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Taking the dialogic organizational development perspective, this study aims to investigate the framing processes when engaging in dialogue for strategy implementation and how these enable the evolution of implementation opportunities.

Through a qualitative exploratory study conducted in a large multinational, the authors analyse the dialogue and interactions among 25 dyads when identifying opportunities to contribute to strategy implementation. The data analysis relies on a process-coding approach and linkography, a valuable protocol analysis for identifying recursive interaction schemas in conversations.

The authors identify four main framing processes – shaping, unveiling, scattering and shifting – and provide a framework of how these processes affect individuals’ mental models through increasing the tangibility of opportunities or elevating them to new value hierarchies.

From a theoretical perspective, this study contributes to the strategy implementation and organizational development literature, providing a micro-perspective of how dialogue allows early knowledge structures to emerge and shape the development of opportunities for strategy implementation.

From a managerial perspective, the authors offer insights to trigger action and change in individuals to contribute to strategy when moving from formulation to implementation.

Rather than focusing on the structural control view of strategy implementation and the role of the top management team, this study considers strategy implementation as a practice and what it takes for organizational actors who do not take part in strategy formulation to enact and shape opportunities for strategy implementation through constructive dialogue.

As part of a national plan to govern professional and organizational development in Norwegian specialist healthcare, the country’s hospital clinics are tasked with constructing development plans. Using the development plan as a case, the paper analyzes how managers navigate and legitimize the planning process among central actors and deals with the contingency of decisions in such strategy work.

This study applies a qualitative research design using a case study method. The material consists of public documents, observations and single interviews, covering the process of constructing a development plan at the clinical level.

The findings suggest that the development plan was shaped through a multilevel translation process consisting of different contending rationalities. At the clinical level, the management had difficulties in legitimizing the process. The underlying tension between top-down and bottom-up steering challenged involvement and made it difficult to manage the contingency of decisions.

The findings are relevant to public sector managers working on strategy documents and policymakers identifying challenges that might hinder the fulfillment of political intentions.

This paper draws on a case from Norway; however, the findings are of general interest. The study contributes to the academic discussion on how to consider both the health authorities’ perspective and the organizational perspective to understand the manager’s role in handling the contingency of decisions and managing paradoxes in the decision-making process.

This paper aims at understanding how automotive firms integrate customer relationship management (CRM) tools and big data analytics (BDA) into their marketing strategies to enhance total quality management (TQM) after the coronavirus disease (COVID-19).

A qualitative methodology based on a multiple-case study was adopted, involving the collection of 18 interviews with eight leading automotive firms and other companies responsible for their marketing and CRM activities.

Results highlight that, through the adoption of CRM technology, automotive firms have developed best practices that positively impact business performance and TQM, thereby strengthening their digital culture. The challenges in the implementation of CRM and BDA are also discussed.

The study suffers from limitations related to the findings' generalizability due to the restricted number of firms operating in a single industry involved in the sample.

Findings suggest new relational approaches and opportunities for automotive companies deriving from the use of CRM and BDA under an overall customer-oriented approach.

This research analyzes how CRM and BDA improve the marketing and TQM processes in the automotive industry, which is undergoing deep transformation in the current context of digital transformation.

Exploration and exploitation have long been documented as prominent approaches to business management and organizational adaptation to external environment. Maintaining balance between these activities is a key to survival and prosperity. However, there is little direct evidence of the effect of such combined usage of both approaches on firm performance in times of crisis, especially within small- and medium-sized enterprises (SMEs). The purpose of this paper is to reveal the role of balanced ambidexterity in shaping firm performance during COVID-19 recession.

Based on a survey of 333 Russian SMEs, the authors test the proposed theoretical framework linking innovative ambidexterity to firm performance level and variability taking into account technological uncertainty.

The results show that innovative ambidexterity tends to increase level and decrease variability of performance outcomes, whereas technological uncertainty acts as a positive contingency for this impact.

The results provide an improved understanding of ambidexterity and organizational literatures by clarifying the contingent nature of the ambidexterity–firm performance relationship during COVID-19 recession.

Self-regulation is the level of learning where the learner becomes an active agent in their learning process in terms of activity and aspects of motivation and metacognition. The current paper mostly deals with the metacognitive aspect. The purpose of this study is to gain insight into self-regulation of learning in the context of modern technology in higher education. This study also aims to highlight the direction, tendencies and trends toward which self-regulation of learning is moving in relation to modern technologies.

The review study was compiled via searches in three databases: Scopus, Web of Science and ERIC. A filter was used to search for empirical studies solely in English, published over the past decade on the topics of self-regulation of learning and technology in higher education.

