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E-commerce live streaming is a new influencer advertising method that allows influencers to interact directly with consumers on e-commerce platforms. Although evidence suggests that influencer live-streaming advertisements (ads) on social media can increase consumers’ buying impulses, little research examined how this similar but new advertising method on e-commerce platforms may influence consumers’ urge to buy impulsively. This study explores the role of influencer credibility, celebrity effect, perceived entertainment, trust and perceived usefulness on consumers’ attitudes toward influencer ads and their urge to buy impulsively.

A questionnaire containing seven constructs was developed and distributed to participants using a convenient sample and snowball sampling approach. The constructs were measured based on validated measurement items from the literature and adjusted according to this study’s focus. A total of 236 valid responses were obtained from the survey and used for data analysis. A partial least squares structural equation modeling approach was employed for parameter estimation and model testing.

The empirical results show that all constructs influenced consumers’ urge to buy impulsively via attitude toward influencer ads. The proposed research model explains 61.7% of the variance in attitude toward influencer ads and 19.4% of the urge to buy impulsively.

This is an early study investigating the relationship between influencer advertising and impulse buying. The results provide valuable insights into improving the design of influencer ads and marketing strategies.

1. I-eIB model tests the mechanism of influencer ads on consumers’ buying impulse.

2. Consumers’ attitude towards influencer ads affects their urge to buy impulsively.

3. Influencer credibility affects consumer attitude via celebrity effect as a mediator.

4. Trust affects consumer attitude via perceived usefulness as a mediator.

5. Entertaining ads help develop favorable consumer attitude.

I-eIB model tests the mechanism of influencer ads on consumers’ buying impulse.

Consumers’ attitude towards influencer ads affects their urge to buy impulsively.

Influencer credibility affects consumer attitude via celebrity effect as a mediator.

Trust affects consumer attitude via perceived usefulness as a mediator.

Entertaining ads help develop favorable consumer attitude.

In the context of collaborative research and development (R&D), multi-actor participation and multi-resource integration of technological knowledge has become the mainstream paradigm for the R&D and spillover of industry generic technology (GT). As GT's core characteristics, “fundamentality” and “externality,” make differential requests on knowledge bases regarding the R&D and spillover of GT (SGT). Knowledge breadth can enhance the generality of technology. The purpose of this paper is to integrate “generic technology R&D” and “generic technology spillover” into a single study, and try to solve the theoretical problem of “whether broader mean more general?”

This paper collects and collates the patent data from the two patent databases of Derwent and SooPAT, and then makes an empirical analysis of the patent data collected by the authors with the data analysis software Stata.

Taking 352 strategic emerging firms in China as the sample, this paper examined the effects of general knowledge breadth (GKB) and specific knowledge breadth (SKB) on the R&D and SGT. The authors concluded that both general and SKB have a positive effect on the R&D of GT (RGT), and the latter has a greater effect. There is a significant inverted U-shaped relationship between SKB and SGT.

The theoretical contributions of this paper are as follows. GT can effectively link different technologies and knowledge fields (Gambardella and Giarratana, 2013; Appio et al., 2017a, b). Therefore, existing studies regard the role of knowledge breadth on the R&D and SGT as an existing hypothesis. This paper challenges such hypothesis in two ways. First, this paper divides knowledge breadth into “general knowledge breadth” and “specific knowledge breadth” in response to the insufficient division of knowledge breadth in previous research, although some existing studies have examined the antecedents of the R&D and SGT from the perspective of R&D and SGT. Thus, the authors define GKB as the scope of context-free knowledge and SKB as the scope of context-specific knowledge, both of which shows differential nature, source and application. Second, this paper decomposes the effect of knowledge breadth on RGT, as well as on SGT, basing on distinguishing the SKB from GKB. Existing research reaches a consensus of the positive role of knowledge breadth, no matter on RGT or SGT (e.g. Schmidt et al., 2016; Appio et al., 2017a, b). Yet, such hypothesis ignores the refinement and decomposition of “knowledge breadth” in the research field of R&D and SGT, which is essential in promoting the development of GT theory. In this paper, the authors find that these two types of knowledge breadths play different roles in the RGT, and especially SKB plays a double-edged sword effect on the SGT.

