Search results

The purpose of this paper is to address the conflicting views on the role of bottom‐up learning; this research combines the information‐processing and organizational inertia views to explore how bottom‐up learning affects both exploratory and exploitative innovations and if the effects are contingent on organizational formalization.

Data for this study are obtained through an interview survey instrument from 213 firms. The questionnaire is adopted from several previous studies on organizational learning, structure and innovation with minor translation adjust. A pilot test was conducted and necessary modifications were made to the questionnaire. Tests show that the sampling validity is not biased by non‐response bias and the measure reliability and validity are acceptable. Furthermore, Harman one‐factor and CFA tests show that the results should not be biased by common method bias. The multicollinearity is also tested and controlled during regression analysis.

The findings show that bottom‐up learning has accelerated positive effect on exploitative innovation while having an inverted U‐shaped effect on explorative innovation. Furthermore, the organizational formalization strengthens the positive effect of bottom‐up learning on exploitative innovation and the U‐shaped effect on exploratory innovation.

The paper contributes to organizational inertia and information‐processing theories by providing a complete picture on how firms built exploitative and exploratory innovations through bottom‐up learning aligned with appropriate organizational structure.

How do firms make effective strategic change when competitive advantage deteriorates fast in a dynamic environment? Based on information-processing theory and organizational inertia theory, the purpose of this paper is to investigate how bottom-up learning affects the speed and magnitude of strategic change and if these relationships are contingent on strategic flexibility.

Using data of 213 firms in China, the authors conduct an empirical test of hypotheses through a stepwise multivariate regression approach.

The empirical study suggests that resource flexibility weakens the positive relationship between bottom-up learning and the speed of strategic change while strengthens the impact of bottom-up learning on the magnitude of strategic change. In addition, coordination flexibility strengthens the positive impact of bottom-up learning on the speed and magnitude of strategic change.

The findings not only provide a more nuanced and in-depth understanding of strategic change, but also offer strong guidance for firms on how to make better use of strategic flexibility in order to benefit from bottom-up learning.

The aim of this study was to examine how manufacturing digitalization can be leveraged to promote green innovation in the digital era by investigating the effects of manufacturing digitalization on green process innovation, and thus firm performance. The authors also explored how the role of manufacturing digitalization varies with horizontal information sharing, vertical bottom-up learning and technological modularization.

Five hypotheses were examined by performing regression analyses on survey data from 334 manufacturing firms in China.

Manufacturing digitalization positively affects green process innovation, and thus firm performance. Furthermore, this positive effect is strengthened by horizontal information sharing and technological modularization and weakened by vertical bottom-up learning.

This study extends the literature rooted in the natural-resource-based view by identifying the crucial role of green process innovation and investigating the value of manufacturing digitalization for developing green capabilities in the digital era. It also contributes to this line of research by revealing contingent factors to leverage manufacturing digitalization from the information processing perspective. Furthermore, this study extends information processing theory to the digital context and identifies the interaction of organizational design (vertical bottom-up learning and horizontal information sharing) and digital investment (manufacturing digitalization).

Considering personality as changeable through a bottom-up process of altering states, habits and traits, constitutes a shift in the predominant paradigm within personality psychology. The purpose of this paper is to reconsider Bateson’s theory of learning and organizational triple-loop learning in light of this recent empirical evidence.

This paper uses a multi-disciplinary conceptual approach. Based on an integrative analysis of literature from recent work in personality psychology, four dimensions (process, content, time and context) are identified that allow linking personality change and triple-loop learning.

Identifying a bottom-up process of changing states, habits and traits as being central to change personality, allows for reconsidering Bateson’s theory of learning as a theory of personality development (Learning II) and personality change (Learning III). Functionally equivalent, organizational triple-loop learning is conceptualized as a change in an organization’s identity over time that may be facilitated through a change in responding to events and a change in the organization’s routines.

Interventions that change how organizations respond to events and that change the routines within an organization may be suitable to facilitate triple-loop learning in terms of changing organizational identity over time.

This paper contributes to the discussion on Bateson’s theory of learning and organizational triple-loop learning. As interest in personality change grows in organization studies, this paper aims to transfer these findings to organizational learning.

This paper aims to review the extant intelligent home specifications and put forward a new dimension for the specifications of intelligent home (IHS).

This study adopts a learning (bottom‐up) algorithm which emphasizes the importance of learning and adaptability to the dynamic environmental changes in the IHS.

The study finds that the intelligent home has been characterized by automation, integration of facilities and communication. However, it is contended here that an intelligent home specification in such a hard‐wired (top‐down) approach cannot be sustained in the light of the continuous changes of user requirements. Hence, adaptation to users' needs must be encompassed in a system of home intelligence.

This study provides a framework for all stakeholders to work for a common goal and a platform for benchmarking the performance of intelligent home in the long run.

This is the first to adopt the learning (bottom‐up) algorithm in defining home intelligence.

