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The paper aims to capture the nuances of two client–supplier relationships to offer new insights on the influences of transactional, knowledge and social elements in outsourcing partnerships.

The study has used descriptive case studies with narrative storylines. Interviews were conducted with three relationship managers (boundary gatekeepers) to understand preferred governance practices between clients and suppliers in diverse economic markets.

Experiences of three real-life cases engaged in offshore outsourcing have helped to identify the market, operational knowledge and social influences in a relational exchange. Findings reveal that offshore partnerships are first constituted with service-level agreements, which set control measures and layout business expectations from both partners. Boundary gatekeepers bring further accountability across firms by designing social networks for capturing and sharing of knowledge, thereby reducing each partner’s perception of risk. As firms evaluate transactional, knowledge and social elements for building a futuristic relational exchange, more disaggregated and dispersed enterprises evolve as new opportunities are explored in foreign markets.

The retrospective nature of the client–supplier partnership is a limitation in this research study. However, retrospection adds to experience, and to practice perspectives made in hindsight, and therefore has a positive influence in this study.

This paper shares real-world experiences that can be used by scholars and practitioners to better understand how relational governance practices operate in a global socio-economic setting.

Quality management standards (e.g. ISO 9001) lead to process conformance in the realization of quality goods and services; however, they can be rather document intensive. This paper investigates documentation practices used for aligning “light-weight” Scrum methods with ISO 9001 in a leading healthcare software firm.

The authors investigated how “light-weight” Scrum approaches fit with organizational documentation practices for ISO 9001 compliance in one leading healthcare software development firm. Three investigative rounds were conducted with software professionals having different Scrum roles to understand their challenges in maintaining process documentation with Scrum methods.

ISO standards stipulate certain mandatory documentation as evidence that certain pre-defined processes are followed in the build-up of quality goods and services. However, this may result in “heavy-weight” document driven approaches that interfere with “light-weight” Scrum methods. Case study findings reveal tensions faced by software professionals in maintaining the ISO 9001 documentation. That is, while some level of documentation is considered useful, software professionals consider certain other documentation tasks to be excessive and cumbersome. Further, many operational documents were written retrospectively for administrative compliance, leading to reduced, incomplete and ambiguous descriptions.

The study provides much value for practitioners in adapting their documentation with ongoing operational processes. Further, the critique on current ISO 9001 implementations in Agile environments has implications for future documentation practice.

The empirically drawn findings showcase some of the challenges in maintaining ISO 9001 documentation within Scrum projects. The study has contributed to both theory and practice in relation to the co-existence of ISO drawn standards with Agile approaches used for software development.

The purpose of this paper is to propose a process mining approach to help in making early predictions to improve students’ learning experience in massive open online courses (MOOCs). It investigates the impact of various machine learning techniques in combination with process mining features to measure effectiveness of these techniques.

Student’s data (e.g. assessment grades, demographic information) and weekly interaction data based on event logs (e.g. video lecture interaction, solution submission time, time spent weekly) have guided this design. This study evaluates four machine learning classification techniques used in the literature (logistic regression (LR), Naïve Bayes (NB), random forest (RF) and K-nearest neighbor) to monitor weekly progression of students’ performance and to predict their overall performance outcome. Two data sets – one, with traditional features and second, with features obtained from process conformance testing – have been used.

The results show that techniques used in the study are able to make predictions on the performance of students. Overall accuracy (F1-score, area under curve) of machine learning techniques can be improved by integrating process mining features with standard features. Specifically, the use of LR and NB classifiers outperforms other techniques in a statistical significant way.

Although MOOCs provide a platform for learning in highly scalable and flexible manner, they are prone to early dropout and low completion rate. This study outlines a data-driven approach to improve students’ learning experience and decrease the dropout rate.

Early predictions based on individual’s participation can help educators provide support to students who are struggling in the course.

This study outlines the innovative use of process mining techniques in education data mining to help educators gather data-driven insight on student performances in the enrolled courses.

Mobile apps offer construction workers a quick, affordable and user-friendly platform for meeting their information, communication and computing needs, with nearly 13,000 construction apps currently available in the market. This study aims to report construction managers’ perspectives on the uptake of mobile apps in the New Zealand construction industry.

