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Employing multi-type laborers (MLs) is common in multinational and cross-culture projects (MPCs). Different attributes of MLs can lead to uncertain and dynamic laborer behaviors (i.e. behavioral diversities), which may cause project deviations. Previous studies do not consider the uncertainties or dynamics of behaviors adequately or they only provide general suggestions. The purpose of this paper is to combine system dynamics (SD) and agent-based modeling (ABM) to build an integrated model. The proposed ABM-SD can gain better understanding of MLs’ behavioral diversities, reveal the associated impacts and improve project management.

Based on extensively review in construction labor management and computer simulation, architecture is built to depict the relationships between the affecting factors of MLs’ behaviors, MLs’ behavioral diversities and project performance. Second, conceptual structures of the ABM-SD model are developed. Third, methods to implement the model in practice are introduced, focusing on data collection and model structure adjustment. Finally, the model is tested in a case study.

Different ML groups have distinctive behaviors which constantly change through interactions between MLs, engineers and external environment. Inadequate consideration of the diversities can result in inaccurate estimation of productivity, work quality and absenteeism, causing severe project deviations such as schedule delay, cost overrun and high absenteeism. On the other hand, using the ABM-SD model, the root causes of project deviations are analyzed from the perspective of MLs’ behavioral diversities and the optimization of labor management can significantly improve project performance.

This paper supplements previous studies because the ABM-SD model takes fully use of the strength of simulation of solving uncertain and dynamic problems and combines both qualitative and quantitative findings in existing studies of labor management. Besides, the ABM-SD model is also a practical management tool to better monitor laborer behaviors and forecast the impacts. The limitation is mainly about the small scale of the case study. However, the ABM-SD model already demonstrates the mechanism about how MLs’ different behaviors affect a project, which fulfill the aim of the study.

The ABM-SD model can simulate MLs’ behavioral diversities and produce reliable estimations of project performance. It also allows to optimize management plans. Furthermore, The ABM-SD model is adjustable based on specific project conditions, which make it applicable for different tasks, different laborer compositions and even different projects. Thus, the ABM-SD model can be a practical tool for engineers in MCPs.

SD and ABM are applied to study behaviors with well-known benefits in both separated and integrated manner. However, few studies use the approach to investigate MLs’ behaviors in MCPs. Hence, the proposed ABM-SD model is an original attempt to improve the laborer management level in MCPs.

This study examines how the different dimensions of a privacy policy separately influence perceived effectiveness of privacy policy, as well as the mediating mechanisms behind these effects (i.e. vulnerability, benevolence). In addition, this study considers privacy concern as a significant moderator in the research model, to examine if the relative influences of privacy policy content are contingent upon levels of users' privacy concern.

The survey experiment was conducted to empirically validate the model. Specifically, three survey experiments and six scenarios were designed to manipulate high and low levels of the three privacy policy dimensions (i.e. transparency, control and protection). The authors totally distributed 450 copies of the questionnaire, of which 407 were valid.

This paper found that (1) all the three privacy policy dimensions directly influence perceived effectiveness of privacy policy; (2) all the three privacy policy dimensions indirectly influence perceived effectiveness of privacy policy by enhancing perceived corporate benevolence, whereas control also affects perceived effectiveness of privacy policy by reducing perceived vulnerability; and (3) individuals with high-privacy concern are much more impacted by privacy policy contents than individuals with low-privacy concern.

The findings could provide website managers with guidelines on how to design privacy policy contents by reducing user perceptions of vulnerability and enhancing user perceptions of corporate benevolence. The managers need to focus on customers' perceived vulnerability and corporate benevolence when launching or updating privacy policies. Furthermore, the managers also need to attend to users' privacy concerns, especially for multinational companies or companies with specific consumer groups.

This study extends the current privacy policy literature by articulating the separate influences of the three privacy policy dimensions and their impact mechanisms on perceived effectiveness of privacy policy. It also uncovers privacy concerns as a boundary condition that influence the effects of privacy policy contents on users' privacy perceptions.

Given that the use of Google Trends data is helpful to improve forecasting performance, this study aims to investigate whether the precision of forecast combination can benefit from the use of Google Trends Web search index along with the encompassing set.

