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This study examines the connection between different digital-twin characteristics and organizational control. Specifically, the study aims to examine whether the digital-twin characteristics exploration, guidance and gamification will affect formal and social control.

The study is based on an analysis of survey results from 139 respondents comprising applied university students who use digital twins.

The results offer an interesting contribution to the literature. The authors consider the digital-twin characteristics exploration, guidance and gamification and investigate their contribution to two types of organizational controls: formal and social. The results show that two characteristics, exploration and gamification, affect the extent to which digital twins can be utilized for social control. Exploration and guidance’s role is significant concerning the extent to which digital twins can be utilized for formal control.

This study contributes to literature by considering multiple digital-twin characteristics and their contribution to two different control outcomes. First, it diverges from previous technical-oriented research by investigating digital twins in a human context. Second, the study is the first to examine digital twins’ effects from an organizational control perspective systematically.

Digital transformation shapes industries and influences the forms of collaboration between companies. This study aims to investigate digital business strategy as a key to facilitating collaboration beyond organizational boundaries.

The study focuses on the connection between digital business strategy and collaboration performance. The authors identify five types of digital business strategy elements based on the literature: development, objectives, resources, management capabilities, and digital leadership. The authors then studied the implications of these elements for collaboration performance using a survey. The study’s empirical data were collected from manufacturing and service companies, and 202 valid responses were received. The implications of the research elements were tested through regression analysis, which included the moderating effects of digitally enabled performance measurement.

The theoretical research framework identifies digital business strategy as a key determinant of collaboration performance, thus advancing the understanding of how companies can utilize digital business strategies and achieve enhanced collaboration performance. The results also show that the effect of digital business strategy on collaboration performance may be moderated by digitally enabled performance management.

The results suggest that management capabilities associated with digital strategy are a crucial element in positively influencing collaboration performance. Further, digital strategy-related resources can be better managed with digitally enabled performance measurement system, which is reflected in improved collaborative performance. Thus, companies should invest in management capabilities and connect their digital business strategies and performance measurement systems to develop collaboration in digital transformation.

The study is among the first to translate an empirical understanding of the digital transformation of small and medium-sized companies into a conceptual framework of a digital business strategy.

Recent advancements in Artificial Intelligence (AI) and, at its core, Machine Learning (ML) offer opportunities for organizations to develop new or enhance existing capabilities. Despite the endless possibilities, organizations face operational challenges in harvesting the value of ML-based capabilities (MLbC), and current research has yet to explicate these challenges and theorize their remedies. To bridge the gap, this study explored the current practices to propose a systematic way of orchestrating MLbC development, which is an extension of ongoing digitalization of organizations.

Data were collected from Finland's Artificial Intelligence Accelerator (FAIA) and complemented by follow-up interviews with experts outside FAIA in Europe, China and the United States over four years. Data were analyzed through open coding, thematic analysis and cross-comparison to develop a comprehensive understanding of the MLbC development process.

The analysis identified the main components of MLbC development, its three phases (development, release and operation) and two major MLbC development challenges: Temporal Complexity and Context Sensitivity. The study then introduced Fostering Temporal Congruence and Cultivating Organizational Meta-learning as strategic practices addressing these challenges.

This study offers a better theoretical explanation for the MLbC development process beyond MLOps (Machine Learning Operations) and its hindrances. It also proposes a practical way to align ML-based applications with business needs while accounting for their structural limitations. Beyond the MLbC context, this study offers a strategic framework that can be adapted for different cases of digital transformation that include automation and augmentation of work.

This study aims to study the gas film stiffness of the spiral groove dry gas seal.

The present study represents the first attempt to calculate gas film stiffness in consideration of the slipping effect by using the new test technology for dry gas seals. First, a theoretical model of modified generalized Reynolds equation is derived with slipping effect of a micro gap for spiral groove gas seal. Second, the test technology examines micro-scale gas film vibration and stationary ring vibration to determine gas film stiffness by establishing a dynamic test system.

An optimum value of the spiral angle and groove depth for improved gas film stiffness is clearly seen: the spiral angle is 1.34 rad (76.8º) and the groove depth is 1 × 10^–5 m. Moreover, it can be observed that optimal structural parameters can obtain higher gas film stiffness in the experiment. The average error between experiment and theory is less than 20%.