The fused filament fabrication (FFF) process is an additive manufacturing technique used in engineering design. The mechanical properties of parts manufactured by FFF are influenced by the printing parameters. The mechanical properties of rigid thermoplastics for FFF are well defined, while thermoplastic elastomers (TPE) are uncommonly investigated. The purpose of this paper is to investigate the influence of extruder temperature, bed temperature and printing speed on the mechanical properties of a thermoplastic elastomer.

Regression models predicting mechanical properties as a function of extruder temperature, bed temperature and printing speed were developed. Tensile specimens were tested according to ASTM D638. A 3×3 full factorial analysis, consisting of 81 experiments and 27 printing conditions was performed, and models were developed in Minitab. Tensile tests verifying the models were conducted at two selected printing conditions to assess predictive capability.

Each mechanical property was significantly affected by at least two of the investigated FFF parameters, where printing speed and extruder temperature terms influenced all mechanical properties (p < 0.05). Notably, tensile modulus could be increased by 21%, from 200 to 244 MPa. Verification prints exhibited properties within 10% of the predictions. Not all properties could be maximized together, emphasizing the importance of understanding FFF parameter effects on mechanical properties when making design decisions.

This work developed a model to assess FFF parameter influence on mechanical properties of a previously unstudied thermoplastic elastomer and made property predictions within 10% accuracy.

The purpose of this paper is to present a method to accurately forecast the tendency of the gross amount of energy sources consumption of the country and construct a new kind of algorithm for forecasting that synthesizes the advantages of the grey model, Markov chains, and least square method.

With the application of this new algorithm, this paper have forecasted the trend of the gross amount of energy sources consumption of the country and come to the conclusions that the new algorithm is more precise than the grey model. It is proved that the improved grey‐Markov chain algorithm is effective and can be used by authorities to make decision.

It was found that combining the grey model, Markov chains, and least square method, can be a new algorithm for forecasting the trendency of the gross amount of energy sources consumption.

The new algorithm is only suitable for the short‐term forecast.

The grey forecasting method reflects the overall tendency of primitive data sequence of the gross amount of energy source, and the Markov chain forecasting method reflects the effect of the random fluctuation. The least square method reflects the tendency of increase. The new algorithm is more precise than the grey model.

The purpose of this paper is to extract the characterized mineralization information from large numbers of data obtained from geologic exploration based on rough set; analyze the inherent relation between mineral information genes and metallogenic probability, and offer the scientific basis for target prediction.

Mineral information includes all kinds of relative metallogenic information. In order to extract comprehensive metallogenic prediction information, it is necessary to filter initial observation information to emphasize the factors that are most advantageous to metallogenic prognosis. Rough set can delete irrespective or unimportant attributes on the premises of no information missing and no classification ability changing, without supplementary information or prior knowledge, which has important theoretic and practical value for metallogenic prognosis.

The association and importance of geological information referring to prospecting are found out through attribute reduction based on rough set.

The analysis of geological and mineral information based on rough set is a novel approach for high‐dimensional complex non‐deterministic polynomial problems which are predominant in geological research. The research successfully extracts characterized mineralization information to offer the scientific basis for target prediction.

The purpose of this paper is to forecast the reliable storage life of a certain kind of equipment under the normal stress level.

Through the stepping stress acceleration life test and the failure mechanism analysis, this paper aims to confirm the stress level for the stepping stress acceleration life test of a certain kind of equipment and establish the data processing mathematical model and storage life forecasting method.

The stress level for the stepping stress acceleration life test of a certain kind of equipment is confirmed and the data processing mathematical model and storage life forecasting method is established.

Availability of data is the main limitation affecting which model will be applied.

Useful advice for products' storage life forecasting.

The paper presents a new approach to product storage life estimation.

This paper reports an empirical examination of the societal, financial and technology-related challenges amid the ongoing pandemic (COVID-19) in the service supply chain network.

A combination of qualitative approach (for items generation pertinent to the constructs involved) and quantitative approach (self-administered questionnaires from the top and middle management of the sampled companies) was used for data collection. In total, 272 complete responses were received and analyzed through structural equation modeling.

