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Data science industry is a multidisciplinary field that deals with a large amount of data and derives useful information for taking routine and strategic business decisions. The purpose of this article is to examine the relationship between pricing models, engagement models, and firm performance (FP). This study also aims at uncovering the most effective pricing model and engagement model for improving FP.

Indian data scientists were the respondents of the study. A total of 213 responses were carefully chosen. The data were analyzed using structural equations on Statistical Package for Social Sciences-Analysis of Moment Structures (SPSS-AMOS) version 25 software.

The findings of the study suggested the positive and significant impact of pricing models and engagement models on FP. Value-based pricing strategies have the maximum impact on FP. On the other hand, managed services have a higher influence on FP.

By developing a multi-faceted framework, this study is a novel contribution to the field of business strategy, especially for the data science industry.

The main aim of this study is to explore the relationship between information system flexibility and dynamic capabilities to build sustainable and net zero supply chains under the influence of environmental dynamism.

We have formulated a self-administered survey, with 359 participants contributing responses. Prior to delving into foundational assumptions, such as homoscedasticity and normality, a nonresponse bias analysis was executed. The integrity of the data, in terms of reliability and construct validity, was gauged using confirmatory factor analysis. Subsequent regression outputs corroborated all the proposed assumptions, fortifying the extant scholarly literature.

The empirical findings of this research underscore a positive correlation between Information system flexibility, dynamic capabilities and a net zero supply chain, especially in the context of environmental dynamism. Data sourced from the cement manufacturing sector support these observations. We also found that environmental dynamism moderates the relationship between data analytics capability and sustainable supply chain flexibility but does not moderate the relationship between Resource flexibility and sustainable supply chain flexibility. Additionally, this research strengthens the foundational principles of the dynamic capability theory.

The conceptual framework elucidates the interplay between information system flexibility, dynamic capabilities, and sustainable supply chain flexibility, emphasizing their collective contribution towards achieving sustainable chain net zero, introducing environmental dynamics as a moderating variable that augments the scholarly discourse with a nuanced layer of analytical depth.

Due to the rapid development of artificial intelligence (AI) technology, increasing the use of AI in healthcare is critical, but few studies have explored the extent to which physicians cooperate with AI in their work to achieve productive and innovative performance, which is a key issue in operations management (OM). We conducted empirical research to answer this question.

We developed a conceptual model based on the ambidextrous perspective. To test our model, we collected data from 200 Chinese hospitals. One senior and one junior physician from each hospital participated in this research so that we could get a more comprehensive view. Based on the sample of 400 participants and the conceptual model, we examined whether different types of AI use have distinct impacts on physicians’ productivity and innovation by conducting hierarchical regression and post hoc tests. We also introduced team psychological safety climate (TPSC) and AI technology uncertainty (AITU) as moderators to investigate this topic in further detail.

We found that augmentation AI use is positively related to overall productivity and innovative job performance, while automation AI use is negatively related to these two outcomes. Furthermore, we focused on the impacts of the ambidextrous use of AI on these two outcomes. The results highlight the positive impacts of complementary use on both outcomes and the negative impact of balance on innovative job performance. TPSC enhances the positive impacts of complementary use on productivity, whereas AITU inhibits the negative impacts of automation and balanced use on innovative job performance.

In the age of AI, organizations face greater trade-offs between performance and technology management. This study contributes to the OM literature from the perspectives of operational performance and technology management in three ways. First, it distinguishes among different AI implementations and their diverse impacts on productivity and innovative performance. Second, it identifies the different conditions under which automation AI use and augmentation are superior. Third, it extends the ambidextrous perspective by becoming an early adopter of this approach to explore the implications of different types of AI use in light of contingency factors.

Extending the springboard perspective with the resource dependence theory, the authors posit that cross-border mergers and acquisitions (M&As) are a new channel for emerging economy firms (EEFs) to enhance their technology capabilities. This study aims to examine the impact of cross-border M&As initiated by EEFs on their technology augmentation vis-à-vis matched domestic M&A cases and investigate the factors influencing the difference in post-merger innovation capability.