The present study represents the first attempt to calculate gas film stiffness in consideration of the slipping effect by using the new test technology for dry gas seals.

Simulation-based optimisation (SO) is a popular optimisation approach for building and civil engineering construction planning. However, in the framework of SO, the simulation is continuously invoked during the optimisation trajectory, which increases the computational loads to levels unrealistic for timely construction decisions. Modification on the optimisation settings such as reducing searching ability is a popular method to address this challenge, but the quality measurement of the obtained optimal decisions, also termed as optimisation quality, is also reduced by this setting. Therefore, this study aims to develop an optimisation approach for construction planning that reduces the high computational loads of SO and provides reliable optimisation quality simultaneously.

This study proposes the optimisation approach by modifying the SO framework through establishing an embedded connection between simulation and optimisation technologies. This approach reduces the computational loads and ensures the optimisation quality associated with the conventional SO approach by accurately learning the knowledge from construction simulations using embedded ensemble learning algorithms, which automatically provides efficient and reliable fitness evaluations for optimisation iterations.

A large-scale project application shows that the proposed approach was able to reduce computational loads of SO by approximately 90%. Meanwhile, the proposed approach outperformed SO in terms of optimisation quality when the optimisation has limited searching ability.

The core contribution of this research is to provide an innovative method that improves efficiency and ensures effectiveness, simultaneously, of the well-known SO approach in construction applications. The proposed method is an alternative approach to SO that can run on standard computing platforms and support nearly real-time construction on-site decision-making.

For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch date of the futures to roughly two years after constituent stocks of the futures all becoming shortable, a time period witnessing significantly increased trading activities.

In order to answer questions as follows, this study adopts the neural network for modeling the irregular trading volume series of the CSI300 nearby futures: are the research able to utilize the lags of the trading volume series to make predictions; if this is the case, how far can the predictions go and how accurate can the predictions be; can this research use predictive information from trading volumes of the CSI300 spot and first distant futures for improving prediction accuracy and what is the corresponding magnitude; how sophisticated is the model; and how robust are its predictions?

The results of this study show that a simple neural network model could be constructed with 10 hidden neurons to robustly predict the trading volume of the CSI300 nearby futures using 1–20 min ahead trading volume data. The model leads to the root mean square error of about 955 contracts. Utilizing additional predictive information from trading volumes of the CSI300 spot and first distant futures could further benefit prediction accuracy and the magnitude of improvements is about 1–2%. This benefit is particularly significant when the trading volume of the CSI300 nearby futures is close to be zero. Another benefit, at the cost of the model becoming slightly more sophisticated with more hidden neurons, is that predictions could be generated through 1–30 min ahead trading volume data.

The results of this study could be used for multiple purposes, including designing financial index trading systems and platforms, monitoring systematic financial risks and building financial index price forecasting.

A number of literature contributions have underlined the importance of developing value-added logistics activities or VALS in order to help improve customers’ satisfaction. However, there is usually very little attention given regarding where to perform these VALS. This study aims to: (1) identify a comprehensive set of factors which may influence the location of VALS, (2) to analyze to what extent those factors influence location decisions, and (3) to distinguish the determinants behind the location choices for distribution centers and for the kind of VALS that will be developed in these distribution centers.

In this paper, we will present a conceptual framework on the locations of VALS in view of the identifying determinants for assigning VALS to logistical centers. We argue that the optimal location of VALS is determined by complex interactions between the determinants at the level of the choice of a distribution system, distribution center location factors, and different logistical characteristics regarding products.

The paper aims to throw light on the interactions taking place between the different chemical compositions at various temperatures. P-methylacetophenone is a polar dissolvable, which is positively related by dipole–dipole co-operations and is exceptionally compelling a direct result of the shortfall of any critical primary impacts because of the absence of hydrogen bonds; hence, it might work an enormous dipole moment (μ = 3.62 D). Alcohols additionally assume a significant part in industries and research facilities as reagents and pull in incredible consideration as helpful solvents in the green innovation. They are utilized as pressure-driven liquids in drugs, beauty care products, aromas, paints removers, flavors, dye stuffs and as a germ-free specialist.