The findings clearly show a correlation between self-regulation of learning and modern technology, especially after a significant event such as the Covid-19 pandemic. However, in the wake of this change, the field of education has seen the emergence of methods and new platforms that can provide support for the development of self-regulated learning strategies.

The originality of the study lies in the fact that it focuses on the link between self-regulation of learning and modern technologies in higher education, including some predictions of the future direction of self-regulation of learning in this context.

The purpose of this article is to discuss how the mastery of resilience in operations and supply chains plays a significant role in the transition to a more sustainable future. Furthermore, it is supposed to propose avenues for future research on operational and supply chain resilience, interacting with the sustainability literature in our field.

A conceptual review of resilience and sustainability themes within operations and supply chain management research is conducted. Reflections on the topic are informed by relevant literature published over the last decade.

The major conceptual contributions are threefold: (1) This article elaborates on the understanding of operational resilience and supply chain resilience concepts and reviews their respective primary research streams. (2) It proposes resilience as the missing element in the pursuit of excellence in organizations that want to contribute to a more sustainable future. (3) The article offers a research framework that provides a future research agenda at the intersection of resilience and sustainability in operations and supply chain management research.

The article highlights gaps in current research and illustrates further areas of research that need to be addressed to maximize the contribution of operations and supply chain management research in supporting practitioners to achieve a more sustainable future.

The purpose of this paper is to develop a multistakeholder scale for assessing an excellent human resource (HR) function to demonstrate the extent to which it is focused on creating value from the perspective of its key internal and external stakeholders.

At the first stage, an in-depth literature review was conducted to extract the best practices for an excellent HR function. Then, to test the validation of the developed framework, it was sent to HR academics and practitioners in different countries. The survey responses were analyzed using the methods of the structural equation modeling (SEM) and confirmatory factorial analysis.

This study proposes an excellent HR multistakeholder assessment scale consisting of ten criteria based on the perceptions of internal and external HR stakeholders.

This study suggests a framework for assessing overall HR excellence based on the perceptions of key internal and external HR stakeholders. In addition, it is recommended that future researchers empirically test the developed scale across various industries and firm sizes.

HR managers, by using this framework, could continuously assess their HR excellence and compare their HR excellence with other companies’ HR excellence in the industry and then plan for continuous improvement in different HR areas to improve their stakeholders’ experiences.

This paper identifies the enablers and results of an excellent HR department and designs a multistakeholder feedback scale to better understand key internal and external HR stakeholders’ perceptions.

Strategic flexibility (SF) has become increasingly important for firms because of the fast changes in the external environment. In line with the practical importance of SF, an emerging research field has developed around it that has attempted to understand the nature of SF and the key relationships. The aim of this study is to unveil the semantic structure of the recent literature on SF and to suggest new promising areas for future research.

The authors conduct a systematic literature review with a bibliographic analysis technique, which allows authors to identify the main recent streams in the literature, as well as offer reflections and suggestions for future research.

The authors uncover three main emerging areas in the research on SF, namely SF as a dynamic capability, the role of knowledge management for SF and the relationship between a firm SF and the external environment. The authors put forward three avenues for future research on SF: Avenue 1. SF, business model innovation (BMI) and other dynamic capabilities (DC), Avenue 2. Digital technologies and SF/organizational agility and Avenue 3. SF and sustainability. Articles included in the special issue entitled “A strategic perspective on flexibility, agility and adaptability in the digital era” contribute to Avenue 2, thus paving the way for filling some of the identified gaps regarding the relationship between SF and digitalization.

To the best of authors’ knowledge, this is the first literature review on SF that uses a bibliometric approach to draw conclusions on the findings in the literature. The review contributes to the theoretical understanding of SF by illustrating and explicating core topics that have persisted over time, as well as by presenting three main avenues for further developing authors’ knowledge around SF.

The purpose of this systematic narrative review is to discourse on vision and organizational performance. By analysing work-life and organization studies journals, the authors respond to a call to view the process of visioning more holistically.

The methodological approach is a discourse-oriented qualitative content analysis. The authors explore visioning through an epistemological lens, which emphasizes both the connections and differences between “traditional” philosophical approaches.

The findings show how the different interpretations of vision and related concepts are tied to the following themes: clarity, causality, embodiment and sensory experiences and actionability.

Through the frameworks of scientific realism and relativism, the authors illustrate novel insights into the ways in which visioning occupies a place in knowledge management.

Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.

The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.

The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.

The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.