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This paper aims to find an effective and standardized function transformation method to apply in both high-growth original data sequences and low-growth original data sequences, which can improve the accuracy of model prediction in GM(1, 1) forecast.

In GM(1, 1) forecast, many original data sequences need to meet the quasi-exponential characteristic by methods of function transformation. However, many methods of function transformation have complex transformation processes or narrow application range. On the basis of the research results of Ye and Li, the paper presents a standardized approach based on to original data sequences and designs four situations of the standardized approach. By using high-growth and low-growth original data sequences as the objects, respectively, the paper verifies the effectiveness of the proposed method and compares the forecasting effects of GM(1, 1) based on function transformation with the original GM(1, 1).

Most of the results show that function transformations can improve the accuracy of the conventional GM(1, 1) forecast, and transform is a powerful tool to effectively process original data sequence of GM(1, 1) modeling.

GM(1, 1) forecast have been widely used in many fields such as agriculture, economy, meteorology, and geology. The proposed method in this paper can effectively apply to prediction of high-growth original data sequences and low-growth original data sequences, to some extent, enrich and deepen application of GM(1, 1) forecast.

The paper succeeds in providing a standardized approach based on and designs four intensity levels for different data sequences based on the standardized approach.

Inter-organisational governance is an important enabler for information processing, particularly in relationships undergoing digital transformation (DT) where partners depend on each other for information in decision-making. Based on information processing theory (IPT), the authors theoretically and empirically investigate how governance mechanisms address information asymmetry (uncertainty and equivocality) arising in capturing, sharing and interpreting information generated by digital technologies.

IPT is applied to four cases of public–private relationships in the Dutch infrastructure sector that aim to enhance the quantity and quality of information-based decision-making by implementing digital technologies. The investigated relationships are characterised by differing degrees and types of information uncertainty and equivocality. The authors build on rich data sets including archival data, observations, contract documents and interviews.

Addressing information uncertainty requires invoking contractual control and coordination. Contract clauses should be precise and incentive schemes functional in terms of information requirements. Information equivocality is best addressed by using relational governance. Identifying information requirements and reducing information uncertainty are a prerequisite for the transformation activities that organisations perform to reduce information equivocality.

The study offers insights into the roles of both governance mechanisms in managing information asymmetry in public–private relationships. The study uncovers key activities for gathering, sharing and transforming information when using digital technologies.

This study draws on IPT to study public–private relationships undergoing DT. The study links contractual control and coordination as well as relational governance mechanisms to information-processing activities that organisations deploy to reduce information uncertainty and equivocality.

The aim of this study was to identify interventions for and mechanisms of the digital transformation of purchasing and supply management (PSM) processes. The digital transformation of tactical and operational PSM processes has often progressed slowly despite the solid knowledge of advanced technologies.

This study used a qualitative exploratory approach based on 14 interviews with PSM executives from firms that are continuously working toward using advanced technologies in their PSM processes but have not yet gained full strategic benefits from digital transformation.

This study formulates five propositions regarding interventions and mechanisms that can positively influence the digital transformation of PSM processes. The main intervention in this regard is the renewal of data infrastructure, including platforms. PSM-related data should meet needs from both tactical and operational viewpoints. When applications serve as a source of data, they support digital transformation. Mechanisms such as supplier measurement and process improvement are outcomes of the digital transformation of PSM processes.

This study highlights the importance of common data sets for tactical and operational purchasing. These purchasing data should be owned and served by a cross-functional team. To create this interoperability, a firm needs global governance of open standards.

This study makes a theoretical contribution to the discussion of what kind of interventions positively influence on the digital transformation of PSM processes. Specifically, this study explains the integration needs of data and applications.

Constrained clustering is an important recent development in clustering literature. The goal of an algorithm in constrained clustering research is to improve the quality of clustering by making use of background knowledge. The purpose of this paper is to suggest a new perspective for constrained clustering, by finding an effective transformation of data into target space on the reference of background knowledge given in the form of pairwise must- and cannot-link constraints.

