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The purpose of this paper is to evaluate the effectiveness of a leadership development program based on International Organization for Standardization (ISO) principles. The program utilized process‐designed training groups to help participants develop emotional and social competence.

The study involved 162 managers from nine different companies in a random assignment control group design. There were nine different groups with nine managers in each group. Each group was required to follow the identical process. Trained moderators led the groups during year 1, but during year 2 a group member served as moderator, with all new moderators committing to following the process. The outcome measure was the Emotional Competence Inventory (ECI), a multi‐rater measure of social and emotional competencies associated with effective leadership. Outcome data were collected before the program started, one year later, and two years later.

Results indicated that after two years the intervention group had improved more than the controls on all ECI variables.

The paper offers recommendations for future research on the mechanisms underlying the process‐designed group strategy and contextual factors that optimize results.

The paper describes a leadership development strategy that appears to be more economical and consistent in its delivery than traditional approaches such as workshops or executive coaching.

Although ISO principles are utilized widely in the business world, this is the first study that has used this approach in the design and delivery of management development. Also, few evaluations of management development efforts utilize a random assignment control group design with pre‐ and post‐measures or examine the impact on emotional and social competence, as demonstrated in the workplace over such a long period of time.

In the era of artificial intelligence (AI), algorithms have gained unprecedented importance. Scientific studies have shown that algorithms are frequently mentioned in papers, making mention frequency a classical indicator of their popularity and influence. However, contemporary methods for evaluating influence tend to focus solely on individual algorithms, disregarding the collective impact resulting from the interconnectedness of these algorithms, which can provide a new way to reveal their roles and importance within algorithm clusters. This paper aims to build the co-occurrence network of algorithms in the natural language processing field based on the full-text content of academic papers and analyze the academic influence of algorithms in the group based on the features of the network.

We use deep learning models to extract algorithm entities from articles and construct the whole, cumulative and annual co-occurrence networks. We first analyze the characteristics of algorithm networks and then use various centrality metrics to obtain the score and ranking of group influence for each algorithm in the whole domain and each year. Finally, we analyze the influence evolution of different representative algorithms.

The results indicate that algorithm networks also have the characteristics of complex networks, with tight connections between nodes developing over approximately four decades. For different algorithms, algorithms that are classic, high-performing and appear at the junctions of different eras can possess high popularity, control, central position and balanced influence in the network. As an algorithm gradually diminishes its sway within the group, it typically loses its core position first, followed by a dwindling association with other algorithms.

To the best of the authors’ knowledge, this paper is the first large-scale analysis of algorithm networks. The extensive temporal coverage, spanning over four decades of academic publications, ensures the depth and integrity of the network. Our results serve as a cornerstone for constructing multifaceted networks interlinking algorithms, scholars and tasks, facilitating future exploration of their scientific roles and semantic relations.