Exploratory research methods were used in two stages. First stage involved interviews with 14 construction managers who were registered with the New Zealand Registered Master Builders Association, followed by an online questionnaire in the second stage. The link of online questionnaire was sent to all members of participating professional organisations by their administrators. In total, 228 responses were received, of which 60 per cent participants completed the entire questionnaire.

Results show an overall positive attitude towards the use of apps. Perceptions of top management personnel differed slightly from those of middle managers; the former expressed interest in apps usage at strategic level such as improving long-term client relationship management and satisfaction; while the latter were more interested in the apps use at operational and tactical levels such as task- or project-level productivity improvement.

Though bias has been minimised by giving equal opportunity to each member of trade and professional organisations to participate in this survey, of the 228 responses received, only 60 per cent participants completed the entire questionnaire. This is below the minimum requirement for a holistic representation of views. As a result, the findings might not be generalised beyond the study’s scope.

The study provides new insights on the uptake of smartphone apps in New Zealand’s construction sector from the perspective of construction managers who make strategic decisions. The findings have implications for policy formulation and implementation in regards to the use of mobile apps for productivity improvement in the sector. Mobile apps developers could also gain understanding on functional needs and preferences of the construction workforce, which will help in development of more relevant apps.

The purpose of this paper is to seek answers to questions on how equity of information literacy and learning outcomes have evolved with the ongoing advances in technologies in teaching and learning across schools. The authors’ report on a five-year long bring your own device (BYOD) journey of one school, which was one of the earliest adopters of one-to-one learning devices in New Zealand.

Using a socio-cultural ecological lens for analysis, a longitudinal study has investigated aspects of how digital/information literacy, computer self-efficacy, and nature of technology usage are transforming school and classroom curriculum practices.

Findings of this study reveal a significant shift in social and academic boundaries between formal and informal learning spaces. One-to-one learning devices provide the link between school and home, as students take more ownership of their learning, and teachers become facilitators. Curricula changes and proper technological support systems introduced in the school structures have given agency to students resulting in greater acceptance of the BYOD policy and extensions to learning beyond formal classroom spaces. Digital divide amongst learners has evolved beyond equity in access and equity in capabilities to become more inclusive, thereby paving the way for equity in learning outcomes.

This study has been conducted in a school which is located in a relatively high socio-economic region. To achieve a more holistic view, there is a need for further studies to be conducted in schools from low socio-economic communities.

This paper adds to the existing literature by sharing teacher reflections on their use of innovative pedagogies to bring changes to classroom curricular practice.

Over the past few years, technology-mediated learning has established itself as a valuable pathway towards learners’ academic and social development. However, within the adoption stages of information and communications technology-enabled education, further questions have been raised in terms of equity of information literacy and learning outcomes. For the past three years, the authors have been working with one of the earliest secondary schools in New Zealand to introduce a Bring Your Own Device (BYOD) policy. In this paper, the authors present the findings of a longitudinal investigation into the BYOD project, which offers new insights into the digital divide issues in the context of evolving teaching and learning practices across three levels, namely, digital access, digital capability and digital outcome.

This study is an empirically grounded longitudinal case research conducted over a three-year period in one secondary school in New Zealand. This research has included a number of methods, including surveys, interviews and classroom observations, to gather qualitative data from various stakeholders (teachers, students and parents).

The findings from the study of the BYOD project inform of digital divide issues in the context of evolving teaching and learning practices across formal and informal spaces. The authors explored how the BYOD policy has influenced existing divides in the learning process across three levels, namely, digital access, digital capability and digital outcome. The result sheds light on key issues affecting the learning process to contextualise factors in the three-level digital divide for the BYOD technology adoption process in classroom settings.

The study presents findings from an ongoing investigation of one secondary school, an early adopter of the BYOD policy. While the authors have followed the school for three years, more in-depth studies on how teaching and learning practices are evolving across formal and informal spaces will be further qualified in the next stages of data collection.

The study contributes to new knowledge on how digital inclusion can be supported beyond mere access to meaningful use of technology to reinforce student learning and their overall skill development.

