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Effective cooperative communication and knowledge management capabilities are particularly important in the cross-team cooperation. Based on the social exchange theory, this paper aims to further explore the role and influence mechanism of cooperative communication and knowledge sharing on co-innovation performance in open innovation projects (OIPs).

This paper takes China’s high-tech enterprises as the research object by using a paired questionnaire survey based on 168 pairs of OIPs. Hayes’ PROCESS programme and the bootstrap technique are used to test the hypothesis model and the significance of the mediation effect.

This study finds that cooperative communication is an effective mechanism for the internal and external interaction of OIPs. Knowledge sharing plays an intermediary role in the relationship between cooperative communication and co-innovation performance; both explicit knowledge sharing and tacit knowledge sharing have a significant positive impact on co-innovation performance. And the impact of cooperative communication on co-innovation performance is based on effectively promoting the knowledge sharing of partners.

This research incorporates cooperative communication, knowledge sharing and co-innovation performance into a unified research framework to study the co-innovation at the project level, enriches the research scenario on the mechanism of cooperative behaviour, deepens the understanding of the role and influence mechanism of cooperative communication and knowledge sharing on co-innovation performance in OIPs, verifies the mediating role of knowledge sharing to co-innovation, and also helps extend the social exchange theory to a new research setting.

The purpose of this chapter is to develop a new perspective model of well-balanced information economy.

The methodological tools of this work are based on application of the method of comparative analysis for comparing the old and the new model of information economy, modeling of socio-economic systems, simplex-method as an algorithm for solving the optimization task of linear programming, related to improvement of the conceptual model of information economy, and the method of formalization for graphical presentation of a new model of information economy.

The developed and presented author’s perspective model of information economy envisages balancing in four main directions: infrastructural possibilities and readiness for their practical usage by society and business; state regulation and market self-management; existing and new information; and external information exchange. Despite the clarity and strictness of conceptual settings, the new model is peculiar for increased flexibility and adaptation to the peculiarities of the socio-economic practice of economic system. Thus, the new model of well-balanced information economy allows overcoming all drawbacks – logical mismatches and contradictions – of the existing (old) model of information economy.

Due to optimization of the conceptual model, its practical implementation in economic activities of modern socio-economic systems becomes possible – as a result of which, with accumulation of experience, it will be possible to assess true value of the idea of information economy’s formation for humanity and the global economic system.

Standard estimation of ARMA models in which the AR and MA roots nearly cancel, so that individual coefficients are only weakly identified, often produces inferential ranges for individual coefficients that give a spurious appearance of accuracy. We remedy this problem with a model that uses a simple mixture prior. The posterior mixing probability is derived using Bayesian methods, but we show that the method works well in both Bayesian and frequentist setups. In particular, we show that our mixture procedure weights standard results heavily when given data from a well-identified ARMA model (which does not exhibit near root cancellation) and weights heavily an uninformative inferential region when given data from a weakly-identified ARMA model (with near root cancellation). When our procedure is applied to a well-identified process the investigator gets the “usual results,” so there is no important statistical cost to using our procedure. On the other hand, when our procedure is applied to a weakly identified process, the investigator learns that the data tell us little about the parameters – and is thus protected against making spurious inferences. We recommend that mixture models be computed routinely when inference about ARMA coefficients is of interest.

Organizations are fast adopting new technologies such as automation, analytics and artificial intelligence, collectively called intelligent automation, to drive digital transformation. When adopting intelligent automation, there is a need to understand the success factors of these new technologies and adapt agile software development (ASD) practices to meet customer expectations. The purpose of this paper is to explore the success factors of intelligent automation and create a framework for managers and practitioners to meet dynamic business demands. Total interpretive structural modeling (TISM) framework is a suitable approach to integrate quantitative measurement with qualitative semi-structured interviews capturing the context of the individual organization environment.

This paper identified agility factors and their interrelationships using a TISM framework. TISM results were validated using a one-tailed t-test to confirm the interrelationships between factors. Furthermore, the agility index of a case project organization was assessed using a graph-theoretic approach (GTA) to identify both the triggering factors for agility success and improvement proposals.

Results showed that leadership vision, organization structure and program methodology were driving factors. The TISM model was validated statistically and the agility index of the intelligent automation case project organization was calculated to be79.5%. Here, a GTA was applied and the triggering factors for improvement of the agility index were identified.

The limitations of the study are described along with the opportunities for future research as the field evolves through the rapid innovation of technology and products.

The increasing role of digital transformation in enterprise strategy and operations requires practitioners to understand how ASD practices must be planned, measured and/or improved over time through the implementation of automation, analytics and artificial intelligence programs. The TISM digraph provides a framework of hierarchical structure to organize the influencing factors, which assists in achieving organizational goals. This study highlights the driving factors which contribute to the success of intelligent automation projects and project organizations.

This is a first attempt to analyze the interrelationships among agility factors in intelligent automation projects (IAP) using TISM and the assessment of the agility index of a case IAP organization using a GTA.

This paper aims to investigate the relation between technological innovation modes and their impact on eco-efficiency of industrial enterprises in China.

This paper first constructs a model to evaluate and measure the eco-efficiency of industrial enterprises in China from 2006 to 2010. Second, this paper compares the role of technological innovation modes – specifically, domestic independent innovation, foreign technology import and domestic technology transfer – in improving eco-efficiency of industrial enterprises in the Eastern, Central and Western regions of China by logarithmic regression.

The study finds that domestic independent innovation has a positive significant influence in improving eco-efficiency of industrial enterprises in the Eastern region; domestic technology transfer has a positive significant role in the Central region; and foreign technology import and domestic technology transfer positively affect the Western region.

