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The aim of this study is to produce information concerning factors which may hamper or promote the use of effectiveness data in secondary health care middle and upper management. Additionally, the study aims to acquire knowledge of the ways in which the managers would generate effectiveness data for use in their own work.

The study was conducted by interviewing department directors, chief medical officers and directors of nursing (n=38) in the surgical, medical and psychiatric divisions of the five largest hospital districts in Finland.

The use of effectiveness data in management was hampered by factors relating to research, managerial work and the organization. Factors relating to the production of effectiveness data, managerial behaviour and a universal demand for evidence‐based operations were considered conducive to the use of such information. Managers would cultivate the use of effectiveness data by improving its accessibility, usability and visibility.

The findings may help healthcare organizations in developing the use of effectiveness data in their decision‐making.

The paper addresses managers’ willingness to apply effectiveness data in decision‐making although the present quality, reliability and accessibility of effectiveness data do not meet the managers’ needs. The use of effectiveness data in management can be influenced by enhancing organizational patterns of action and supporting managerial decision‐making.

There is widespread awareness that evidenced-based policy-making is critical for the long-term development of inclusive education systems. Policy-makers, data collection experts and researchers are aware of the need for data collection at national level that not only meets the requirements of international policy guidelines, but also works within a shared approach so as to promote a synergy of efforts at national and international levels.

Monitoring inclusive education at the system level is increasingly seen as a priority for country and EU level decision-makers. However, what form this monitoring should take and what issues it should focus upon are less clear.

This chapter looks across a number of recent European Agency studies in order to highlight and consider key issues and questions in relation to monitoring the implementation of inclusive education in terms of a system’s efficiency, effectiveness and ability to be equitable for all learners.

By drawing upon the findings of European Agency work considering a range of policy priority areas, it is possible to highlight a number of common factors that apply to monitoring the dimensions of efficiency, effectiveness and equity in different educational contexts or systems.

Despite the increasing role of the data warehouse as a supportive decision-making tool in today's business world, academic research for measuring its effectiveness has been lacking. This paucity of academic interest stimulated us to evaluate data warehousing effectiveness in the organizational context of Jordanian banks.

This paper develops a theoretical model specific to the data warehouse system domain that builds on the DeLone and McLean model. The model is empirically tested by means of structural equation modelling applying the partial least squares approach and using data collected in a survey questionnaire from 127 respondents at Jordanian banks.

Empirical data analysis supported that data quality, system quality, user satisfaction, individual benefits and organizational benefits have made strong contributions to data warehousing effectiveness in our organizational data context.

The results provide a better understanding of the data warehouse effectiveness and its importance in enabling the Jordanian banks to be competitive.

This study is indeed one of the first empirical attempts to measure data warehouse system effectiveness and the first of its kind in an emerging country such as Jordan.

This study aims to achieve three goals: present a holistic, flexible and dynamic model; define the model’s factors and explain how these factors lead to effective and efficient usage of big data; and generate indexes based on experts’ input to rank them based on their importance.

This paper uses the analytic hierarchy process, a quantitative method of decision-making, to evaluate the importance of the factors presented in the model. The fundamental principle of the overall model is that of a dynamo which is borrowed from electromagnetic physics. The model is also based on three IS theories.

Technological advancements and data security are among the most important factors that may impact the effectiveness and efficiency of big data usage. Authentication, governments’ focus on it and transparency and accountability are the most important factors in techno-centric, governmental-centric and user-centric factors, respectively.

The findings of this paper confirmed earlier findings in the literature and quantitatively assessed some of the factors that were conceptually presented. This paper also presented a framework that can be used in future studies.

Policy and decision-makers may need to upgrade pertinent technologies such as internet security, frame policies toward information technology (IT) and train the users.

This paper fills a gap in the literature by presenting a comprehensive study of how different factors dynamically contribute to the effective usage of big data in the public sector. It also quantitatively presents the importance of the factors based on the data collected from 12 IT experts.

The paper discusses theoretical and practical approaches to the problems of assessing library performance and especially of measuring outcomes in the developing world, i.e. the impact and value of the library services for their users and society as a whole. The importance of a systematic evaluation of a library’s activities and the use of modern management methods and tools is emphasized. The need for an appropriate management information system as one of the key elements for the library decision‐making process is demonstrated. The need to enable libraries in developing countries to implement national systems of library statistics is demonstrated. To do this it is important to educate the librarians to collect and use data effectively, to enable them to be able to make the transition from measuring inputs to measuring outputs, and to understand the possible methods and importance of measuring library outcomes.

