Leveraging Open-standard Interorganizational Information Systems for Process Adaptability and Alignment: An Empirical Analysis

PurposeThe purpose of this paper is to understand the value creation mechanisms of open-standard inter-organizational information system (OSIOS), which is a key technology to achieve Industry 4.0. Specifically, this study investigates how the internal assimilation and external diffusion of OSIOS help manufactures facilitate process adaptability and alignment in supply chain network.Design/methodology/approachA survey instrument was designed and administrated to collect data for this research. Using three-stage least squares estimation, the authors empirically tested a number of hypothesized relationships based on a sample of 308 manufacturing firms in China.FindingsThe results of the study show that OSIOS can perform as value creation mechanisms to enable process adaptability and alignment. In addition, the impact of OSIOS internal assimilation is inversely U-shaped where the positive effect on process adaptability will become negative after an extremum point is reached.Originality/valueThis study contributes to the existing literature by providing insights on how OSIOS can improve supply chain integration and thus promote the achievement of industry 4.0. By revealing a U-shaped relationship between OSIOS assimilation and process adaptability, this study fills previous research gap by advancing the understanding on the value creation mechanisms of information systems deployment

The effect of strategic synergy between local and neighborhood environmental regulations on green innovation efficiency: The perspective of industrial transfer

Considering the environmental governance dilemma caused by environmental decentralization, this study aims to explore whether the strategic synergy between local and neighborhood environmental regulations can be an essential tool to improve green innovation efficiency and achieve sustainable development. Using the data of industrial firms from 2005 to 2019, and employing network slack-based measure and Tobit regression, this study provides empirical evidence that (1) the green innovation efficiency shows an upward trend in fluctuations but still has great room for improvement; (2) the direct impact of local environmental regulation on green innovation is positive, but the indirect impact through forcing firms to transfer into the neighborhood with loose regulation is negative, that is, the industrial transfer plays a suppression effect; (3) the strategic synergy of environmental regulations has U-shaped and direct effect on green innovation and also has a positive indirect effect through inhibiting the firm's behavior transferring into the neighborhood. This study reveals the influence mechanism of the strategic synergy of local-neighborhood environmental regulations and offers empirical evidence to explain the reason why synergistic environmental governance can effectively promote green innovation, which provides the theoretical guidance for government to formulate environmental policies and construct an environmental governance system

Toward Sustainability:Using Big Data to Explore Decisive Supply Chain Risk Factors Under Uncertainty

Rapid market changes aimed at sustainability have led to supply chain risks and uncertainties in the Taiwanese light-emitting diode industry. These risks and uncertainties can be captured by social media, quantitative and qualitative data (referred to herein as big data), but the industry has been unable to manage this information boom to respond to customer needs. These various types of data have their own characteristics that affect decision making about developing firm capabilities. This study aggregates the various data to undertake an extensive investigation of supply chain risks and uncertainties. Specifically, this study proposes using the fuzzy and grey Delphi methods to identify a set of reliable attributes and, based on these attributes, transforming big data to a manageable scale to consider their impacts. Subsequently, both the fuzzy and grey Decision Making Trial and Evaluation Laboratories applied to determine the causal relationships for supply chain risks and uncertainties. The results reveal that capacity and operations have greater influence than other supply chain attributes and that risks stemming from triggering events are difficult to diagnose and control. The implications, conclusions and findings are addressed

Leveraging social media in new product development: organisational learning processes, mechanisms and evidence from China

Purpose The main purpose of this paper is to investigate how social media can provide important platforms to facilitate organisational learning and innovation in new product development (NPD) process. Design/methodology/approach Using a multiple case-study approach, this study assesses qualitative data collected via 56 interviews from 13 world-leading Chinese companies in the high-technology industry. Findings The study identified three distinct types of organisational learning mechanisms for firms to extract potential innovation inherent in social media. It further determined various organisational enablers that facilitate the connections between these mechanisms and NPD performance. Research limitations/implications This research contributes to the emerging literature on digital product development and organisational learning. The cases were conducted in the Chinese context, hence, the results may not be fully generalisable to other organisations, industries and countries without appropriate re-contextualisation. Practical implications The empirical evidence showcases the various mechanisms adopted by managers in different NPD phases. It identifies several technological and organisational adaptations that managers can apply to smartly scale their social presence and facilitate NPD. Originality/value Despite the exponential growth of social media use in identifying and interacting with external stakeholders, managerial practice and academic research have paid little attention to how social media can be leveraged for NPD. The value of this research comes from applying a qualitative method to gain in-depth insights into the mechanisms for leveraging social media to facilitate innovation in NPD

