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The purpose of this article is to elicit understanding of how forgiveness, religion and spirituality, and relationships can better our lives. It draws from the life of Everett L. Worthington, Jr, a positive psychologist and Commonwealth Professor Emeritus at Virginia Commonwealth University. He has published almost 50 books and over 500 scholarly articles or chapters.

This is a case study, followed by a ten-question interview. The core methodology is positive autoethnography which is embraced by Worthington to reveal life lessons from things he has done.

Worthington reveals a life honoring the interwoven lives of people. He has studied forgiveness intensively and finds it to be an essential way of making our way in the world and in a world community that all too often hosts hurt.

An extensive literature has developed to understand what forgiveness is, how it comes about naturally and how the REACH Forgiveness method can help people who struggle to forgive themselves or others, and do it more quickly, thoroughly and frequently. Forgiving has psychological, social, spiritual and physical benefits to the forgiver.

This article is filled with practical information on how to forgive and how to pursue eudaemonia, which Worthington defines as virtue for oneself.

Forgiveness has widespread social implications. Good relationships are those that can help form, maintain, grow and repair when damaged close emotional bonds. Forgiveness helps repair, maintain and grow those bonds.

Worthington has been instrumental in the establishment and growth of the subfield of forgiveness studies and in the study of humility.

GraphQL is a new Open API specification that allows clients to send queries and obtain data flexibly according to their needs. However, a high-complexity GraphQL query may lead to an excessive data volume of the query result, which causes problems such as resource overload of the API server. Therefore, this paper aims to address this issue by predicting the response data volume of a GraphQL query statement.

This paper proposes a GraphQL response data volume prediction approach based on Code2Vec and AutoML. First, a GraphQL query statement is transformed into a path collection of an abstract syntax tree based on the idea of Code2Vec, and then the query is aggregated into a vector with the fixed length. Finally, the response result data volume is predicted by a fully connected neural network. To further improve the prediction accuracy, the prediction results of embedded features are combined with the field features and summary features of the query statement to predict the final response data volume by the AutoML model.

Experiments on two public GraphQL API data sets, GitHub and Yelp, show that the accuracy of the proposed approach is 15.85% and 50.31% higher than existing GraphQL response volume prediction approaches based on machine learning techniques, respectively.

This paper proposes an approach that combines Code2Vec and AutoML for GraphQL query response data volume prediction with higher accuracy.

This study examines the effect of delegation of decision rights, moral justification (MJ), and ethical climate (EC) on managers’ misreporting in the financial services sector. We employed an online research panel called Qualtrics, to collect data based on a sample of 127 middle-level managers from various US financial services firms. We find that MJ mediates the relation between delegation and misreporting, suggesting delegation of decision rights increases employees’ misreporting indirectly by increasing MJ. We also find that EC significantly moderates the relationship between MJ and misreporting. Furthermore, our test of the moderated-mediation effect reveals that the indirect effect of the delegation of decision rights on misreporting through MJ is stronger when there is a higher level of instrumental climate (IC) and a lower level of principle climate (PC).

This study explores factors that influence the initiation of leadership coaching relationships that include externally employed coaches and school administrators.

This qualitative research study includes semi-structured interviews, observations and documents collected across three academic years within the context of a university-based leadership coaching program. Participants included six leadership coaches and six school administrators who participated in the program.

Qualitative analysis indicates that gender and race, prior professional experience, pre-existing professional relationships and the complexity of the district’s organizational structure influence the initiation of the coaching relationship.

Confidentiality restrictions imposed by the program limit opportunities for member checking and other forms of triangulation. Additional data collection using more expansive research methods would help address this limitation.

This study contributes to the sparse literature about leadership coaching with school administrators by describing how different factors influence initiation coaching relationships.