Grey prediction models generate single-model forecasts, while Google Trends index serves as an explanatory variable for multivariate models. Then, three combination sets, including sets of univariate models (CUGM), all constituents (CAGM) and constituents that survive the forecast encompassing tests (CSET), are generated. Finally, commonly used combination methods combine the individual forecasts for each combination set.

The tourism volumes of four frequently searched-for cities in Taiwan are used to evaluate the accuracy of three combination sets. The encompassing tests show that multivariate grey models play a role to be reckoned with in forecast combinations. Furthermore, the empirical results indicate the usefulness of Google Trends index and encompassing tests for linear combination methods because linear combination methods coupled with CSET outperformed that coupled with CAGM and CUGM.

With Google Trends Web search index, the tourism sector may benefit from the use of linear combinations of constituents that survive encompassing tests to formulate business strategies for tourist destinations. A good forecasting practice by estimating ex ante forecasts post-COVID-19 can be further provided by scenario forecasting.

To improve the accuracy of combination forecasting, this research verifies the correlation between Google Trends index and combined forecasts in tourism along with encompassing tests.

過去的研究顯示 Google 搜尋趨勢資料有助於改善旅遊需求預測的準確度，本研究就此進一步探討 Google 搜尋趨勢網頁搜尋指標與涵蓋性檢定的使用對於組合預測準確度所造成的影響。

本研究以 Google 搜尋趨勢指標做為多變量灰色預測模式的解釋變數，並以單變量與多變量灰色模式產生各別預測值。在分別產生由所有的單變量模式 (CUGM)所有的模式 (CAGM), 以及經過涵蓋性檢定所留存下來之模式 (CSET) 所組成之集合後，就各別的組合集以常用的組合方法產生預測值。

以台灣的四個熱搜旅遊城市的旅遊人數進行三個組合集的預測準確度分析。涵蓋性檢定顯示多變量灰色模式在組合預測中扮演重要的角色，而結果亦呈現線性組合方法在 CSET優於在 CUGM 與 CAGM 的準確度，突顯搜尋趨勢指標與涵蓋性檢定對於線性組合方法的有用性。

藉由 Google 搜尋趨勢網頁搜尋指標與涵蓋性檢定，旅遊部門應可透過線性組合方法的預測規劃旅遊目的地的經營策略。新冠疫情下於各季的事前預測亦可結合情境預測具體呈現。

為提升組合預測在旅遊需求的預測準確度，本研究結合涵蓋性檢定以分析 Google 搜尋趨勢指標與組合預測準確度之間的關聯性。

旅遊需求,涵蓋性檢定,Google 搜尋趨勢,灰色預測,組合預測

研究型论文

Dado que el uso de los datos de Google Trends es útil para mejorar la precisión de las predicciones, este estudio examina si el uso del índice de búsqueda web de Google Trends combinado con la agregación de relevancia puede mejorar la precisión del predictor.

El modelo predictivo gris genera predicciones bajo un único modelo, mientras que el modelomultivariado utiliza el indicador Google Trends como variable explicativa. Se generaron tresensamblajes generales, incluido el Modelo armónico único (CUGM), los ensamblajes de todos loscomponentes (CAGM) y la prueba de presencia de componentes con predicción (CSET). Laspredicciones individuales encada grupo luego se combinan utilizando métodos de correlación deuso común.

Utilizando el número de turistas en las cuatro ciudades más investigadas de Taiwán, los tresgrupos combinados se clasificaron según su precisión. Las pruebas incluidas muestran que losmodelos multivariados en escala de grises son importantes para la predicción. Además, losresultados de las pruebas muestran que el índice de Google Trends y las pruebas que incluyenmétodos de suma lineal son útiles porque los métodos combinados con CSET funcionan majorque los métodos combinados con CSET. CAGM y VCUG.

La industria de viajes puede usar el índice de búsqueda web de Google Trends para desarrollarestrategias comerciales para atracciones basadas en un conjunto lineal de componentes.

Con el objetivo de mejorar la precisión de los pronósticos agregados, este estudio investiga larelación entre el índice de tendencias de Google y las expectativas generales de viaje junto con laevidencia global.

