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This conceptual paper aims to contribute to the knowledge management (KM) literature by seeking to determine whether wisdom management (WM) will replace KM in future.

This exploratory paper follows the interpretivist research philosophy and the deductive approach. The data collection is based on selected literatures from three disciplines (KM, philosophy and psychology). The findings were qualitatively analysed.

The findings are threefold: (1) the discussion of wisdom has been either neglected or superficially discussed in the KM literature; (2) despite the fact that wisdom is widely discussed and researched in philosophy and psychology disciplines, there is no commonly agreed upon definition of wisdom, and a dichotomy exists between the implicit and explicit theories of wisdom; (3) wisdom research in philosophy and psychology disciplines provides valuable input to KM by identifying the dimensions, components and characteristics of wisdom and wise individuals.

Important sources may have been unintentionally overlooked in this paper. This paper identifies the need for empirical research and discussion about WM as the next potential phase of KM. It offers several implications for researchers, managers and management educators as this paper shows that WM is emerging as a new discipline.

This paper makes a theoretical contribution to the fifth phase of KM by drawing attention to wisdom and WM as the next potential phase of KM.

This study examined the use of five servant leadership dimensions including altruistic calling, emotional healing, wisdom, persuasive mapping, and organizational stewardship by male and female servant leaders. Staff members (368) employed in county government offices across a Midwestern state were sampled using the Servant Leadership Questionnaire and a series of demographic questions. Results indicated males and females equally and effectively utilized both communal and agentic servant leadership dimensions. These findings contest prevailing gender role stereotypes in leadership.

The purpose of this study is to propose the notion of the value and contribution of the wisdom of older workers in working life.

Using a framework of organisational aesthetics, this paper presents an empirical case study analysing the material collected from Finnish municipal home care services and sheltered accommodation designed for older people. Through the analysis of the stories of older and younger employees, the case study shows how workers construct ideas of skills related to ageing, how the wisdom of older workers has been transformed into practices and how aesthetic wisdom might benefit this social process.

The findings reveal three characteristics of ageing wisdom – caring, graceful and joyful wisdom – that all present focused sensitivity, emphasizing the subject’s experience, feelings and emotions. The findings also illustrate how the authority of older workers made visible this feeling-driven wisdom and values, and through these means, enhanced the skills of younger employees.

The present paper challenges the debate on ageing which has mainly focused on the problems of older workers in a social context.

This paper provides new insights into the skills of older workers in the empirical and theoretical framework of organisational aesthetics.

The purpose of this paper is to pinpoint some key variables that help shape the notion of older workers as a source of organizational wisdom capital.

Toward that end, the paper reviews a selective bibliography in order to support its arguments.

The evidence garnered throughout this paper – fundamentally through different lens of analysis – suggests that older workers may be considered as valuable assets. Furthermore, a sizeable number of members of this cohort continue, even in the latter stages of their careers, to be willing, well-equipped, and able to enhance, if properly utilized, companies to achieve other patterns of performance. Accordingly, it is advocated here that their knowledge and expertise constitutes an authentic source of organizational wisdom capital that deserves careful attention from organizations to maintain by means of suitable incentives and training.

This paper highlights other aspects that should not be disdained by organizations such as career-ending, work characteristics, and mastery-avoidance goals. Thus, companies that are interested in keeping older talents must be attuned to their wishes and aspirations, as well as being proactive by offering tailor-made job-products to them.

Given the trend of aging workforce, it is likely that organizations will be increasingly impacted by societal demands and public policies toward benefiting and respecting older talents.

This paper advocates that older workers are usually living memories of organizational life. Rather, they tend to keep in their minds those failures and successful ideas, projects, initiatives, and leaderships, which added or not value throughout their trajectories, as well as things that worked out or not. Fundamentally, they are able to provide answers to vital questions.

Leader member exchange has previously been found to be a solid predictor of positive organizational outcomes. Much research has tested a variety of possible antecedents to Leader Member Exchange (LMX), but only a limited number involving leadership styles. In this study servant leadership dimensions were tested for relationship to LMX quality. Strong correlations were found, tested for collinearity, and the best predictive model identified via regression analysis. The value to leadership educators was highlighted, limitations were recognized, and suggested areas for future research discussed.

Traditional classification algorithms always have an incorrect prediction. As the misclassification rate increases, the usefulness of the learning model decreases. This paper presents the development of a wisdom framework that reduces the error rate to less than 3% without human intervention.

The proposed WisdomModel consists of four stages: build a classifier, isolate the misclassified instances, construct an automated knowledge base for the misclassified instances and rectify incorrect prediction. This approach will identify misclassified instances by comparing them against the knowledge base. If an instance is close to a rule in the knowledge base by a certain threshold, then this instance is considered misclassified.

The authors have evaluated the WisdomModel using different measures such as accuracy, recall, precision, f-measure, receiver operating characteristics (ROC) curve, area under the curve (AUC) and error rate with various data sets to prove its ability to generalize without human involvement. The results of the proposed model minimize the number of misclassified instances by at least 70% and increase the accuracy of the model minimally by 7%.

This research focuses on defining wisdom in practical applications. Despite of the development in information system, there is still no framework or algorithm that can be used to extract wisdom from data. This research will build a general wisdom framework that can be used in any domain to reach wisdom.