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High-tech start-up creation is associated with complex challenges originating from quick transformations in technologies and markets. To raise start-up survival and success chances, founders need to ensure a rapid conversion of a venture idea into a working business. This paper aims to explore how identity-related characteristics of founders influence the speed of the start-up creation process.

For this study, a longitudinal multiple-case-study design was selected to identify a vivid flow of decisions and actions taken by high-tech start-ups for analysis in depth. Over 20 months, a series of interviews were organized with founders of six start-ups located in the same business incubator in Russia. Also, a set of additional data sources was engaged, including publicly available data and internal documents provided by businesses.

The findings reveal contrasting dynamics of start-up creation processes among founders with differing role identities. Identity fit and identity misfit are suggested to be serious pull and push factors in the process of organizational becoming through the impact they have on the situational regulatory focus of founders.

The current research contributes to the entrepreneurship stream of research by extending the knowledge of how cognition affects the process of new venture creation.

Different leadership styles are used to make innovations in organizations. So, a sound system of social exchanges has always been a need in this dynamic and technological world to challenge organizational problems. Drawing on the social exchange theory, this study aims to empirically investigate the mediating relationship of a set of social exchanges, e.g. leader-member exchange (L.M.X.), knowledge sharing behavior (K.S.B.) and voice behavior (V.B.), between transformational leadership (T.L) and innovative work behavior (I.W.B). Particularly, it explores the best social exchange behavior between T.L and I.W.B that plays a highly constructive role in the innovativeness of the hospitality industry in Pakistan.

The study targeted 403 frontline employees from hotels situated near Swat Valley, Pakistan. The study used a quantitative approach by using a convenient sampling technique. Structural equation modeling was run by using Smart partial least square 3.3.3 to test the proposed model.

The research supported that T.L significantly influenced I.W.B via a L.M.X., K.S.B. and V.B. T.L did not directly and significantly influence I.W.B so, there were full mediations between T.L and I.W.B. Specifically, knowledge-sharing behavior played a highly constructive role in innovativeness.

The study targeted frontline employees from one place, Swat valley; however, data collection from different tourist places may generalize the results based on social exchanges and innovative behavior. A dyadic interaction between top-level and middle-level management may closely trace the innovative ideas among the employees.

The study found knowledge sharing to be a highly effective mechanism that supports employee innovation more than a L.M.X. and V.B. As a result, the managers should establish a sound system of knowledge sharing, which means a knowledge economy so that employees innovativeness can be boosted and promoted.

The present study was the first study in the hotel industry of Pakistan that reveals a highly effective mediating mechanism: K.S.B., which exists with T.L to increase workers’ innovativeness highly.

The purpose of this study is to identify the critical readiness factors (CRFs) that mainly affect the implementation of Lean Six Sigma (LSS) in an organization and their interactions, and to develop a model that allows the management to assess the Lean Implementation Readiness Level.

The methodology is separated into two main parts: the literature review and the assessment model development. In the literature review, the main CRFs and their interactions for LSS implementation in Scopus Data Base were identified. The second part of the methodology is the model development. It was built on a stepwise framework that considers the relations among the CRFs and their importance. Moreover, it was used fuzzy-based linguistic variables given by the experts working in the company to consider the actual performance rating of each CRF. The model has been validated in the healthcare sector in nine hospitals.

From the model application, it is possible to note that the most frequent level among the nine hospitals interviewed is “Average Ready”. Also, the most extreme level of readiness occurred ones while the most extreme level of not readiness never occurred. Results show that in 78% of the cases, there would have been a high probability of implementation failure. Also, it was possible to identify for each hospital if the CRFs are good, if they are weak and need to change or if another factor needs to be improved before it and what this factor is.

This work proposes a new methodology that eliminates the negative aspects and limitations of the total interpretive structural modeling methodology and the fuzzy logic approach currently applied to evaluate the LSS readiness of a company. The present methodology lies in the fact that it provides a solution not only by defining the weak CRFs but also by giving an indication of priority as it identifies the weak antecedent factors that inhibit the preparedness of the depending factors.

Currently, digital technology has been proposed as a new archetype for developing an effective traceability system in the perishable food supply chain (FSC). Implementation of such a system needs significant investment and the burden lies with the members of the supply chain. The purpose of this paper is to examine the impact on the profit of the supply chain members due to the implementation of an effective traceability system with such a large investment. The study also tries to explore the impact of the implementation of such a system by coordination among the members through a cost-sharing mechanism.

A two-level supply chain that comprises a supplier and retailer is analyzed using a game-theoretic approach. The mathematical models are developed considering the scenario for an individual, centralized and both members invest using a cost-sharing mechanism. For each of the models, the impact of product selling price, information sensing price and quality improvement level on profit is analyzed through numerical analysis.

