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The purpose of this paper is to identify staff skills and competencies on which Chinese open universities should focus their professional development activities in order to enhance the quality of open and distance learning (ODL) in China.

Data were collected from surveys of 220 academic and academic-related Chinese staff, each of whom participated in one of seven 3-day Learning Design and Course Creation (LDCC) workshops at three large regional open universities in China between 2017 and 2019. The workshops were based on the UK Open University (UKOU) approach to learning design (LD) and course creation. Using content analysis methodology, textual responses were analysed and compared against the Instructional Design Competencies framework provided by the International Board of Standards for Training, Performance and Instruction (IBSTPI).

(1) Designing instructional interventions, (2) keeping up to date with design theories, (3) communicating in order to manage stakeholders, teams and projects were the main competencies identified from the participants responses. However, these three identified competencies differed in emphasis between the institutions that took part.

In Western higher education institutions (HEIs), LD has developed as an important approach to improving quality. A need has been identified for robust approaches to quality and professional development opportunities to enhance teaching and learning standards in open and distance learning (ODL) in China. This paper identifies and discusses specific LD skills and competencies that could be targeted to improve the quality of ODL in China.

Distribution of long-haul goods could be managed via multimodal transportation networks where decision-maker has to consider these factors including the uncertainty of transportation time and cost, the timetable limitation of selected modes and the storage cost incurred in advance or delay arriving of the goods. Considering the above factors comprehensively, this paper establishes a multimodal multi-objective route optimization model which aims to minimize total transportation duration and cost. This study could be used as a reference for decision-maker to transportation plans.

Monte Carlo (MC) simulation is introduced to deal with transportation uncertainty and the NSGA-II algorithm with an external archival elite retention strategy is designed. An efficient transformation method based on data-drive to overcome the high time-consuming problem brought by MC simulation. Other contribution of this study is developed a scheme risk assessment method for the non-absolutely optimal Pareto frontier solution set obtained by the NSGA-II algorithm.

Numerical examples verify the effectiveness of the proposed algorithm as it is able to find a high-quality solution and the risk assessment method proposed in this paper can provide support for the route decision.

The impact of timetable on transportation duration is analyzed and making a detailed description in the mathematical model. The uncertain transportation duration and cost are represented by random number that obeys a certain distribution and designed NSGA-II with MC simulation to solve the proposed problem. The data-driven strategy is adopted to reduce the computational time caused by the combination of evolutionary algorithm and MC simulation. The elite retention strategy with external archiving is created to improve the quality of solutions. A risk assessment approach is proposed for the solution scheme and in the numerical simulation experiment.

In today’s highly competitive milieu, many organizations are entering into the international market to gain greater share and take advantage of higher production. However, the introduction of advanced technology has brought a significant amount of competition within the supply chain especially within the bounds of the power distribution sector. This study aims to examine how information and knowledge sharing influence supply chain performance in the power distribution sector.

The quantitative method and cross-sectional survey design were used in the study. A sample of 200 officers specifically selected from power distribution companies was used for the study. Data was analysed using descriptive, correlation, regression and structural equation models.

The relationship between information sharing, knowledge sharing and performance of the supply chain are positively mediated by supply chain collaboration. The findings indicate that technological innovation positively moderates the relationship between information sharing, knowledge sharing and supply chain performance.

Information sharing, knowledge sharing and technological innovation are critical indicators driving the supply chain operation of power distribution organizations.

This study presents a contemporary approach towards understanding knowledge and information sharing as antecedents of supply chain performance.

The purpose of this paper is to investigate the performance implications of internationalization strategies for Chinese multinational corporations (MNCs). Specifically, the authors examine the individual and joint effects of speed of internationalization in developed and developing countries and age on Chinese MNCs’ performance.

The authors constructed a unique and comprehensive database on the internationalization strategies of 206 Chinese firms over 14 years and deployed random-effect regressions to assess the effects of age, speed of expansion in terms of number of subsidiaries and countries, and the types of destination (developed vs developing country) on firm performance.

The analyses show that age is negatively related to performance but rapid expansion of subsidiaries in developing countries and geographic scope in developed countries are positively related to performance. In addition, the impacts of young age and two types of expansion, fast expansion of subsidiaries in developing countries and fast expansion of geographic scope in developed countries, are cumulative.

The authors combine the arguments of the learning advantages of newness and fast movers and model the simultaneous effects of age and speed of two types of international expansion (in terms of number of subsidiaries and countries) in both developed and developing countries on performance. The strong empirical support for the hypotheses based on analyses of a unique data set of Chinese MNCs’ internationalization patterns lends credence to the proposed model.

This paper aims to provide a predictive framework of customer churn through six stages for accurate prediction and preventing customer churn in the field of business.