Research and experience show that evidence-based practice (EBP), i.e. using the best available knowledge in daily professional work, is difficult to achieve in social services. The purpose of this study is to understand the development of organizational EBP learning processes in daily work through workplace education for staff and managers of supported homes for people with cognitive disabilities. The authors examine how the EBP model and new knowledge are understood and made actionable in the workplace, applying theories of organizational learning.

The authors used empirical material collected from an EBP workplace education pilot in Sweden, as well as documents on national EBP implementation in Swedish social services. Before the pilot, a focus group interview was conducted with regional senior managers. Participating managers and staff were individually interviewed two to three years after the pilot.

The study illustrates how knowledge-based action emerged from education where EBP was interpreted, understood, reflected on, and tested, supported by codified EBP tools in the work context. The participants, when supervised, and when observing and questioning their own behaviors in practice, contributed to double-loop learning (DLL) processes. Codification of EBP knowledge into useful tools and socialization processes during education and workplace meetings was crucial in developing individual and group DLL and knowledge-based actions.

The bottom-up approach to EBP development and the adaptive contextual learning at the workplace gave new insights into organizational learning in social service workplaces.

This contribution highlights opportunities for new insights into organizational learning processes through the use of practice-based innovative organizational learning technologies (iOLTs). The article explores the varied possibilities and application of learning technologies in terms of organizational learning perspectives.

Given this is a relatively new field of practice and research, the three organizational learning theoretical perspectives – behavioural, action and practice – form a base upon which we can conceptualise learning as mediated through iOLTs and how we can leverage these technologies, particularly for practice-based organizational learning, which focuses more on the intangibles of learning.

Due to the pervasive and ubiquitous potential of organizational learning technologies, new avenues for analysing the mediating effect of technologies on learning enable our research and practice attention to shift from formal learning to the informal; from top-down learning management to bottom-up learning creation; from cognitive and behavioural approaches to social, spontaneous and contextual learning – helping us decipher the “language” of learning in concrete ways.

The iOLTs are emerging and at an ever-increasing pace. Practice-based iOLTs can help trace and decipher the “language” of learning in concrete ways, which is a key aspect in our being able to leverage our organizational learning capacities.

The purpose of this paper is to present and discuss two different learning approaches to lean through a sensemaking lens.

This is comparative case study within one organisation, and a qualitative analysis of interview data, participative observations and documents.

Although this study found that both practices promoted lean thinking in the organisation, the “calculating lean approach” has an approach oriented towards one profession, namely, medical doctors, whereas “doing lean” has a more multi-professional-oriented approach.

This research enriches the literature on workplace learning in relation to lean, seen from a practice-based perspective. The authors argue that “The calculating lean approach” and its use of data presentations is best suited to persuade the powerful doctors to join, when working with small improvements. “The doing lean approach,” on the contrary, is more suited to promote more comprehensive changes from the bottom up that require a deeper understanding of lean among all the professions involved.

The purpose of this paper is to highlight the challenges regarding learning in stakeholder relations in profit and nonprofit organizations.

Conclusions and models presented in the paper have been designed based on the systems perspective, critical thinking and critical review of previous contributions.

Organizational learning has been examined in profit and nonprofit organizations, and factors which stimulate this process have been identified. More precisely, factors contributing to organizational learning in board of directors have been critically examined, accreditation as a factor contributing to organizational learning in higher education institutions (HEIs) has been suggested, learning dynamics in university–industry collaborations and inter-professional learning have been examined, as well as organizational learning as a bottom-up approach supported by transformational leaderships.

Conclusions and models provided in the paper need further empirical testing and validation.

Useful implications for practitioners in profit and nonprofit sector have been suggested based on the critical analysis of previous contributions regarding stimulation of organizational learning in stakeholder relations.

Contributions from previous authors have been systemically and critically reviewed, adapted models have been provided and suggestions for practitioners in this regard have been offered.

The purpose of this paper is to describe and position the state-of-the-art of text and image mining methods in business research. By providing a detailed conceptual and technical review of both methods, it aims to increase their utilization in service research.

On a first stage, the authors review business literature in marketing, operations and management concerning the use of text and image mining methods. On a second stage, the authors identify and analyze empirical papers that used text and image mining methods in services journals and premier business. Finally, avenues for further research in services are provided.

The manuscript identifies seven text mining methods and describes their approaches, processes, techniques and algorithms, involved in their implementation. Four of these methods are positioned similarly for image mining. There are 39 papers using text mining in service research, with a focus on measuring consumer sentiment, experiences, and service quality. Due to the nonexistent use of image mining service journals, the authors review their application in marketing and management, and suggest ideas for further research in services.

This manuscript focuses on the different methods and their implementation in service research, but it does not offer a complete review of business literature using text and image mining methods.

The results have a number of implications for the discipline that are presented and discussed. The authors provide research directions using text and image mining methods in service priority areas such as artificial intelligence, frontline employees, transformative consumer research and customer experience.

The manuscript provides an introduction to text and image mining methods to service researchers and practitioners interested in the analysis of unstructured data. This paper provides several suggestions concerning the use of new sources of data (e.g. customer reviews, social media images, employee reviews and emails), measurement of new constructs (beyond sentiment and valence) and the use of more recent methods (e.g. deep learning).