Elsevier.
Sanchez, R. (2016). Strategic Learning and Knowledge Management. Wiley, Chichester
Wang, G. A., Jiao, J., Abrahams, A. S., Fan, W. and Zhang, Z. (2013). ExpertRank: A topic-aware expert finding algorithm for online knowledge communities. Decision Support Systems, 54(3), 1442–1451.
Wei, C., Cheng, T. H., & Pai, Y. C. (2006). Semantic enrichment in knowledge repositories: Annotating semantic relationships between discussion documents. Journal of Database Management, 17(1), 49–66
Wu, B., Goel, V. and Davison, B. D. (2006). Propagating trust and distrust to demote web spam. In Proceedings of the 15th international world wide web conference. Edinburgh, Scotland
Wright, K. (2015). Personal knowledge management: Supporting individual knowledge worker performance. Knowledge Management Research and Practice, 3 (3): 156–165
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