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The purpose of this paper is to assess the success factors in a large organization that contributed to the success of organizational transformation (OT) through business diversification using a complex adaptive systems (CAS) framework. This assessment is done to determine how well the CAS framework can explain the success factors that contribute to the success of large-scale organizational change in complex organizations. If the CAS framework is capable of explaining the organizational factors that lead to the success of change implementation, the managers can employ this framework to increase the likelihood of success while implementing change.

This study uses qualitative research methodology. The data were collected from the case study organization (CSO) through 40 in-depth semi-structured interviews and analyzed using thematic deductive analysis approach.

The CAS framework explains the success factors that contribute to the success of OT through business diversification.

This paper provides a comprehensive guide for change implementation by combining the insights from the CAS framework with identified success factors (for change implementation) from the case organization.

The originality of this paper lies in extending the principles of existing change models, for successful change implementation by using the CAS framework. The prescribed change models and the CAS framework/complexity theory are two distinct sets of literature; this paper successfully merges the two to develop a comprehensive set of guidelines for change implementation. By doing so, this paper highlights the fact that alternative, non-linear, change approaches, instead of conventional multistep change models, can be effective in implementing large-scale organizational change successfully given the complexities of current organizational environments.

The author aims to walk beside the singular privileged class of White women’s suffrage feminist origin story to (re)construct plausible feminist fragmented threads as antenarratives in the context of business management education. To accomplish this (re)assembling of threads, the author examined two North American business trade publications created and used within two business schools, Harvard University’s Harvard Business Review (HBR), established in 1922, and Western University’s The Quarterly Review of Commerce (The Quarterly), established in 1933.

The author carefully reviewed almost 4,000 articles from HBR and The Quarterly, focusing on 308 articles that addressed the experiences of complex women. With this subset of collected articles, the author highlighted overlooked details, accidents and errors, generating interest and curiosity about the emergence of these fragmented and paradoxical origins that align with Foucault's histories of errors. By grouping these narrative fragments into themes and conducting a critical discourse analysis that incorporated influences from the external environment, the author reconstructed plural feminist origins antenarratives.

The themes discovered, including women as consumers, explicit working women concerns, women as authors/coauthors, diversity and social justice initiatives, and women in higher education/training, are not merely descriptive observations. They are the building blocks for identifying and analyzing the power relations circulating among feminist origins antenarratives within management education circles. These antenarratives include shedding light on women working in capitalist contexts, the educational needs of business women, and men and naming (but not breaking) the “mythologies” of women at work. These findings are transformative to the understanding of plural feminist origins.

The uniqueness of this work lies in its threefold contributions: moving away from the notion of a singular feminist origin story and instead embracing the complexity of multiple, paradoxical and incomplete origins; shedding light on the spectrum of power relations – ranging from productive to oppressive – that shaped the experiences of women in two management educational circles during the first half of the 20th century; and introducing the concept of inflection points, which underscores the fluidity of knowledge.

This paper aims to explore the impact of the data observation period on the computation of altmetric measures like velocity index (VI) and half-life. Furthermore, it also attempts to determine whether article-level computations are better than computations on the whole of the data for computing such measures.

The complete publication records for the year 2016 indexed in Web of Science and their altmetric data (original tweets) obtained from PlumX are obtained and analysed. The creation date of articles is taken from Crossref. Two time-dependent variables, namely, half-life and VI are computed. The altmetric measures are computed for all articles at different observation points, and by using whole group as well as article-level averaging.

The results show that use of longer observation period significantly changes the values of different altmetric measures computed. Furthermore, use of article-level delineation is advocated for computing different measures for a more accurate representation of the true values for the article distribution.

The analytical results show that using different observation periods change the measured values of the time-related altmetric measures. It is suggested that longer observation period should be used for appropriate measurement of altmetric measures. Furthermore, the use of article-level delineation for computing the measures is advocated as a more accurate method to capture the true values of such measures.

The research work suggests that altmetric mentions accrue for a longer period than the commonly believed short life span and therefore the altmetric measurements should not be limited to observation of early accrued data only.

The present study indicates that use of altmetric measures for research evaluation or other purposes should be based on data for a longer observation period and article-level delineation may be preferred. It contradicts the common belief that tweet accumulation about scholarly articles decay quickly.

Several studies have shown that altmetric data correlate well with citations and hence early altmetric counts can be used to predict future citations. Inspired by these findings, majority of such monitoring and measuring exercises have focused mainly on capturing immediate altmetric event data for articles just after the publication of the paper. This paper demonstrates the impact of the observation period and article-level aggregation on such computations and suggests to use a longer observation period and article-level delineation. To the best of the authors’ knowledge, this is the first such study of its kind and presents novel findings.