Most of existing methods in constrained clustering are limited to learn a distance metric or kernel matrix from the background knowledge while looking for transformation of data in target space. Unlike previous efforts, the author presents a non-linear method for constraint clustering, whose basic idea is to use different non-linear functions for each dimension in target space.

The outcome of the paper is a novel non-linear method for constrained clustering which uses different non-linear functions for each dimension in target space. The proposed method for a particular case is formulated and explained for quadratic functions. To reduce the number of optimization parameters, the proposed method is modified to relax the quadratic function and approximate it by a factorized version that is easier to solve. Experimental results on synthetic and real-world data demonstrate the efficacy of the proposed method.

This study proposes a new direction to the problem of constrained clustering by learning a non-linear transformation of data into target space without using kernel functions. This work will assist researchers to start development of new methods based on the proposed framework which will potentially provide them with new research topics.

This study aims to investigate the critical success factors of digital transformation in the construction industry and identify whether the respondents' profiles influence their perceptions of critical success factors for digital transformation.

To achieve the objectives, a literature review was first conducted based on technology-organization-environment (TOE) framework. Then a questionnaire survey was carried out. A total of 86 people were surveyed in this study, mainly from the construction industry. At the level of data processing, SPSS was used for analysis. Among the main tests used were the Shapiro–Wilk test, reliability analysis, mean rank analysis, Kruskal–Wallis test and Mann–Whitney U test.

The study identified 15 critical success factors of digital transformation and found the three most important factors of digital transformation. Furthermore, respondents with different years of experience, enterprises with different sizes and different years made no difference in the perception of factors. Respondents' different occupations and types of enterprises created a bias in the perception of factors for digital transformation.

Firstly, the small sample size of the questionnaire limits the reference value of data analysis for certain groups. In addition, this study focuses broadly on construction enterprises without specifically examining different types of enterprises, thus lacking depth in its findings.

This study establishes a connection between TOE theory and the construction industry through an extensive literature review, identifying relevant factors and providing a reference for future research.

The study's results would enrich the research on digital transformation in the construction industry and provide a reference for the digital transformation of construction enterprises.

This paper aims to evaluate the state-of-the-art in CSV uplift tools. Based on this evaluation, a method that incorporates data transformations into uplift mapping languages by means of functions is proposed and evaluated. Typically, tools that map non-resource description framework (RDF) data into RDF format rely on the technology native to the source of the data when data transformation is required. Depending on the data format, data manipulation can be performed using underlying technology, such as relational database management system (RDBMS) for relational databases or XPath for XML. For CSV/Tabular data, there is no such underlying technology, and instead, it requires either a transformation of source data into another format or pre/post-processing techniques.

To evaluate the state-of-the-art in CSV uplift tools, the authors present a comparison framework and have applied it to such tools. A key feature evaluated in the comparison framework is data transformation functions. They argue that existing approaches for transformation functions are complex – in that a number of steps and tools are required. The proposed method, FunUL, in contrast, defines functions independent of the source data being mapped into RDF, as resources within the mapping itself.

The approach was evaluated using two typical real-world use cases. The authors have compared how well our approach and others (that include transformation functions as part of the uplift mapping) could implement an uplift mapping from CSV/Tabular into RDF. This comparison indicates that the authors’ approach performs well for these use cases.

This paper presents a comparison framework and applies it to the state-of-the-art in CSV uplift tools. Furthermore, the authors describe FunUL, which, unlike other related work, defines functions as resources within the uplift mapping itself, integrating data transformation functions and mapping definitions. This makes the generation of RDF from source data transparent and traceable. Moreover, as functions are defined as resources, these can be reused multiple times within mappings.

The purpose of this paper is to utilize the proposed function transformation to make the original data series meet the properties of smooth ratio being lessen and stepwise ratio deviation being reduced, so that to improve the accuracy of grey forecasting model.

