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Theories are crucial for addressing research questions and advancing the boundaries of knowledge. However, in the field of strategic management, the existence of diverse schools of thought from various disciplines, including economics, politics, and sociology, poses significant challenges for researchers seeking to develop theories for argumentation and theorization. In this study, we have conceptualized a novel approach to selecting an appropriate theory for addressing specific research questions.

Thought experiment, disciplined imagination, and a conceptual examination of a diverse set of theories.

Because the central focus in the field of strategic management revolves around how firms achieve sustainable high performance, a research question should initially clarify the fundamental phenomenological issues it aims to address. Subsequently, the process of problematization should identify the ontological assumptions and premises that establish a connection between the research question and existing theories. Finally, the identification and abstraction of rhetorical concepts derived from these assumptions and premises lead to theory selection criteria, namely connectedness, reliability, parsimoniousness, and falsifiability.

Although we believe that our model for theory selection is generalizable to a wide range of management disciplines, we have primarily focused on its application in the field of strategic management. Future work could validate and further explore the applicability and effectiveness of this model in selecting appropriate theories for conceptual development in other domains.

While many researchers have proposed methods for writing theoretical papers, few have provided suggestions specifically focused on theory selection. This paper stands out as one of the few that not only attempts to address this gap but successfully develops a comprehensive model for theory selection.

A physically-based elasto-viscoplastic constitutive model is proposed to examine the size effects of the precipitate and blocks on the creep for martensitic heat-resistant steels with both the dislocation creep and diffusional creep mechanisms considered.

The model relies upon the initial dislocation density and the sizes of M₂₃C₆ carbide and MX carbonitride, through the use of internal variable based governing equations to address the dislocation density evolution and precipitate coarsening processes. Most parameters of the model can be obtained from existing literature, while a small subset requires calibration. Based on the least-squares fitting method, the calibration is successfully done by comparing the modeling and experimental results of the steady state creep rate at 600° C across a wide range of applied stresses.

The model predictions of the creep responses at various stresses and temperatures, the carbide coarsening and the dislocation density evolution are consistent with the experimental data in literature. The modeling results indicate that considerable effect of the sizes of precipitates occurs only during the creep at relatively high stress levels where dislocation creep dominates, while the martensite block size effect happens during creep at relatively low stress levels where diffusion creep dominates. The size effect of M₂₃C₆ carbide on the steady creep rate is more significant than that of MX precipitate.

The present study also reveals that the two creep mechanisms compete such that at a given temperature the contribution of the diffusion creep mechanism decreases with increasing stress, while the contribution of the dislocation creep mechanism increases.

Disaster risk reduction is of prime importance in informal settlements in the Global South, where several forms of vulnerability coexist. Policy and official programmes, however, rarely respond to the needs and expectations of citizens and local leaders living in these settlements. Even though these agents constantly attempt to reduce risks in their own way, we know very little about their activities, motivations and effective impact on risk reduction. Here we seek to conceptualize bottom-up initiatives to better grasp their origins, limitations and success.

Through a four-year action-research project in Colombia, Cuba and Chile, we theorize about the production of change by local agents. Through detailed case studies we explored the activism of 17 local leaders. Through narrative analysis we studied their motivations and explanations. Finally, by documenting 22 initiatives, we revealed effective changes in space.

In the face of risk and disasters, residents and leaders in informal settings engaged in symbolic, physical and social spaces of interaction. Their actions were guided by trust, emotions, time cycles and activism. Local agency was justified by narratives about risk and climate change that differ from those of authorities and scholars.

There is still limited understanding of bottom-up initiatives in informal settings. It is crucial to conceptualize their origins, limitations and success. The focus on three specific countries necessitates further research for broader applicability and understanding.

A better comprehension of bottom-up actions is crucial for informing policies and programmes aimed at reducing risk in informal settings. Stakeholders must recognize the political, social and cultural roles of these actions for more impactful climate action.

We borrow Simon’s concept of “artefact” to introduce the notion of “Artefacts of Disaster Risk Reduction”, providing insights into the multifaceted nature of bottom-up initiatives. We also emphasize the simultaneous political and phenomenological character of these actions, contributing to a deeper understanding of their origins and impact.

This study aims to further refine the model, explore the influence of cutting parameters on the machining process, and apply it to practical engineering to improve the efficiency and quality of titanium alloy machining.

