title: The Nature of the Relationships between Social Networks, Interpersonal Trust, Management Support, and Knowledge Sharing

authors: Al Saifi, Said Abdullah

subjects: Social networks

abstract: Purpose – Past research has shown that, by implementing knowledge sharing, an organisation can maintain its long-term competitive advantage. Hence, this research will explore the nature of the relationships between social networks, interpersonal trust, management support, and knowledge sharing. 

Methodology/approach – In order to achieve the above purpose, semi-structured interviews were used to gather qualitative data. Interviewee participants included top and middle managers and frontline employees. The total number of participants included in the research was 25, equally representing five companies. The core business of all the companies was large-scale manufacturing. A grounded theory approach was used to analyse the data, augmented by the computer-assisted qualitative data analysis software, Nvivo.

Findings – The results reveal that social networks facilitate knowledge sharing in diverse ways. These ways are: the use of multiple communication styles, brainstorming and problem solving, learning and teaching, training, employee rotation, and consultation. In addition, the data from the interviews suggests that, through various factors, the level of interpersonal trust, influences the extent to which employees are willing to share knowledge. These factors are organisational, relational, and individual factors. Furthermore, this study shows that both middle and top managers can play significant roles in facilitating knowledge sharing between employees. These roles are: encouragement of participation in decision-making, provision of recognition, breaking down of barriers, building up of teams, providing training or assigning others to do training, encouragement of training, communication, learning, putting knowledge into practice in the form of processes, and movement of employees.

Research contributions – Six models were developed from the qualitative analysis of the field data. The brainstorming and problem solving model identifies various steps for brainstorming and problem solving which influence social networks and knowledge sharing. The model of learning and teaching explains how social networks can be built based on the receivers’ levels of knowledge, namely, the novice, competent, expert, and proficient levels. The model of factors influencing social networks and knowledge sharing illustrates various factors. These are: using multiple communication strategies, brainstorming and problem solving, learning and teaching, training, employee rotation, and consultation. The model of factors influencing interpersonal trust describes three factors for achieving such trust: organisational, relational, and individual factors. This model also elaborates on three factors that negatively influence interpersonal trust. These are division between departments, team conflict, and a sense of vulnerability. 

The model of the role of management teams in encouraging participation in decision-making elaborates on levels of decision-making among employees and the way in which knowledge flows between top and middle management and frontline employees. The integrative model deciphers the relationships between social networks, interpersonal trust, management support, openness, and knowledge sharing. In addition, the relationships between each area of emphasis and knowledge sharing are included in the model. Based on this model, a survey questionnaire was developed.

These models provide new insights into the relationships between social networks, interpersonal trust, management support, and knowledge sharing. By applying these models to appropriate field situations, both practitioners and academics may be able to improve current practices relating to how knowledge is shared and evolves within organisations.

text: The Nature of the Relationships between Social Networks, Interpersonal Trust, Management Support, and Knowledge Sharing Purpose – Past research has shown that, by implementing knowledge sharing, an organisation can maintain its long-term competitive advantage. Hence, this research will explore the nature of the relationships between social networks, interpersonal trust, management support, and knowledge sharing. 

Methodology/approach – In order to achieve the above purpose, semi-structured interviews were used to gather qualitative data. Interviewee participants included top and middle managers and frontline employees. The total number of participants included in the research was 25, equally representing five companies. The core business of all the companies was large-scale manufacturing. A grounded theory approach was used to analyse the data, augmented by the computer-assisted qualitative data analysis software, Nvivo.

Findings – The results reveal that social networks facilitate knowledge sharing in diverse ways. These ways are: the use of multiple communication styles, brainstorming and problem solving, learning and teaching, training, employee rotation, and consultation. In addition, the data from the interviews suggests that, through various factors, the level of interpersonal trust, influences the extent to which employees are willing to share knowledge. These factors are organisational, relational, and individual factors. Furthermore, this study shows that both middle and top managers can play significant roles in facilitating knowledge sharing between employees. These roles are: encouragement of participation in decision-making, provision of recognition, breaking down of barriers, building up of teams, providing training or assigning others to do training, encouragement of training, communication, learning, putting knowledge into practice in the form of processes, and movement of employees.

Research contributions – Six models were developed from the qualitative analysis of the field data. The brainstorming and problem solving model identifies various steps for brainstorming and problem solving which influence social networks and knowledge sharing. The model of learning and teaching explains how social networks can be built based on the receivers’ levels of knowledge, namely, the novice, competent, expert, and proficient levels. The model of factors influencing social networks and knowledge sharing illustrates various factors. These are: using multiple communication strategies, brainstorming and problem solving, learning and teaching, training, employee rotation, and consultation. The model of factors influencing interpersonal trust describes three factors for achieving such trust: organisational, relational, and individual factors. This model also elaborates on three factors that negatively influence interpersonal trust. These are division between departments, team conflict, and a sense of vulnerability. 

The model of the role of management teams in encouraging participation in decision-making elaborates on levels of decision-making among employees and the way in which knowledge flows between top and middle management and frontline employees. The integrative model deciphers the relationships between social networks, interpersonal trust, management support, openness, and knowledge sharing. In addition, the relationships between each area of emphasis and knowledge sharing are included in the model. Based on this model, a survey questionnaire was developed.

These models provide new insights into the relationships between social networks, interpersonal trust, management support, and knowledge sharing. By applying these models to appropriate field situations, both practitioners and academics may be able to improve current practices relating to how knowledge is shared and evolves within organisations.

year: 2014

title: Domain specific transfer learning using image mixing and stochastic image selection

authors: Coup, Sheldon

subjects: computer science

abstract: Can a gradual transition from the source to the target dataset improve knowledge transfer when fine-tuning a convolutional neural network to a new domain? Can we use training examples from general image datasets to improve classification on fine-grained datasets? We present two image similarity metrics and two methods for progressively transitioning from the source dataset to the target dataset when fine-tuning to a new domain. Preliminary results, using the Flowers 102 dataset, show that the first proposed method, stochastic domain subset training, gives an improvement in classification accuracy compared to standard fine-tuning, for one of the two similarity metrics. However, the second method, continuous domain subset training, results in a reduction in classification performance.

text: Domain specific transfer learning using image mixing and stochastic image selection Can a gradual transition from the source to the target dataset improve knowledge transfer when fine-tuning a convolutional neural network to a new domain? Can we use training examples from general image datasets to improve classification on fine-grained datasets? We present two image similarity metrics and two methods for progressively transitioning from the source dataset to the target dataset when fine-tuning to a new domain. Preliminary results, using the Flowers 102 dataset, show that the first proposed method, stochastic domain subset training, gives an improvement in classification accuracy compared to standard fine-tuning, for one of the two similarity metrics. However, the second method, continuous domain subset training, results in a reduction in classification performance.

year: 2019

title: Te Hokinga ki te Pā Harakeke

authors: Hohepa, Margie Kahukura

abstract: 'Tiakina Te Pā Harakeke' investigates the collective values that nourish and enhances the well-being of tamariki Māori as taonga within our whānau, hapū and iwi. lt explores ways in which Māori values and tikanga are harnessed in contemporary whānau contexts to provide safe, vibrant and enriched childrearing. parenting and child-focussed strategies.

text: Te Hokinga ki te Pā Harakeke 'Tiakina Te Pā Harakeke' investigates the collective values that nourish and enhances the well-being of tamariki Māori as taonga within our whānau, hapū and iwi. lt explores ways in which Māori values and tikanga are harnessed in contemporary whānau contexts to provide safe, vibrant and enriched childrearing. parenting and child-focussed strategies.

year: 2014