Demanda de viajes, Experiencia global, Tendencias de Google, Predicción gris

Trabajo de investigación

This research investigates subgroup formation as an important mediator in the goal interdependence-intragroup conflict linkage. Specifically, it proposes that subgroup formation will mediate the relationship between cooperative goal interdependence and intragroup conflict, but not for competitive goal interdependence and intragroup conflict. Further, competitive goal interdependence is posited to have direct, positive effects on intragroup conflict. Using structural equation modeling analyses with 79 student project teams, the findings revealed that subgroup formation fully mediated the relationship between cooperative goal interdependence and task and process conflict, but only partially mediated the relationship between cooperative goal interdependence and relationship conflict. As predicted, subgroup formation did not mediate the relationship between competitive goal interdependence and intragroup conflict; however, competitive goal interdependence was negatively, rather than positively, related to intragroup conflict.

In light of the current challenges in the business environment, firms, particularly those involved in supply chains, must foster innovation. In this context, the current study employs the theory of dynamic organizational capabilities (DOCs) to track supply chain learning capabilities (SCLCs) and independently uncover their relationship to innovation at both the product and process levels. Similarly, the study intends to investigate the influence of technological turbulence (TT) on these relationships as a moderating variable.

The data were gathered using in-person interviews with 189 CEOs with some supply chain management proficiency from small and medium-sized enterprises (SMEs) in the industrial zones of eastern Egypt. The study used a survey approach to collect data, and the SEM-PLS technique was utilized to analyze the data.

Study findings revealed that SCLCs positively affect product and process innovation. In addition, TT positively moderates the relationship between SCLCs and product and process innovation, except for risk-taking capability. Further theoretical and practical implications are derived from the study findings.

This research adds to the knowledge of the dynamic capabilities theory (DCT), which affects how firms interact with their external environment. Studying learning capabilities are employed as essential competencies to counterbalance high levels of TT in the external environment in terms of innovative performance and vice versa if firms do not attempt to strengthen their dynamic learning capabilities in supply chains. In addition, this study contributes to the literature by studying learning capabilities from the external perspective, where SCLCs are being developed as a new variable to improve innovation.

This paper aimed to introduce a systematic body of knowledge via a scientometric review, guiding the sustainable transition from conventional construction to prefabricated construction. The construction industry currently faces a challenge to balance sustainable development and the construction of new buildings. In this context, one of the most recent debates is prefabricated construction. As an emerging construction approach, although existing knowledge makes contributions to the implementation of prefabricated construction, there is a lack of a comprehensive and in-depth overview of the critical knowledge themes and gaps.

This study uses the scientometric analysis to review the state-of-the-art knowledge of prefabricated construction. It retrieved data from the Web of Science core collection database. CiteSpace software was used to conduct the analysis and visualization; three analysis methods identify the knowledge hotspots, knowledge domains and knowledge topics. Finally, according to integrating the hidden connections among results, a body of knowledge for prefabricated construction application can be inferred.

The results show that 120 knowledge hotspots, five critical knowledge domains and five prominent knowledge topics are vital for promoting implementation of prefabricated construction. Based on the afore analysis, a body of knowledge for prefabricated construction that can systematically cover a broad knowledge of prefabricated construction-related research and activities are integrated and proposed in this paper.

Body of knowledge systematically covers a broad knowledge of prefabricated construction applications and is vital to guide researchers and practitioners to conduct related research and activities, thereby promoting the sustainable transition to prefabricated construction implementation.

This study aims to examine the impact of mobile interactivity dimensions (active control, personalization, ubiquitous connectivity, connectedness, responsiveness and synchronicity) on customer engagement.

A quantitative field survey study was conducted to collect the required data from actual users of mobile shopping in three countries: Jordan, the United Kingdom (UK) and Saudi Arabia.

The results are based on structural equation modelling and support the impact of five dimensions of mobile interactivity: active control, personalization, ubiquitous connectivity, responsiveness and synchronicity. The impact of connectedness is not supported. The results also support the significant impact of customer engagement on customer loyalty.

This study only considered the shopping activities conducted by mobile channels, while other channels (e.g., online channels, traditional channels and social media shopping channels) are not considered. Furthermore, the current model does not consider the impact of personal factors (e.g., technology readiness, self-efficacy and user experience). The results of the current study present a foundation that can guide marketers and practitioners in the area of mobile shopping.