Mixtures were prepared by mass in airtight ground stopper bottles. The mass measurements were performed on a digital electronic balance (Mettler Toledo AB135, Switzerland) with an uncertainty of ±0.0001 g. The uncertainty in mole fraction was thus estimated to be less than ±0.0001. The densities of pure liquids and their mixtures were determined using a density meter (DDH-2911, Rudolph Research Analytical). The instrument was calibrated frequently using deionized doubly distilled water and dry air. The estimated uncertainty associated with density measurements is ±0.0003 g.cm⁻³. Viscosities of the pure liquids and their mixtures were determined by using Ostwald’s viscometer. The viscometer was calibrated at each required temperature using doubly distilled water. The viscometer was cleaned, dried and is filled with the sample liquid in a bulb having capacity of 10 ml. The viscometer was then kept in a transparent walled water bath with a thermal stability of ±0.01K for about 20 min to obtain thermal equilibrium. An electronic digital stop watch with an uncertainty of ±0.01 s was used for the flow time measurements for each sample at least four readings were taken and then the average of these was taken.

Negative values of excess molar volume, excess isentropic compressibility and positive values of deviation in viscosity including excess Gibbs energy of activation of viscous flow at different temperatures (303.15, 308.15 and 313.15 K) may be attribution to the specific intermolecular interactions through the hetero-association interaction between the components of the mixtures, resulting in the formation of associated complexes through hydrogen bond interactions.

The excess molar volume (V^E) values were analyzed with the Prigogine–Flory–Patterson theory, which demonstrated that the free volume contribution is the one of the factors influencing negative values of excess molar quantities. The Jouyban–Acree model was used to correlate the experimental values of density, speed of sound and viscosity.

The purpose of this paper is to analyze the impact of logistics performance on the carbon (CO2) emissions of Balkan countries.

Fixed-effects panel regression analysis is used to estimate the causal relationship between CO2 emissions and logistic performances of Balkan countries. Logistics performance is measured by logistics performance index (LPI) which was published by the World Bank in 2007, 2010, 2012, 2014 and 2016 and used for ranking countries by means of their logistics performance. LPI is based on six main indicators: customs procedures, logistics costs and the quality of the infrastructure for overland and maritime transport. As a measure of carbon emissions of sampled countries, the natural logarithm of carbon dioxide emission per capita is used in this study.

The results obtained reveal that there is a positive and significant relationship between logistics performance and CO2 performances of the sampled Balkan countries.

This study is based on only 11 Balkan countries. In this sense, the data used in the analysis is limited.

Considering the important geostrategic position of the Balkan region, logistics sector has an important role for the development of the countries in that region. In this sense, the findings of this study may provide useful insights for policymakers to achieve sustainable economic development. Furthermore, as far as the authors know, this is the first study that focuses on the relationship between logistics performance and carbon emissions of Balkan countries.

Although politicians' use of performance information affects political decisions and, through them, the well-being of society, there is a lack of studies exploring what contextual factors are associated with annual active performance information use among politicians. Furthermore, past studies on this subject have been cross-sectional rather than longitudinal.

In this qualitative case study, triangulation of observations and 10 semi-structured interviews were used to ensure the robustness of findings. The study was conducted in a Finnish municipality known as Kangasala.

A dialogue culture, constructive political climate, trusted information sources and high-quality information attained via accessible information channels explained the high information use in primarily unfavorable conditions to such use. The authors’ findings contradict many prior interview and survey studies that did not recognize the simultaneous contributions of the information provider, channel and quality, along with organizational and environmental factors to high performance information use. The results contradict to some extent the findings from other countries as these studies have explained high levels of use with unique combinations of drivers, whereas we identify common attributes of these combinations and talk about their meaning in the success of Kangasala's public financial management. However, the findings of this case study cannot be generalized.

This study describes a case organization that created a supportive environment for politicians' frequent performance information use that contributed to improvements. Past studies provide little knowledge about establishing sustained high levels of information use among politicians, so the case offers ideas and inspiration for improving this use.