This paper aims to evaluate educational data mining methods to increase the predictive accuracy of student academic performance for a university course setting. Student engagement data collected in real time and over self-paced activities assisted this investigation.

Classification data mining techniques have been adapted to predict students’ academic performance. Four algorithms, Naïve Bayes, Logistic Regression, k-Nearest Neighbour and Random Forest, were used to generate predictive models. Process mining features have also been integrated to determine their effectiveness in improving the accuracy of predictions.

The results show that when general features derived from student activities are combined with process mining features, there is some improvement in the accuracy of the predictions. Of the four algorithms, the study finds Random Forest to be more accurate than the other three algorithms in a statistically significant way. The validation of the best-known classifier model is then tested by predicting students’ final-year academic performance for the subsequent year.

The present study was limited to datasets gathered over one semester and for one course. The outcomes would be more promising if the dataset comprised more courses. Moreover, the addition of demographic information could have provided further representations of students’ performance. Future work will address some of these limitations.

The model developed from this research can provide value to institutions in making process- and data-driven predictions on students’ academic performances.

The use of technology in education is still seen as a symbol of modernity in Indonesia. Without adequate technological infrastructural support from institutions, teachers develop ways to incorporate technology into their classrooms. The purpose of this paper is to investigate the affective domain in learning mathematics with technology across genders and across two student groups, where in one group the students shared learning devices, while the other group of students used individual devices.

The study adopts both quantitative and qualitative methods and is based on data collected from five secondary schools in Indonesia.

The findings reveal attitudinal differences are associated with technology usage in mathematics classrooms. Quantitative measurements across four attitudinal subscales—mathematics motivation, attitudes to the use of technology in mathematics, technological confidence and mathematics confidence—indicate that affordances in technologies influence boys and girls attitudes; while qualitative data share further insights on gender perspectives related to attitudinal differences.

Appropriate pedagogical approaches with equitable access to technologies are important for engaging students in learning mathematics with technology.

This empirical study reveals aspects related to student participation with technologies in classrooms, which has important implications for student development.

The study contributes to literature on mathematics education related to the use of learning technologies in secondary schools of a developing country.

Organizations invest in enterprise systems (ESs) with an expectation to share digital information from disparate sources to improve organizational effectiveness. This study aims to examine how organizations realize digital business strategies using an ES. It does so by evaluating the ES data support activities for knowledge creation, particularly how ES data are transformed into corporate knowledge in relevance to business strategies sought. Further, how this knowledge leads to realization of the business benefits. The linkage between establishing digital business strategy, utilization of ES data in decision‐making processes, and realized or unrealized benefits provides the reason for this study.

This study develops and utilizes a transformational model of how ES data are transformed into knowledge and results to evaluate the role of digital business strategies in achieving benefits using an ES. Semi‐structured interviews are first conducted with ES vendors, consultants and IT research firms to understand the process of ES data transformation for realizing business strategies from their perspective. This is followed by three in‐depth cases (two large and one medium‐sized organization) who have implemented ESs. The empirical data are analyzed using the condensation approach. This method condenses the data into multiple groups according to pre‐defined categories, which follow the scope of the research questions.

The key findings emphasize that strategic benefit realization from an ES implementation is a holistic process that not only includes the essential data and technology factors, but also includes factors such as digital business strategy deployment, people and process management, and skills and competency development. Although many companies are mature with their ES implementation, these firms have only recently started aligning their ES capabilities with digital business strategies correlating data, decisions, and actions to maximize business value from their ES investment.

The findings reflect the views of two large and one medium‐sized organization in the manufacturing sector. Although the evidence of the benefit realization process success and its results is more prominent in larger organizations than medium‐sized, it may not be generalized that smaller firms cannot achieve these results. Exploration of these aspects in smaller firms or a different industry sector such as retail/service would be of value.

The paper highlights the importance of tools and practices for accessing relevant information through an integrated ES so that competent decisions can be established towards achieving digital business strategies, and optimizing organizational performance. Knowledge is a key factor in this process.

The paper evaluates a holistic framework for utilization of ES data in realizing digital business strategies. Thus, it develops an enhanced transformational cycle model for ES data transformation into knowledge and results, which maintains to build up the transformational process success in the long term.