This paper is the first to identify the role of technological innovation modes in improving eco-efficiency. The findings can help enterprises in the three regions adopt the most effective technological innovation mode. In addition, the results provide valuable insights into policy development to improve China’s overall eco-efficiency and to balance economic and industrial development among the three regions.

Using a sample of manufacturing firms listed in China between 2007 and 2019, first, this paper aims to examine whether peer firms influence corporate trade credit supply. Next, the authors examine the channels through which peer firms influence corporate trade credit supply by testing the predictions of rivalry and information theories. Furthermore, the authors examine the heterogeneity of the industry peer effect on corporate trade credit supply. Finally, the authors examine the economic consequences of the industry peer effect on corporate trade credit supply.

The sample includes all manufacturing firms listed on both the Shanghai and Shenzhen securities exchanges for the sample period from 2007 to 2019, and the data come from the China Stock Market & Accounting Research database. The authors use the fixed effects method to examine the industry peer effect on trade credit supply. The results are robust to a series of robustness tests. To address the potential endogeneity problem, the authors adopt appropriate instruments by estimating instrumental variable models (two-stage least square). The authors use Heckman’s two-stage model to mitigate the sample selection bias.

The authors provide strong empirical evidence showing that the industry peer effect on trade credit supply exists in the manufacturing sector. It is also found that both competitive rivalry-based and information-based theories can provide explanations of the industry peer effect on trade credit supply. This process is both active imitation and passive reaction. Additional analysis suggests that the industry peer effect on trade credit supply is more pronounced for state-owned firms, firms with low customer concentration and firms with high geographical proximity. The amplification effect and spillover effect are the economic consequences of the industry peer effect on trade credit supply. In other words, the trade credit supply based on peer effect will not only increase the liquidity risk of the firm per se but also induce and increase the liquidity risk of the industry.

The study makes some important contributions. First, the authors find robust evidence that peer firms’ trade credit supply is an important factor in explaining corporate trade credit supply, which extends the literature by connecting the firm’s trade credit supply with the peer effect. Second, the study provides a new micro-perspective for understanding that firms use trade credit supply as a tool of competition, which proves the importance of rivals’ decision-making as a determinant of corporate decisions. Third, the authors examine the industry peer effect on trade credit supply, which not only helps to guide firms to pay more attention to the potential risk and spillover effects of the trade credit supply decision-making relevance but also helps to clarify the industry interaction phenomenon of corporate decision-making behavior. It is an important practical significance to play a role as a bridge between the microlevel of the firm and the meso-level of the industry. Finally, the study provides inspiration for the formulation of industry norms and policies.

The purpose of this paper is to formulate the reliability optimization problem in stochastic and interval domain and also to solve the same under different stochastic set up.

Stochastic programming technique has been used to convert the chance constraints into deterministic form and the corresponding problem is transformed to mixed-integer constrained optimization problem with interval objective. Then the reduced problem has been converted to unconstrained optimization problem with interval objective by Big-M penalty technique. The resulting problem has been solved by advanced real coded genetic algorithm with interval fitness, tournament selection, intermediate crossover and one-neighbourhood mutation.

A new optimization technique has been developed in stochastic domain and the concept of interval valued parameters has been integrated with the stochastic setup so as to increase the applicability of the resultant solution to the interval valued nonlinear optimization problems.

The concept of probability distribution with interval valued parameters has been introduced. This concept will motivate the researchers to carry out the research in this new direction.

The application of genetic algorithm is extended to solve the reliability optimization problem in stochastic and interval domain.

Addresses one of the fundamental issues of e‐marketing: how to attract and win over the consumer in the highly competitive Internet marketplace. Analyses the factors affecting the online consumer's behavior and examines how e‐marketers can influence the outcome of the virtual interaction and buying process by focusing their marketing efforts on elements shaping the customer's virtual experience, the Web experience. Identifying the Web experience components and understanding their role as inputs in the online customer's decision‐making process are the first step in developing and delivering an attractive online presence likely to have the maximum impact on Internet users. Click‐and‐mortar firms delivering superior Web experience influence their physical clients’ perceptions and attitudes, driving additional traffic to traditional sales outlets. Provides a contribution to the theoretical debate around the factors influencing the online consumer's behavior and outlines some noticeable similarities and differences between the traditional and virtual consumers.

The purpose of this paper is to investigate the effect of online service failure on online customer satisfaction and offline customer loyalty, and the moderating role of brand strength is also examined. While extant research on brick and click service mode recognizes the positive spillover effect from offline stores to online stores, this study analyzes the negative spillover effect from online stores to offline stores.

This paper tests the hypotheses by two studies. Study 1 is based on a 2 (failure severity: mild vs severe) × 2 (brand strength: strong vs weak) between-subjects experimental design using scenarios in a brick and click retailer context, while study 2 is based on data collected from a scenario-based questionnaire survey and analyzed through the structural equation modeling.

The results indicate that participants exposed to severe online service failure show lower online satisfaction as compared to their counterparts exposed to mild online service failure, but they show the similar level of offline loyalty in both degrees of online service failure. Nevertheless, these results are not moderated by brand strength significantly.

An experimental design and a scenario-based questionnaire survey are used to test the framework. However, the generalizability of the research findings is still limited to a specific study setting. Future research in a different setting is needed to further validate the presented findings.

The findings suggest that physical service providers should adopt aggressive online expansion strategy to seize the market and pay more attention to online service quality rather than online marketing only.

This is one of few studies to explore the risk of brick and click service mode, and provide a clear understanding of the likely occurrence of online service failure and its impact on online customer satisfaction and offline customer loyalty. It extends prior research by exploring non-existence of negative perceptual effect from online service failure to offline customer loyalty in the short run and weakening brand effect, which contributes to cross-channel spillover effect in the integrated multi-channel context and brand building in the internet era.