This paper estimates the post-acquisition innovation capability of acquirers from emerging economies (EEs) that engage in cross-border M&As. To remove possible selection bias, the authors leverage a difference-in-difference-style approach in combination with a matched sample constructed by pairing each cross-border M&A case with a similar domestic deal. The data set contains 266 cross-border M&As and 266 matched domestic M&A deals between 2003 and 2011, whereby acquirers are based in 6 EEs and targets are in 36 countries consisting of both EEs and advanced economies (AEs).

The present empirical results show that cross-border M&As engaged by EEFs are an important engine for improving EEFs’ innovation capability through technology augmentation. The main empirical results are as follows. First, compared with matched domestic acquirers with similar characteristics, EE cross-border M&As have a positive effect on innovation capability. Second, the positive effect of the EEFs’ cross-border M&As relative to the matched domestic M&As on innovation capability is driven largely by cross-border M&As with targets in AEs. Third, the increase in post-M&A innovation capability of the EE cross-border acquirers comes mainly from deals where targets are based in countries with relatively superior human capital and innovation capability than those of the acquirers.

To the best of the authors’ knowledge, this study is the first systematic study of whether cross-border M&As serve as an effective channel of technology augmentation for EE acquirers compared to matched domestic acquirers with similar characteristics.

Create and share a MATLAB library that performs data augmentation algorithms for audio data. This study aims to help machine learning researchers to improve their models using the algorithms proposed by the authors.

The authors structured our library into methods to augment raw audio data and spectrograms. In the paper, the authors describe the structure of the library and give a brief explanation of how every function works. The authors then perform experiments to show that the library is effective.

The authors prove that the library is efficient using a competitive dataset. The authors try multiple data augmentation approaches proposed by them and show that they improve the performance.

A MATLAB library specifically designed for data augmentation was not available before. The authors are the first to provide an efficient and parallel implementation of a large number of algorithms.

Although businesses continue to take up artificial intelligence (AI), concerns remain that companies are not realising the full value of their investments. The study aims to provide insights into how AI creates business value by investigating the mediating role of Business Process Management (BPM) capabilities.

The integrative model of IT Business Value was contextualised, and structural equation modelling was applied to validate the proposed serial multiple mediation model using a sample of 448 organisations based in the EU.

The results validate the proposed serial multiple mediation model according to which AI adoption increases organisational performance through decision-making and business process performance. Process automation, organisational learning and process innovation are significant complementary partial mediators, thereby shedding light on how AI creates business value.

In pursuing a complex nomological framework, multiple perspectives on realising business value from AI investments were incorporated. Several moderators presenting complementary organisational resources (e.g. culture, digital maturity, BPM maturity) could be included to identify behaviour in more complex relationships. The ethical and moral issues surrounding AI and its use could also be examined.

The provided insights can help guide organisations towards the most promising AI activities of process automation with AI-enabled decision-making, organisational learning and process innovation to yield business value.

While previous research assumed a moderated relationship, this study extends the growing literature on AI business value by empirically investigating a comprehensive nomological network that links AI adoption to organisational performance in a BPM setting.

This paper aims to propose a new deep learning technique to detect the type of material to improve automated construction quality monitoring.

A new data augmentation approach that has improved the model robustness against different illumination conditions and overfitting is proposed. This study uses data augmentation at test time and adds outlier samples to training set to prevent over-fitted network training. For data augmentation at test time, five segments are extracted from each sample image and fed to the network. For these images, the network outputting average values is used as the final prediction. Then, the proposed approach is evaluated on multiple deep networks used as material classifiers. The fully connected layers are removed from the end of the networks, and only convolutional layers are retained.

The proposed method is evaluated on recognizing 11 types of building materials which include 1,231 images taken from several construction sites. Each image resolution is 4,000 × 3,000. The images are captured with different illumination and camera positions. Different illumination conditions lead to trained networks that are more robust against various environmental conditions. Using VGG16 model, an accuracy of 97.35% is achieved outperforming existing approaches.

It is believed that the proposed method presents a new and robust tool for detecting and classifying different material types. The automated detection of material will aid to monitor the quality and see whether the right type of material has been used in the project based on contract specifications. In addition, the proposed model can be used as a guideline for performing quality control (QC) in construction projects based on project quality plan. It can also be used as an input for automated progress monitoring because the material type detection will provide a critical input for object detection.