The six stages are as follows: first, collection of customer behavioral data and preparation of the data; second, the formation of derived variables and selection of influential variables, using a method of discriminant analysis; third, selection of training and testing data and reviewing their proportion; fourth, the development of prediction models using simple, bagging and boosting versions of supervised machine learning; fifth, comparison of churn prediction models based on different versions of machine-learning methods and selected variables; and sixth, providing appropriate strategies based on the proposed model.

According to the results, five variables, the number of items, reception of returned items, the discount, the distribution time and the prize beside the recency, frequency and monetary (RFM) variables (RFMITSDP), were chosen as the best predictor variables. The proposed model with accuracy of 97.92 per cent, in comparison to RFM, had much better performance in churn prediction and among the supervised machine learning methods, artificial neural network (ANN) had the highest accuracy, and decision trees (DT) was the least accurate one. The results show the substantially superiority of boosting versions in prediction compared with simple and bagging models.

The period of the available data was limited to two years. The research data were limited to only one grocery store whereby it may not be applicable to other industries; therefore, generalizing the results to other business centers should be used with caution.

Business owners must try to enforce a clear rule to provide a prize for a certain number of purchased items. Of course, the prize can be something other than the purchased item. Business owners must accept the items returned by the customers for any reasons, and the conditions for accepting returned items and the deadline for accepting the returned items must be clearly communicated to the customers. Store owners must consider a discount for a certain amount of purchase from the store. They have to use an exponential rule to increase the discount when the amount of purchase is increased to encourage customers for more purchase. The managers of large stores must try to quickly deliver the ordered items, and they should use equipped and new transporting vehicles and skilled and friendly workforce for delivering the items. It is recommended that the types of services, the rules for prizes, the discount, the rules for accepting the returned items and the method of distributing the items must be prepared and shown in the store for all the customers to see. The special services and reward rules of the store must be communicated to the customers using new media such as social networks. To predict the customer behaviors based on the data, the future researchers should use the boosting method because it increases efficiency and accuracy of prediction. It is recommended that for predicting the customer behaviors, particularly their churning status, the ANN method be used. To extract and select the important and effective variables influencing customer behaviors, the discriminant analysis method can be used which is a very accurate and powerful method for predicting the classes of the customers.

The current study tries to fill this gap by considering five basic and important variables besides RFM in stores, i.e. prize, discount, accepting returns, delay in distribution and the number of items, so that the business owners can understand the role services such as prizes, discount, distribution and accepting returns play in retraining the customers and preventing them from churning. Another innovation of the current study is the comparison of machine-learning methods with their boosting and bagging versions, especially considering the fact that previous studies do not consider the bagging method. The other reason for the study is the conflicting results regarding the superiority of machine-learning methods in a more accurate prediction of customer behaviors, including churning. For example, some studies introduce ANN (Huang et al., 2010; Hung and Wang, 2004; Keramati et al., 2014; Runge et al., 2014), some introduce support vector machine ( Guo-en and Wei-dong, 2008; Vafeiadis et al., 2015; Yu et al., 2011) and some introduce DT (Freund and Schapire, 1996; Qureshi et al., 2013; Umayaparvathi and Iyakutti, 2012) as the best predictor, confusing the users of the results of these studies regarding the best prediction method. The current study identifies the best prediction method specifically in the field of store businesses for researchers and the owners. Moreover, another innovation of the current study is using discriminant analysis for selecting and filtering variables which are important and effective in predicting churners and non-churners, which is not used in previous studies. Therefore, the current study is unique considering the used variables, the method of comparing their accuracy and the method of selecting effective variables.

Omnichannel retail are catching increasing attention from multiple research area, due to its widespread and essential application in retailing industry. The principal object of this paper is to systematically review current studies on omnichannel retail in information systems, operations and marketing research area. Further, the second purpose of this study is to provide insights and guides on omnichannel research to facilitate advance research in the area.

A systematic review on omnichannel retail in retailing industry has been conducted. 33 research articles from 2014 to 2020 from Financial Times 50 journals (FT 50 journals) were identified to be reviewed. The articles were reviewed on the basis of study area namely: information systems, operations and marketing. These research areas were further divided into subcategories to provide in-depth and crystal clear review of literature.

The review outcome showed that omnichannel topics are active in management studies. There are three main research areas: information systems, operations management and marketing. This study found that IT enhances channel capabilities and expands demand in omnichannel retail; operations can be better optimized based on understanding of cross-channel interactions, product category and retailer types; consumer heterogeneity, product category, channel capabilities and retailer type are all found to contribute to complexities of omnichannel marketing.

This study provides a pioneering review to gauge the updated literature concerned with omnichannel research in terms of IT, operations and marketing perspective. Further, a systematic literature review provides insights and guides for future significant studies on potential research subjects.

