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An infrastructure based on multiple heterogeneous access networks is one of the leading enablers for the emerging paradigm of pervasive computing. The optimal management of diverse networking resources is a challenging problem. This paper aims to present a context‐aware policy mechanism with related end‐to‐end (E2E) evaluation algorithm for adaptive connectivity management in multi‐access wireless networks.

A policy is used to express the criteria for adaptive selection of the best local and remote network interfaces. The best connection can then be used for the establishment of a channel as well as for the maintenance of on‐going data transmission. Rich context information is considered in the policy representation with respect to user profile and preference, application characteristics, device capability, and network quality of service conditions. The decision of the best access networks to be used is made on the basis of an E2E evaluation process. The decision can be made in both master–slave and peer‐to‐peer modes, according to the decision matrixes generated in both ends. The paper focuses on the policy representation and connection evaluation algorithm. A case study is presented to show the usability of the proposed policy mechanism and decision‐making algorithm in the adaptiv management of heterogeneous networking resources.

The proposed policy mechanism is for the adaptive decision of connection selection in channel establishment and vertical handoff between heterogeneous access networks. A policy is represented as a four‐tuple, including the direction and the class of traffic, requirement expression, and concrete evaluation items. Three steps are involved in the evaluation process, namely policy traverse, decision matrix calculation, and decision‐making.

The policy mechanism can be easily extended to include adaptive selection of multiple user devices in addition to multiple connections.

The aim of this study is to investigate and clarify how the triple helix actors can effectively implement the concepts of Kaizen to navigate and overcome the complex obstacles brought on by the global COVID-19 pandemic.

Through broad literature reviews, nine common parameters under triple helix actor have been recognized. A regression analysis has been done to study how the triple helix actors’ common parameters impact Kaizen implementation in business operations.

The results of this study revealed insightful patterns in the relationships between the common parameters of triple helix actor and the dependent variables. Notably, the results also showed that leadership commitment (LC) emerges as a very significant component, having a big impact on employee engagement as well as organizational performance.

In addition to offering valuable insights, this study has limitations including the potential for response bias in survey data and the focus on a specific set of common parameters, which may not encompass the entirety of factors influencing Kaizen implementation within the triple helix framework during the pandemic.

The originality of this study lies in its comprehensive exploration of the interplay between triple helix actors and Kaizen principles in addressing COVID-19 challenges. By identifying and analyzing nine specific common parameters, the study provides a novel framework for understanding how triple helix actors collaboratively enhance organizational performance and employee engagement during challenging times.