Several studies have been conducted to perform quality management, but there are some issues that need to be addressed. In most previous studies, a very limited number of material types were examined. In addition, although some studies have reported high accuracy to detect material types (Bunrit et al., 2020), their accuracy is dramatically reduced when they are used to detect materials with similar texture and color. In this research, the authors propose a new method to solve the mentioned shortcomings.

Technology is revolutionizing the management logic of service systems. The increasing use of artificial intelligence (AI), in particular, is challenging interaction between humans and machines changing the service systems’ value co-creation configurations and logic. To envision possible future scenarios, this paper aims to reflect upon how the humans’ use of AI technology can impact value co-creation.

The study is developed, at a conceptual level, using selected elements from managerial and marketing theoretical frameworks interested in value co-creation – Service-Dominant Logic, Service Science and Viable Systems Approach (VSA) – used as interpretative tools to reframe value co-creation in the digital age.

The interpretative approach adopted and, in particular, the new VSA notion of Intelligence Augmentation (IA), in the perspective of the information variety model, shed new light on value co-creation in the digital age framing a possible “IA effect” that can empower value co-creation in complex decision-making contexts.

The study provides insights useful in the design and management of service systems suggesting a rethinking of the view of AI as a means for mainly increasing the smartness of service systems and a new focus on the enhancement of the human resources contribution to make the service systems wiser.

The paper provides a refocused interpretative view of the interaction between humans and AI that looks at a possible positive impact of the use of AI on humans in terms of augmented decision-making capabilities in conditions of complexity.

In this article, we reflect on how smart technology is transforming service research discourses about service innovation and value co-creation. We adopt the concept of technology smartness’ to refer to the ability of technology to sense, adapt and learn from interactions. Accordingly, we seek to address how smart technologies (i.e. cognitive and distributed technology) can be powerful resources, capable of innovating in relation to actors’ agency, the structure of the service ecosystem and value co-creation practices.

This conceptual article integrates evidence from the existing theories with illustrative examples to advance research on service innovation and value co-creation.

Through the performative utterances of new tech words, such as onlife and materiality, this article identifies the emergence of innovative forms of agency and structure. Onlife agency entails automated, relational and performative forms, which provide for new decision-making capabilities and expanded opportunities to co-create value. Phygital materiality pertains to new structural features, comprised of new resources and contexts that have distinctive intelligence, autonomy and performativity. The dialectic between onlife agency and phygital materiality (structure) lies in the agencement of smart tech–enabled value co-creation practices based on the notion of becoming that involves not only resources but also actors and contexts.

This paper proposes a novel conceptual framework that advances a tech-based ecology for service ecosystems, in which value co-creation is enacted by the smartness of technology, which emerges through systemic and performative intra-actions between actors (onlife agency), resources and contexts (phygital materiality and structure).

This study aims to empirically examine the impact of digital finance on spatial urbanization and elucidate its underlying mechanisms.

Using panel data of Chinese prefecture-level cities from 2011 to 2021, and using a spatial dynamic panel model, the authors analyzed the effects of digital finance on spatial urbanization and the mechanism of its action.

The findings of the study reveal that digital finance, along with its sub-dimensions, namely coverage breadth, usage depth and digitization degree, all contribute to the enhancement of spatial urbanization. The information channel effect generated by the development of postal and telecommunication businesses, the goods delivery effect generated by the development of retail businesses and the wealth accumulation effect generated by the accumulation of household wealth are all important channels through which digital finance promotes spatial urbanization. Digital finance exerts a significant promotional effect on spatial urbanization in second-tier cities, third-tier cities and their subsequent tiers. This observation alludes to the regionally inclusive nature of spatial urbanization promotion facilitated by digital finance.

The present study endeavors to fill this void by employing empirical analysis to investigate the ramifications of digital finance on spatial urbanization, thereby shedding light on the pivotal role played by digital finance in expediting the progression of spatial urbanization. This study undertakes an examination of the spatial spillover effects, thus providing a comprehensive exposition of the influence of digital finance on spatial urbanization. This study introduces this crucial dimension, and the empirical findings elucidate that digital finance fosters the evolution of spatial urbanization by broadening the coverage of information channels, augmenting the efficiency of goods distribution and enhancing wealth accumulation efficacy.