The study reveals that consumer involvement can be a strong motivation for the supply chain members to initiate investment in the traceability system. Further, from an investment perspective cost-sharing model is beneficial compared to the individual investment-bearing model. This mechanism can coordinate as well as benefit the FSC members. However, the model is less beneficial to the centralized model from profit and quality improvement levels.

Food wastage can be less from supplier and retailer perspectives. Moreover, consumers can purchase food items only after verifying their shipping conditions. Consequently the food safety scandals can be reduced remarkably.

Digital technology adoption in the perishable FSC is still considered emerging. The present study helps organizations to implement a traceability system in the perishable FSC through consumer involvement and a cost-sharing mechanism.

This study aims to analyze lean wastes for the poultry sector in Turkey and link lean tools to this study, focusing on identifying each lean waste that affects poultry production and proposing solutions for preventing these lean wastes in the sector. The proposed solutions aim to improve processes by suggesting different lean tools and their applications for the poultry sector.

The study consists of two different applications. First, the waste relationship matrix (WRM) was created to reveal the relationship between seven lean wastes and their importance order. Then, after determining lean tools for eliminating lean wastes, the optimum weight ranking and consistency ratio of the most suitable lean tools were calculated for these wastes and ranked with the best-worst method (BWM).

Results showed that overproduction is the most critical waste that impacts other wastes, followed by defect waste. Due to the nature of the sector, these wastes not only result in economic loss for the company but also in food waste and loss and issues related to animal welfare. Furthermore, the Kaizen approach and 5S implementation are the methods to eliminate these wastes. Detailed discussion on the link between lean tools and lean wastes is provided for the poultry sector.

This is the first study that theoretically and empirically identifies the potential lean waste affecting the poultry sector and provides lean tools for eliminating these wastes. Sector-specific explanations and discussions are presented in the study to show the applicability of lean approaches in the poultry sector to eliminate waste. In addition, this study is the first to integrate the WRM and BWM.

Recycling construction waste is a promising way towards sustainable development in construction. Recycled aggregate (RA) is obtained from demolished concrete structures, laboratory crushed concrete, concrete waste at a ready mix concrete plant and the concrete made from RA is known as RA concrete. The purpose of this study is to apply multiple linear regressions (MLRs) and artificial neural network (ANN) to predict the mechanical properties, such as compressive strength (CS), flexural strength (FS) and split tensile strength (STS) of concrete at the age of 28 days curing made completely from the recycled coarse aggregate (RCA).

MLR and ANN are used to develop a prediction model. The model was developed in the training phase by using data from a previously published research study and a developed model was further tested by obtaining data from laboratory experiments.

ANN shows more accuracy than MLR with an R²-value of more than 0.8 in the training phase and 0.9 in a testing phase. The high R²-value indicates strong relation between the actual and predicted values of mechanical properties of RCA concrete. These models will help construction professionals to save their time and cost in predicting the mechanical properties of RCA concrete at 28 days of curing.

ANN with rectified linear unit transfer function and backpropagation algorithm for training is used to develop a prediction model. The outcome of this study is the prediction model for CS, FS and STS of concrete at 28 days of curing.

Modern technologies are seen as an essential component of the fourth industrial revolution (industry 4.0) and their adoption is vital to transform the existing manufacturing system into industry 4.0-based manufacturing system. Therefore, the primary objective of this research explores the barriers of modern technology adoption and their mitigating solutions in order to align with Industry 4.0 objectives.

Barriers to adopting modern technologies and respective mitigating solutions are identified from the available literature. Further, these barriers are ranked with the help of expert opinions by using the BWM method appropriately. The identified solutions are ranked using the combined compromise solution (CoCoSo) method.

Several modern technologies and their capabilities are recognised to support the industry 4.0-based manufacturing systems. This study identifies 22 barriers to the effective adoption of modern technologies in manufacturing and 14 solutions to overcome these barriers. Change management, the high initial cost of technology and appropriate support infrastructure are the most significant barriers. The most prominent solutions to overcome the most considerable barriers are ‘supportive research, development and commercialisation environment’, ‘updated policy and effective implementation’ and ‘capacity building through training’ that are the top three solutions that need to be addressed.

The barriers and solutions of modern technology adoption are obtained through a comprehensive literature review, so there is a chance to ignore some significant barriers and their solutions. Furthermore, ranking barriers and solutions is done with expert opinion, which is not free from biases.

This identification and prioritisation of barriers will help managers to understand the barriers so they can better prepare themselves. Furthermore, the suggested solutions to overcome these barriers are helpful for the managers and could be strategically adopted through optimal resource utilisation.

This study proposes a framework to identify and analyse the significant barriers and solutions to adopting modern technologies in the manufacturing system. It might be helpful for manufacturing organisations that are willing to transform their manufacturing system into industry 4.0.

The purpose of this paper is to make a contribution to understanding the influence of factors such as the water/cement (W/C) ratio and the granular class on the mechanical and physical properties of high-strength concretes (HSCs). In the formulations of HSC, aggregates by their high mass and volume proportion play an important role. When selecting aggregates, it is necessary to know their intrinsic properties. These properties influence the performance of concrete, in particular the quality of the granulate cimentary adhesion.