The purpose of this paper is to examine the types of justice that affect knowledge acquisition and opportunism in strategic alliances and how these justice mechanisms function.

Data were collected from both top-level and operating-level boundary spanners in 295 strategic alliances in China (a total of 590 boundary spanners). A structural equation model (SEM) with bias-corrected bootstrap method was used to test the hypotheses.

The results show that although both procedural justice and distributive justice are important in deterring opportunism, procedural justice is more effective at enhancing knowledge acquisition than distributive justice is. The results also demonstrate that boundary spanners' helping behaviors are more effective at fostering knowledge acquisition, whereas boundary spanners' voice behaviors have more impact on mitigating opportunism. In addition, boundary spanners' citizenship behaviors partially mediate the relationship between justice and interfirm-level performance.

This study adds a boundary-spanning lens to justice literature by uncovering the missing link between justice and alliance outcomes.

This paper aims to examine alliance governance at different hierarchical levels.

The data is collected from both top-level and operating-level managers in 286 strategic alliances in China (a total of 572 managers). Hierarchical moderated regression models are adopted to test the hypotheses and two-stage regression analyzes are used to correct for endogeneity.

This paper finds that relational governance has a greater impact on alliance performance than contract utilization at the top level. Furthermore, the simultaneous use of relational governance at the top and operating levels have a detrimental impact on alliance performance. Finally, top-level contract utilization has a negative interaction with operating-level relational governance but a positive interaction with operating-level contract utilization.

First, the cross-sectional nature of the data collection approach provides only a snapshot of how each type of governance mechanism and its interactions affect alliance performance. Second, the sample is limited to firms located in emerging markets.

Managers should realize that the effectiveness of contract and relational governance mechanisms varies across different management levels and they should be cautious about the cross-level governance mechanism alignment.

This study advances the interfirm governance literature in that this paper examined alliance governance at different hierarchical levels and provides new insights into the ongoing debate on whether the contract and relational governance mechanisms function as complements or substitutes by exploring the governance alignment across different alliance hierarchies.

It is widely accepted that business excellence comes from firm-specific factors. However, it is still unclear how institutional relatedness – the degree of embeddedness with the dominant institutions that confer resources and legitimacy, influences the business excellence of the firm. The purpose of this study is to explore the influence of three kinds of institutional relatedness, i.e. home government ties, initial public offerings (IPOs) and alliances with foreign firms, on the business excellence of Chinese firms.

This study uses a sample of firms enlisted on the “Most Respected Companies” rank in China during the period 2002–2015 and their paired firms who are absent from the list, by means of ordinary least square regression estimator, to explore the relationship between institutional relatedness and business excellence.

The empirical results suggest that IPOs and alliances with foreign firms significantly strengthen firms’ business excellence. Furthermore, home government ties have positive effects on outbound IPOs and alliances with foreign firms but hinder business excellence.

This study extends the business excellence literature by characterizing institutional rather than firm-specific factors from an institution-based view. It also enriches research on outcomes of institutional relatedness through investigating empirically its impact on business excellence. The findings provide new insights into the dual role of home government ties in achieving business excellence.

This study aims to challenge the generic interpretation of Multilatinas as Latin American firms that have been able to internationalize because of highly competitive strategic capabilities. The authors test whether capabilities that international business researchers commonly associate with internationalization are necessary at different stages of the internationalization process to better understand the extent to which emerging market (EM) firms need to develop them.

International business research suggests a positive association between strategic capabilities and firm internalization. However, it remains unclear what specific capabilities are necessary and when they are necessary. These questions are particularly important in the context of the internationalization of firms from emerging economies, such as Latin America. The authors apply necessary condition analysis (NCA) on a sample of Latin American firms at different internationalization stages to test what strategic capabilities represent necessary conditions for becoming a Multilatina.

The findings suggest that only a few strategic capabilities are necessary for Latin American firms to become “Multilatinas”. While entrepreneurial orientation and marketing and sales capabilities represent necessary conditions, EM firms may internationalize even though other capabilities are developed to a lesser extent. The authors reflect on how shifts in local markets and technology drive the emergence of different types of Multilatinas.

Measuring strategic capabilities across multiple EM firms implies a risk that firm-specific aspects are not fully captured. While the authors focused on the comparative competitive strength of capabilities and took great care to minimize measurement error, the authors acknowledge possible bias. Also, while NCA does not require a minimum sample size, findings from our sample of firms from four countries may not generalize to the region or other EMs.

As a relatively new statistical technique, the use of NCA has spread rapidly. To the best of the authors’ knowledge, the linkage between organizational capabilities and firm internationalization has not been tested from a necessary conditions perspective yet. The reflections on the “Multilatina” concept based on the notion of EM firms as configurations of strategic capabilities inform current debates on EM multinational enterprises.