This paper establishes a comprehensive thermo-mechanical fully coupled orthogonal cutting model. This paper aims to couple the modified Johnson–Cook constitutive model, damage model and contact model to construct a two-dimensional orthogonal cutting thermo-mechanical coupling model for high-speed cutting of Ti6Al4V. The model considers the evolution of microstructures such as plastic deformation, grain dislocation rearrangement, dynamic recrystallization, as well as stress softening and hardening occurring continuously in Ti6Al4V metal during high-speed cutting. Additionally, the model incorporates friction and contact between the tool and the workpiece. It can be used to predict parameters such as cutting process, cutting force, temperature distribution, stress and strain in titanium alloy machining. The study establishes the model and implements corresponding functions by writing Abaqus VUMAT and VFRICTION subroutines.

The use of different material constitutive models can significantly impact the prediction of the cutting process. Some models may more accurately describe the mechanical behavior of the material, thus providing more reliable prediction results, while other models may exhibit larger deviations. Compared to the Tanh model, the proposed model achieves a maximum improvement of 8.9% in the prediction of cutting force and a maximum improvement of 20.9% in the prediction of chip morphology parameters. Compared to experiments, the proposed model achieves a minimum prediction error of 2.8% for average cutting force and a minimum error of 0.57% for sawtooth parameters. This study provides a comprehensive theoretical foundation and practical guidance for orthogonal cutting of titanium alloys. The model not only helps engineers and researchers better understand various phenomena in the cutting process but also serves as an important reference for optimizing cutting processes.

The originality of this research is guaranteed, as it has not been previously published in any journal or publication.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0168/

The constitutive models determine the mechanical response to the defined loading based on model parameters. In this paper, the inverse problem is researched, i.e. the identification of the model parameters based on the loading and responses of the material. The conventional methods for determining the parameters of constitutive models often demand significant computational time or extensive model knowledge for manual calibration. The aim of this paper is to introduce an alternative method, based on artificial neural networks, for determining the parameters of a viscoplastic model.

An artificial neural network was proposed to determine nine material parameters of a viscoplastic model using data from three half-life hysteresis loops. The proposed network was used to determine the material parameters from uniaxial low-cycle fatigue experimental data of an aluminium alloy obtained at elevated temperatures and three different mechanical strain rates.

A reasonable correlation between experimental and numerical data was achieved using the determined material parameters.

This paper fulfils a need to research alternative methods of identifying material parameters.

This study aims to establish a thermally coupled two-dimensional orthogonal cutting model to further improve the modeling process for systematic evaluation of material damage, stiffness degradation, equivalent plastic strain and other material properties, along with cutting temperature distribution and cutting forces. This enhances modeling efficiency and accuracy.

A two-dimensional orthogonal cutting thermo-mechanical coupled finite element model is established in this study. The tanh material constitutive model is used to simulate the mechanical properties of the material. Velocity-dependent friction model between the workpiece and the tool is considered. Material characteristics such as material damage, stiffness degradation, equivalent plastic strain and temperature field during cutting are evaluated through computation. Contact pressure and shear stress on the tool surface are extracted for friction analysis.

Speed-dependent friction models predict cutting force errors as low as 8.6%. The prediction errors of various friction models increase with increasing cutting forces and depths of cut, and simulation results tend to be higher than experimental data.

The current research results provide insights into understanding and controlling tool-chip friction in metal cutting, offering practical recommendations for friction modeling and machining simulation work.

The originality of this research is guaranteed, as it has not been previously published in any journal or publication.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0162/

The purpose of this paper is to study the mechanochemical effect and self-growth mechanism of double-network (DN) gel and to provide a quasiperiodic model for rubber elasticity.

The chemical reaction kinetics is used to identify the mechanochemical transition probability of host brittle network and to explore the mechanical behavior of endosymbiont ductile network. A quasiperiodic model is proposed to characterize the cooperative coupling of host–endosymbiont networks using the Penrose tiling of a 2 × 2 matrix. Moreover, a free-energy model is formulated to explore the constitutive stress–strain relationship for the DN gel based on the rubber elasticity theory and Gent model.

In this study, a quasiperiodic graph model has been developed to describe the cooperative interaction between brittle and ductile networks, which undergo the mechanochemical coupling and mechanical stretching behaviors, respectively. The quasiperiodic Penrose tiling determines the mechanochemistry and self-growth effect of DNs.

It is expected to formulate a quasiperiodic graph model of host–guest interaction between two networks to explore the working principle of mechanical and self-growing behavior in DN hydrogels, undergoing complex mechanochemical effect. The effectiveness of the proposed model is verified using both finite element analysis and experimental results of DN gels reported in literature.