According to the characteristics of anti-cotangent functional graph variation, the theory of functional transformation and grey system modeling, the authors proposed a grey model based on the transformation of Aarc cot x+B function.

The calculated result of practical example shows that the proposed method is both valid on improving fitting effectiveness and forecasting accuracy.

The proposed method in this paper can effectively improve the accuracy of forecasting of high-growth original data series (derivative of data series is not only greater than 1 but also increasing).

The paper succeeds in providing an effective function transformation to make the smooth ratio and stepwise ratio deviation reduced significantly.

The purpose of this paper is to propose a practicable data-driven theory for the implementation and management of organizational change by combining the organization ambidexterity research and the organization change management research.

This study is based on the qualitative approach and uses a single case (in-depth investigation approach) study to come up with a data-driven theory, which is usable in the context of organizational change management and organizational ambidexterity (OA). Besides, in-depth interviews of change management practitioners, this study uses various sources of secondary information.

The study finds that owing to the reactive, ad hoc, and discontinuous nature of change often triggered by external factors or internal crisis within the organization, an organization need to continually engage with the existing data. The outcome must be driven toward preparing for the change through data engagement, implementation and reinforcement. The authors found that in order to be successful it is essential to have a strategy, set-up the right operating model, be clear on the scope of the change management work-stream and continuously monitor the progress through defined milestones and acceptance criteria. For companies targeting to achieve competitive differentiation through ambidexterity, a well-grounded change management program is the key for the success.

The study suggests that there is little work combining organizational change management and OA from a practitioner’s point of view. Accordingly, the authors propose a new data-driven organizational change management theory, which the authors term as the tripod theory for organizational change management. A practitioner’s perspective on the topic using a case study of an insurance company’s data transformation and a framework for structuring the change management program makes a meaningful contribution to the existing literature.

To create the expected value and benefits through open data, appropriate provision and usage of data are required simultaneously. However, the level of provision and usage of open data differs from country to country. Moreover, previous research on open data has only focused on either open data provision or usage. To fill the research gap, the purpose of this paper is threefold: first, to understand the current status of the provision and usage of open data; second, to identify patterns in the provision and usage of open data; and third, to provide appropriate future directions and guidelines for the transformation paths of each pattern.

The authors analyzed the data collected from open data portals of 13 countries that provide information on the provision and usage of open data together.

The authors identified four patterns of the provision and usage of open data, namely, availability-driven, government-driven, market-driven and interaction-driven patterns. Furthermore, three strategic paths of transformation reach a high level of open data provision and usage, namely, data provision-focused, data usage-focused and balanced transformation paths.

This study provides a foundation that enables researchers to build a holistic theory that can integrate fragmented and incomplete knowledge of open data and usage, particularly in the context of government.

This study aims to explore the implementation of data analytics in the Big-Four accounting firms, including the extent to which a digital transformation is changing the work of financial auditors, why it is doing so and how these firms are managing the transformation process.

The authors conducted 23 interviews with 20 participants across four hierarchical levels from three of the Big-Four accounting firms in New Zealand.

The firms have entered the era of “smart audit systems”, in which auditors provide deep business insights that are communicated more effectively through data visualisation. The full potential, however, of data analytics depends not only on the transformation process within accounting firms but also on improvement in the quality of IT systems in client companies. The appointment of transformation managers, the recruitment of technology-savvy graduates and the provision of extensive training are helping to embed data analytics in the Big-Four firms. Accounting graduates in financial audit now need to show that they have the aptitude to become “citizen data scientists”.

The findings explain how data analytics is being embraced in the Big-Four auditing firms and underline the implications for those who work in them.

The findings challenge the “technological reluctance” thesis. In contrast, the authors observe a climate of positive attitudes towards new technology and accompanying actions in the Big-Four firms. The authors show how branches of the Big-Four firms operating distantly from their global headquarters, and with smaller economies of scale, are implementing the new technologies that characterise their global firms.