This article informs school leaders and staffs about existing research findings on the use of data-driven decision-making in creating class rosters. Given that teachers are the most important school-based educational resource, decisions regarding the assignment of students to particular classes and teachers are highly impactful for student learning. Classroom compositions of peers can also influence student learning.

A literature review was conducted on the use of data-driven decision-making in the rostering process. The review addressed the merits of using various quantitative metrics in the rostering process.

Findings revealed that, despite often being purposeful about rostering, school leaders and staffs have generally not engaged in data-driven decision-making in creating class rosters. Using data-driven rostering may have benefits, such as limiting the questionable practice of assigning the least effective teachers in the school to the youngest or lowest performing students. School leaders and staffs may also work to minimize negative peer effects due to concentrating low-achieving, low-income, or disruptive students in any one class. Any data-driven system used in rostering, however, would need to be adequately complex to account for multiple influences on student learning. Based on the research reviewed, quantitative data alone may not be sufficient for effective rostering decisions.

Given the rich data available to school leaders and staffs, data-driven decision-making could inform rostering and contribute to more efficacious and equitable classroom assignments.

This article is the first to summarize relevant research across multiple bodies of literature on the opportunities for and challenges of using data-driven decision-making in creating class rosters.

This paper aims to underscore major opportunities for shared value innovation based on data management efficiency, which has often been overlooked so far. By integrating prior research about digital transformation, shared value creation, entrepreneurial marketing and the innovation-based view of firm performance, it addresses a major gap in the literature.

The innovation-based arguments illustrate how efficient data management may lead to different types of innovation, which provide opportunities for growth and efficiency gains after the coronavirus pandemic.

Many companies’ digitalization programs have concentrated on strengthening the efficiency of current business processes. Thus, these initiatives have contributed to the efficiency of traditional analog activities by using data and smart algorithms. In contrast, the efficiency of the underlying data management was largely neglected, but the COVID-19 pandemic has highlighted its importance. To overcome the limited emphasis on sustainability and efficiency in the digital context, this paper focuses on data management efficiency. After detailing this concept, it is linked to the growing literature about creating shared value, and a process segmentation for implementing shared value innovations in the field of digital efficiency is developed.

The paper extends research into digital transformation by emphasizing that the distinction of effectiveness and efficiency is as relevant in the digital context as in the traditional analog environment. It further provides new insights into creating shared value because it increases the awareness of researchers and managers to consider data management efficiency as a basis for shared value innovation with positive effects on the triple bottom line. The paper also contributes to entrepreneurial marketing research because data management efficiency provides significant opportunities for entrepreneurs, startup firms and innovators in established organizations to develop entirely new markets based on new services, solutions and business models. Finally, the paper deepens the understanding of the innovation-based view of firm performance.

Teacher effectiveness has been a matter of concern not only for the parents and students but also for the policy makers, researchers, and educationists. Drawing from the “self-efficacy” theory (Bandura, 1977), the purpose of this paper is to explore the relationship between teacher self-efficacy and teacher effectiveness. In addition, it explores the role of collaboration among teachers and principal leadership in explaining the above relationship.

Data were collected from 575 secondary school teachers and 6,020 students representing grade 6-12th from 25 privately owned schools in India. Teacher self-efficacy, collaboration and principal leadership were reported by the teachers whereas effectiveness of each teacher was captured from around ten students each who were taught by the corresponding teacher. Data were analyzed using SEM-PLS.

Results confirmed a positive association between teacher self-efficacy and the three dimensions of teacher effectiveness, namely, teacher’s delivery of course information, teacher’s role in facilitating teacher-student interactions, and teacher’s role in regulating students’ learning. Results also confirmed that both collaboration and principal leadership are positively related to teacher self-efficacy.

The results of the study indicate that schools need to focus on enhancing self-efficacy of their teachers and give importance to teacher collaboration and principal leadership in order to improve their effectiveness in terms of delivery of instruction, teacher-student interactions, and regulating student learning.