This study enriches the current understanding of the impact of mobile interactivity on mobile shopping, as well as how mobile interactivity can enhance the level of customer engagement.

The purpose of this study is to build a consensus model of social network group decision-making (SNGDM) based on improved PageRank algorithm. By objectively and fairly measuring the evaluation ability of participants in the decision-making process, the authors can improve the fairness and authenticity of the weight solution of decision-makers (DM) in the decision-making process. This ensures the reliability of the final group consensus results.

This study mainly includes six parts: preference expression, calculation of DM's weight, preference aggregation, consensus measurement, opinion adjustment and alternative selection. First, Pythagorean fuzzy expression is introduced to express the preference of DMs, which expands the scope of preference expression of DMs. Second, based on the social network structure among DMs, the process of “mutual judgment” among DMs is increased to measure the evaluation ability of DMs. On this basis, the PageRank algorithm is improved to calculate the weight of DMs. This makes the process of reaching consensus more objective and fair. Third, in order to minimize the evaluation difference between groups and individuals, a preference aggregation model based on plant growth simulation algorithm (PGSA) is proposed to aggregate group preferences. Fourth, the consensus index of DMs is calculated from three levels to judge whether the consensus degree reaches the preset value. Fifth, considering the interaction of DMs in the social network, the evaluation value to achieve the required consensus degree is adjusted according to the DeGroot model to obtain the overall consensus. Finally, taking the group preference as the reference, the ranking of alternatives is determined by using the Pythagorean fuzzy score function.

This paper proposes a consensus model of SNGDM based on improved PageRank algorithm to aggregate expert preference information. A numerical case of product evaluation is introduced, and the feasibility and effectiveness of the model are explained through sensitivity analysis and comparative analysis. The results show that this method can solve the problem of reaching consensus in SNGDM.

Different DMs may have different judgment criteria for the same decision-making problem, and the angle and depth of considering the problem will also be different. By increasing the process of mutual evaluation of DMs, the evaluation ability of each DM is judged only from the decision-making problem itself. In this way, the evaluation opinions recognized by most DMs will form the mainstream of opinions, and the influence of corresponding DMs will increase. Therefore, in order to improve the fairness and reliability of the consensus process, this study measures the real evaluation ability of DMs by increasing the “mutual judgment” process. On this basis, the defect of equal treatment of PageRank algorithm in calculating the weight of DMs is improved. This ensures the authenticity and objectivity of the weight of DMs. That is to improve the effectiveness of the whole evaluation mechanism. This method considers both the influence of DMs in the social network and their own evaluation level. The weight of DMs is calculated from two aspects: sociality and professionalism. It provides a new method and perspective for the calculation of DM’s weight in SNGDM.

Understanding customer behavior from the perspective of channel integration has become a major stream of research in multi-channel retailing literature. Yet, despite recent advancements in scholarship, how retailers can most effectively sustain customers in online retailing remains unclear. Scholars have suggested online–offline channel integration (OOCI) as an effective multi-channel approach for increasing online loyalty; yet, few studies have explored OOCI's influencing mechanism. This study addresses that gap by investigating how OOCI helps achieve customer loyalty online and further examines the moderating role of retailer credibility in the influencing mechanism of OOCI.

The research model driving this study draws upon the stimulus-organism-response (S-O-R) model and cue consistency theory. The authors collected a sample of 259 customers in China with experience making multi-channel purchases from retailers that have implemented OOCI in online retailing. Structural equation modeling and response surface analyses were employed to conduct data analysis.

The results revealed that the relationship between OOCI and customers' online channel loyalty was mediated by customers' perceptions of the usefulness and risks of online channel usage. The results also found that congruence and incongruence between informational OOCI (IOOCI) and fulfillment OOCI (FOOCI) had different curvilinear associations with perceived online channel usefulness and perceived online channel risk. In addition, retailer credibility weakened the effects of IOOCI on perceived online channel usefulness and FOOCI on perceived online channel risk but strengthened the effect of IOOCI on perceived online channel risk and had no impact on the effect of FOOCI on perceived online channel risk.

Theoretical and practical implications of this study are also discussed.