This study provides a critical review of the literature on information literacy in the workplace and its relevant issues. The purpose of this study is to examine two elements: how the predominantly academic information literacy is experienced in the workplace; and review how academically based information literacy frameworks can be used to increase performance in the workplace.

A critical approach to information literacy was implemented for the introduction and background information by searching for scholarly publications referring to information literacy and information literacy in the workplace. Overall, 65 published articles in English were selected and found suitable.

According to the existing literature, very few frameworks tailored to specific workplaces have been discovered and were all found to be pertinent to academic settings that included researchers. The review revealed the perceived positive role of information literacy in raising work performance. The paper concludes that the benefit for information literate employees and employers is apparent, but those differences in academic information literacy and workplace information literacy are significant.

Information literacy frameworks for the workplace, when used, are still heavily reliant on the educational sector and need to be further researched in order for them to adequately address specific workplace contexts and their socially collaborative information literacy activities.

This study aims to investigate the erosion wear rate of a stainless steel automobile exhaust manifold, both computationally and physically.

The experiment was performed on a motorcycle exhaust manifold as well as on a 3D model, created using SolidWorks 2022 CAD software. The analysis was later achieved using ANSYS 19.2 simulation software using Fluent – code.

The analysis of solid particle erosion in the exhaust manifold revealed that erosion wear is concentrated predominantly at the extrados of the manifold, with the most significant wear occurring at the lowermost bend. The erosion wear rate increases with larger particulate sizes and varies among bends, with negligible wear observed in straight pipes. The SEM analysis further confirmed surface degradation, with rugged textures, pits and grooves indicating abrasive wear. Spine-like structures and fractured soot particles suggest erosive and abrasive forces caused by high-speed contact of exhaust gas compounds. Energy dispersive X-ray spectroscopy revealed significant carbon abundance, indicating carbonaceous compounds from fuel combustion, along with notable amounts of oxygen and iron, typical of oxidized metallic constituents. The discrete phase modeling (DPM) analysis highlighted peak particulate matter deposition at the first bend exit, with maximum concentrations observed at specific angles. This deposition is influenced by centrifugal force, leading to increased PM concentration at outer bend walls. Velocity magnitude contours showed asymmetrical flow profiles, with high turbulence levels and secondary flow induced by centrifugal effects in bend areas. Dynamic pressure contours revealed varying pressures at intrados and extrados, with maximum pressure observed at the intrados of the manifold’s bends. These findings provide valuable insights into erosion wear, particulate dispersion and flow dynamics within the exhaust manifold.

The study investigated an automobile exhaust manifold model using ANSYS Fluent code and DPM to analyze erosion wear rate phenomena and its various constituents. This analysis was conducted in comparison with a physically eroded sample. The study offers insights into the mechanism underlying the exhaust manifold of an automobile.

With the emergence of the digital era, the role of digital leaders in developing digital capabilities and driving their firms towards digital transformation has gained significant attention. Digital dynamic capabilities involve continuous engagement of leaders in sensing, seizing, and transforming activities needed to digitally transform their firms. However, little attention is given toward the role of digital leadership in developing digital dynamic capabilities. We seek to develop an understanding of the role of digital leadership in building digital dynamic capabilities for successful digital transformation.

We conducted a systematic literature review and looked at relevant articles using Google Scholar, ScienceDirect, and Scopus databases with key search items being “digital leadership”, “dynamic capabilities”, “digital dynamic capabilities,”. We used AND, OR operators in between the key terms to search for the relevant articles.

Our conceptual framework and propositions demonstrate the digital leader's role in building three core dynamic capabilities: digital sensing (technological trends, digital scouting, digital vision, future interpretation, and digital strategies), digital seizing (organizational agility and digital portfolio), and transforming (redesigning internal structures and ecosystem partnerships) for successful digital transformation.

This study pioneers an integrated framework that elucidates the role of digital leadership in fostering digital dynamic capabilities essential for successful digital transformation. While previous research has examined digital leadership and transformation in separate silos, our work bridges this gap by defining and dissecting three core capabilities—digital sensing, digital seizing, and transforming. By doing so, we offer both academic and practical communities a nuanced understanding of how digital leadership shapes dynamic capabilities. The study serves as a foundational roadmap for future research and offers actionable insights for organizations striving to navigate the complex landscape of digital transformation.