The results provided empirical evidence that social and physical distancing, travel restrictions, work from home and lockdown practices have two conflicting effects: On one hand, these practices have contributed to the reduction of economic activities, including the low economic outlook, low productivity, high unemployment, poverty, fall in customer demands, dissatisfaction and mental health, that ultimately impacts rise financial and societal issues. On the other hand, the results revealed an insignificant influence of COVID-19 on creating technology-related challenges in the service sector. It shows that the organizations are doing well in combating the technology-related challenges amidst the current pandemic.

Findings of the inquiry recommend implications for the services industry to harmonize a comprehensive strategy and revisit the global norms in sustainable supply chain management activities that have been the backdrop in their operations for a long time.

Findings of the inquiry recommend implications for the services industry to harmonize a comprehensive strategy and revisit the global norms in supply chain management activities that have been the backdrop in their operations for a long time.

Prior studies in the context of the COVID-19 outbreak and its implications have given more attention to the exploratory and theoretical discussion than to empirical evidence. This paper contributes to filling this knowledge gap by empirically exploring the societal, financial and technology-related challenges created by COVID-19. The analysis in this paper covers three dimensions of the PEST model, namely economic, societal and technological factors. This study also helps in laying out a platform for investigating the PEST (political, economic, social and technological) model for guiding the services industry in strategic decision-making in a new era due to COVID-19.

The purpose of this paper is to develop an interval method for vehicle allocation and route planning in case of an evacuation.

First, the evacuation route planning system is described and the notations are defined. An inexact programming model is proposed. The goal of the model is to achieve optimal planning of vehicles allocation with a minimized system time under the condition of inexact information. The constraints of the model include four types: number of vehicles constraint, passengers balance constraints, maximum capacity of links constraints and no negative constraints. The model is solved through the decomposition of the inexact model. A hypothetical case is developed to illustrate the proposed model.

The paper finds that the interval solutions are feasible and stable for evacuation model in the given decision space, and this may reduce the negative effects of uncertainty, thereby improving evacuation managers' estimates under different conditions.

This method entails incorporation of uncertainties existing as interval values into model formulation and solution procedure, and application of the developed model and the related solution algorithm in a hypothetical case study.

The purpose of this paper is to put forward a method which uses a computer to make a large number of simulated tests and determine the reference value of a system reliability index according to a unit reliability index.

At the beginning of conducting an index demonstration and development on a new system, its unit reliability index can already reflect the unit reliability level accurately. Based on this characteristic, this index is used as the confidence lower limit of single side estimated in the unit reliability interval to find the failure distribution law of the unit. On this basis, a great number of simulated tests are made by a computer to calculate the unit failure number and reliability point estimated value every test.

The confidence lower limit of single side estimated in the system reliability interval is found by counting up the simulated test data of these units.

Availability of data is the main limitation regarding which model will be applied.

A useful advice for system reliability analysis and calculation.

This paper presents a new approach of system reliability analysis.

Literature shows that the antecedents of eWoM have received limited attention and the present scales used for measuring eWoM in the hospitality and tourism industry are outdated. Therefore, this study aims to construct a new scale for the generation of positive eWoM in the hospitality and tourism industry.

This study developed a novel scale on eWoM based on the four dimensions of the unified theory of acceptance and use of technology.

The exploratory and confirmatory factor analysis confirms the factorial structure of the new scale. The exploratory factor analysis shows that “performance expectancy” has the highest impact on the constitution of positive eWoM with 8 items, followed by “social influence” and “facilitation condition” with 5 items each. The factor “effort expectancy” is found to have the lowest impact on the constitution of positive eWoM in the hospitality and tourism industry with 3 items. The confirmatory factor analysis in terms of the construct reliability, average variance extracted and maximum shared variance tests confirmed the model validity of the new scale.

This study ensures measuring eWoM with the latest norms that will assist in prudent policy formulation. These results insights into policymakers from the hospitality and tourism industry for the generation of positive eWoM towards their business which will help them achieve better customer loyalty.