Search

Research to inform organizational strategy decisions studies the impact of specific managerial decisions on performance. While links from decisions to outcomes can be convincingly identified in individual, specific circumstances, trying to do the same with a larger set of decisions and outcomes can be problematic. In a situation with a substantive sample, relying on standard metrics such as financial statements, the link to particular outcomes can be difficult to establish. Especially when trying to choose metrics not already correlated in some way. Our research is generally in the field of knowledge management (KM). The discipline has an extensive history of attempts to link better management of knowledge and other intangibles to performance outcomes. While the performance outcomes are fairly straightforward (financial success such as profitability or return on investment, innovation success, etc.), the metric for knowledge management is harder to pin down. The literature shows dozens of approaches. Some are for single firms or a small sample, but even if focusing only on metrics for a larger sample of firms, disagreement exists. But even when a KM metric is chosen for a larger sample, there is often a direct tie between input and output measures, making correlation difficult to establish, let alone causation. If profitability is part of both the input and output measures, of course they are correlated. The most advanced statistical techniques don't establish anything more than what the researcher should already know from a cursory look at the logic of the study and the specifics of the variables. As a result, whether KM actually results in better organizational performance is still an open and much debated question in the field. One research approach that can get around this issue is the natural experiment. Pioneered by Nobel winner David Card and others, the natural experiment uses two measurements with a clear, consequential event separating the two--minimizing the impact of samples that might otherwise be connected contemporaneously. The event, such as a change in law, provides a change in circumstances delinking the measures. Whether structured as a pre/post experiment (measuring before or after the intervening event) or control/experimental group experiment (one group exposed to the event, the other not), the outcome can provide convincing results and can even suggest causation. This paper will cover our work with natural experiments in relation to KM and performance outcomes for merger and acquisition (M&A) activity. Can KM metrics before an M&A event predict the success of the event? Success can be defined by the change in financial performance after the event takes place. [ABSTRACT FROM AUTHOR]

Paper applies an in-depth example to knowledge-development decisions made at the top levels of firms and their consistency with specific training initiatives arranged with third party educational providers. The unique access provided by the network in Poland at the core of this study yields insights at both levels, an opportunity not often available to academic researchers. Case study is employed. The cooperative training network includes multiple firms (five included here), thirty-eight vocational schools, and a technical university overseeing the program. Data collection including preparatory communication, periodic surveys, and additional in-depth interviews with companies as well as data provided by participating schools though only the quantitative survey results are included in this paper. Both firms and schools, as a proxy for participants, are pleased with training initiatives. Moreover, specific results on training initiatives varying by degree of company control and location (in-house, on-campus) provide insights on how firms can gain more control over the nature of the training (firm-specific knowledge) while still maintaining participant interest (often based on obtaining more industry-specific or occupational knowledge). The research is a case study and so not statistically extendable. The case study also takes place in Poland and describes a unique cooperative network including for-profit firms, vocational secondary schools, and a university. Results should be interpreted with some care, but at the same time provide unique depth and insight. Results provide useful guidance for firms participating in training partnerships as well as for schools joining in such partnerships. Specific feedback on the perception and acceptance of specific initiatives (e.g. internships) has been obtained. Brings together several streams of literature often not discussed in one place. Uses this perspective to analyze a unique cooperative network and provide unique depth to the discussion. [ABSTRACT FROM AUTHOR]

Continuing a research program studying new metrics for relational capital, this paper reports on a new analysis of media brands, both traditional and newer entrants. Relational capital is a key aspect of the knowledge assets or intellectual capital of the firm. Unlike human capital (job-related knowledge) or structural capital (knowledge incorporated into the firm itself), relational capital has to do with external relationships. Specifically, knowledge about handling external relationships, especially those with customers. Relational capital is not a widely studied topic in knowledge management (KM) or intellectual capital (IC), including potential metrics. Brand equity, on the other hand, is an idea from another field that is wellknown and much studied. While not the same concept as relational capital, it is clearly related as brand equity comes from a history of customer interactions and the value of the relationships built by the firm. Better knowledge of what satisfies customers plays an obvious role in building brand equity. But brand equity does not have a single, recognized method for calculation. Annual reports and rankings from marketing consulting firms routinely provide estimates of brand equity for high-value, well-known brands. Most of the other brands, not so much. Even so, if we know the brands with the highest equity values and can tie some additional metrics to that status, we can begin to uncover the level of customer knowledge held by individual firms and, by extension, relational capital in a wider variety of organizations. This study focuses on a brand sentiment analysis, using commercial software from Salesforce Social Studio. The web-scraping software collects mentions of a brand (or any keyword) across the web, not only social media but reviews, aggregators, and other sources of brand commentary. From that capability, an assessment of the brand's meaning to users can be assessed at a point in time. In particular, this study looked at established media brands (New York Times, Wall Street Journal, etc.) and new media brands (Buzzfeed, Techcrunch, etc.) over a three-month period in early 2023. Data were collected on brand activity (volume), sentiment (positive/negative), sources, influencers, country of origin/language, and other indicators, including the variance of all the above measures. From there, comparisons can be made across the more established brands and the developing ones, as well as to high-equity brands from other industries (covered in other studies). As noted, some suggestions can then be made concerning what metrics to track over time to assess the ongoing value of relational capital. [ABSTRACT FROM AUTHOR]