Individual and contextual factors have a profound impact on an individual's creativity. In the first part of this research, we concluded that, for a programmer's creativity intention, individual factors including big 5 personality traits and knowledge collection behaviour play a key role. However, it is important to bring contextual factors into the model to provide a holistic understanding. Hence, the objective of the present research is to expand the earlier work by (i) identifying the software engineering occupational stressors relevant to programmers, and (ii) examining their impact as moderators for the relationship between individual factors (i.e., big five personality traits and knowledge collection behaviour) and the creativity intention of the programmer. To analyse the moderating impact of 6 stressors, the survey questionnaire was used to collect data from 294 programmers working in software companies in Pakistan. The data were analysed using the Structural Equation Modelling (SEM) – Partial Least Square (PLS) technique. The findings revealed that in the presence of a moderate level of stress, the relationship between knowledge collection behaviour and creativity intention was strengthened. Furthermore, stressors interacted differently with different personality traits. An overarching statement could be that most of the stressors positively moderated the relationships between different personality traits and creativity intentions. However, contrary to the prior research, the majority of the stressors negatively affected the impact of the openness to experience trait on creativity intention. The research significantly contributes to the body of knowledge of behavioural software engineering. The findings of this research are novel and intriguing in many aspects and will benefit software organizations to increase innovation, by increasing programmers’ creativity through mitigating stress. The study is also one of the few studies which have attempted to understand the interaction between individual and contextual factors with a programmer's creativity.

Abstract To improve and sustain the quality of software products, software process improvement (SPI) is needed. Currently, small and medium software enterprises (SMSEs) represent a high proportion of companies around the world and become a cornerstone in the worldwide industry economy. These companies have realized that improving their process is crucial for success, but they are facing difficulties to implement it due to limited resources, limited knowledge, and time constraints. This study aimed to identify the sustainability success factors (SSFs) that have a positive impact on implementing SPI efforts in SMSEs. Data were collected through a systematic literature review (SLR) approach and quantitatively through a survey questionnaire. A list of 44 SSFs was identified during SLR and empirical study. Results illustrate that there is a positive correlation between the ranks obtained from both dataset (rs (44) = .548, ρ = .001). Therefore, there would be significant differences between the SSFs identified in both datasets. In conclusion, the top‐ranked factors can then be used to guide the SPI coordinators on where they should focus their attention to reach the desired SPI goals, which is crucial to deliver the software products and also facilitates in development of model for SPIs in the future.

Abstract Software Process Improvement (SPI) aims to achieve quality software products in software companies. Ignoring human factors can cause several types of damage in the software development process. Because, Software development is not a purely technical task, but rather it is a relationship between technical and human factors that interact with each other constantly. Currently, Small Medium Enterprises' (SMEs) software development companies in Malaysia still struggle to implement SPI successfully. Therefore, the main objective of this study is to investigate the empirical evidence based on the human factor perspective for helping Malaysian SMEs' software development companies to implement SPI strategies. A questionnaire‐based survey method was used for gathering information from 79 practitioners. The results indicate that 9 factors (employee awareness, leadership involvement, employee involvement, customer involvement, senior management support, staff experience, staff learning, staff skills, and client support) are considered important for implementing SPI in Malaysian SME software development companies. The involvement of both leadership and employees, and getting support from management are the most important human factors that should be considered during the design process of SPI strategies.

Abstract Ineffective software team composition has become recognized as a prominent aspect of software project failures. Reports from results extracted from different theoretical personality models have produced contradicting fits, validity challenges, and missing guidance during software development personnel selection. It is also believed that the technique/s used while developing a model can impact the overall results. Thus, this study aims to (1) discover an effective classification technique to solve the problem and (2) develop a model for composition of the software development team. The model developed was composed of 3 predictors: team role, personality types, and gender variables; it also contained 1 outcome: team performance variable. The techniques used for model development were logistic regression, decision tree, and rough sets theory (RST). Higher prediction accuracy and reduced pattern complexity were the 2 parameters for selecting the effective technique. Based on the results, the Johnson algorithm (JA) of RST appeared to be an effective technique for a team composition model. The study has proposed a set of 24 decision rules for finding effective team members. These rules involve gender classification to highlight the appropriate personality profile for software developers. In the end, this study concludes that selecting an appropriate classification technique is one of the most important factors in developing effective models.