This experimental study focused on the effect of W/C ratio (0.25, 0.30, 0.35), the effect of replacing a part of cement by silica fume (SF) (8%), the effect of fraction of aggregate on properties of fresh and hardened concrete, the effect of different environment conversation like drinking water and sea water on compressive strength and the study of absorption of water and softening using the mix design method of the University of Sherbrooke combined with the Dreux-Gorisse method which gives good results.

At the end of our work, the examination of the results obtained made it possible to establish the correlations between the formulations studied and the physicomechanical characteristics of the concrete compositions (HSC25, HSC16, HSC8). The results of this study show that the use of three granular classifications (DMAX8, DMAX16 and DMAX25) and three report W/C (0.25, 0.30 and 0.35) in two different conservation environment (drinking water and sea water) give HSCs, HSC25 with an W/C = 0.25 ratio has reached the largest mechanical strength of 90 MPa for different environments of conservation. For selecting aggregates, it is necessary to know their intrinsic properties, these properties influence the strength of concrete. In general, there is a slight decrease in the compressive resistance of the specimens stored in seawater, it can be said that the conservation life has not had effect on the resistance (28 days). The effect of aggressive environment can appear in the long term.

Mixed design and concrete fabrication with a 28-day compressive strength of up to 68 MPa or more of 90 MPa can now be possible used in Jiel (Algeria), and it should no longer be considered to be used only in an experimental domain. Addition of SF in concrete showed good development of strength between 7 and 28 days, depending on the design of the mix.

Concrete containing 8% SF with W/B of 0.25 has higher compressive strength than the other concretes, and concretes with SF are more resistant than concretes without SF, so it is possible to have concrete with a compressive strength of 82 MPa for W/C 0.25 without SF. Like as a result, we can avoid the use of SF to affect the strength of concrete at compressive strength of 68 MPa, and a slump of 21 cm, because the SF is the most expensive ingredient used in the composition of concrete and is therefore very important economically. One of the main factors of production of HSC above 90 MPa is use of aggregate DMAX25, which is stronger with W/B of 0.25 and 0.30.

This mixtures leads to a very dense microstructure and low porosity and produces increased permeability of HSC and is able to resist the penetration of aggressive agents. This combination has a positive effect on the economy of concrete.

The combination of the Dreux-Gorisse method with the Sherbrook method is very beneficial for determining the percentage of aggregates used, and the use of coarse aggregates of Jijel to obtain HSC with 90 MPa and 16 cm of workability.

The aim of this article states that in each stage of the industrial revolution, only a few initiatives have been real game changers. In Industry 3.0, “Internet of Information” has transformed the business landscape via connectivity and communications. Enterprises could come together to spur innovation in a cooperative or competitive manner. In Industry 4.0, the “Internet of Value” has shown considerable benefits; and, blockchain technology is expected to touch all layers of a business ecosystem, and the construction industry is not an exception.

This study aims to answer the “How do enterprise blockchain solutions contribute to the vibrancy of the construction ecosystem from social, economic, and environmental aspects?” Following a comprehensive literature review, the Grey Ordinal Priority Approach (OPA-G) is employed in multiple criteria decision analysis (MCDA). OPA-G can select functionally rich enterprise blockchain solutions that meet the needs of the future construction industry, while there is uncertainty in the input data.

The results from the case study show that organization under observation welcomes an enterprise blockchain solution that delivers services related to “renewable energy certificates” in the context of “smart cities and built environment”. Employing high-ranked blockchain solutions brings vibracy and sustainability to construction ecosystem in terms of “C₆. decentralized finance and investment,” “C₃. multi-party and cross-industry collaboration,” and “C₈. data-driven value creation”.

At the micro level, blockchain solutions automate processes, streamline operations, and build new capacities on a new business model. At the macro level, blockchain creates a vibrant ecosystem based on transparency, decentralization, consensus-based democracy, interoperability, etc. Indeed, the capability of blockchain solutions at an enterprise scale (enterprise blockchain solutions) can shape a new construction ecosystem. The practical implications of current research are preparing executives for a fundamentally different next normal in construction.

This paper attempts to develop a simple, static model of tax administration that is capable of explaining the widespread collusive petty tax administration corruption observed in developing countries.

This paper utilizes a positivist research framework and adopts a theoretical method of analysis, although secondary data will also be mentioned to support theoretical arguments whenever it is appropriate to do so.

A high rate of collusive tax corruption is inevitable in developing countries.

The model is static and needs to be extended into a dynamic model.

Traditional enforcement tools such as higher audits or a higher penalty regime against tax evasion do not work. Tax simplification can lessen the incidence of tax corruption.

Fighting tax corruption requires significant changes in the attitudes of taxpayers and tax auditors.

This paper combines the literature on Kantian economics and tax compliance in an innovative fashion.