Improving power quality efficient in demand response: Aggregated heating, ventilation and air-conditioning systems

This study aims to identify the role of aggregated heating, ventilation, and air conditioning (HVAC) loads based on system characteristics using the lazy state switching control mode focusing on the overall power consumption rather individual response speed. This study is attempted to provide secondary frequency regulation using aggregated HVAC loads with more stable operation with the lazy state switching control mode based on conditional switching of the HVAC unit’s working state. The stability of power consumption improves power quality in smart grid design and operation. The aggregated HVAC must reach a stable condition before tracking the automatic generation control signal and fully developed smart grids complex structure. Still, HVAC slowed responses make inappropriate for faster demand response services. Unsuitable control algorithm leads to system instability and HVAC unit overuse. An extended command processing on the client side is proposed to deal with the adjusting command. The unique advantages of the proposed algorithm are three folds. (1) the control algorithm preserves its working state and has nothing conflicting with the lockout constraints for individual system units; (2) the control algorithm shows promising performance in smoothing the overall power consumption for the aggregated population; and (3) the control logic is fully compatible with other control algorithms. The proposed modeling and control strategy are validated against simulations of thousands of units, and the simulation result indicates that the proposed approach has promising performance in smoothing the power consumption of aggregate units’ population

A Big Data Decision-making Mechanism for Food Supply Chain

Many companies have captured and analyzed huge volumes of data to improve the decision mechanism of supply chain, this paper presents a big data harvest model that uses big data as inputs to make more informed decisions in the food supply chain. By introducing a method of Bayesian network, this paper integrates sample data and finds a cause-and-effect between data to predict market demand. Then the deduction graph model that translates foods demand into processes and divides processes into tasks and assets is presented, and an example of how big data in the food supply chain can be combined with Bayesian network and deduction graph model to guide production decision. Our conclusions indicate that the decision-making mechanism has vast potential by extracting value from big data

Sustainable packaged food and beverage consumption transition in Indonesia: Persuasive communication to affect consumer behavior

© 2020 Elsevier B.V. Sustainable consumption transition in relation to consumers’ environmental behavior and manufacturers’ governance of sustainability and persuasive communication has not been adequately addressed by prior studies. This study presents theory on ecological modernization, transition management and persuasive communication to address sustainable consumption transition. This study proposes a valid set of four aspects and fourteen criteria using the Delphi method. The valid attributes are analyzed using fuzzy set theory and decision-making trial and evaluation together to handle the qualitative information and interrelationships among the attributes. This procedure converts qualitative information into numerical data to create a diagram showing the interrelationships among the attributes. This study found that persuasive communication is the most effective factor in convincing consumers to transition to sustainable consumption. Other key factors for this transition include educating consumers, augmenting their knowledge and altering their attitudes toward sustainable consumption. Being environmentally friendly, product labeling, offering an authenticity argument, and reusing and recycling products are the solutions found in this study

Service innovation in sustainable product service systems: Improving performance under linguistic preferences

Sustainable product service systems enable firms that are operating under resource limitations to deliver the best possible outcomes in terms of social well-being and economic growth. However, prior studies have not yet investigated the function of service innovation in sustainable product service systems or analyzed the convergence of importance and performance weighting in maximizing resource utilization in the supply chain. Moreover, prior studies have not yet integrated and proposed a complex interrelationship-driven hierarchical model including qualitative preferences or identifying weighting under linguistic preferences. This study applied the fuzzy Delphi method, fuzzy importance performance analysis and an analytical network process to analyze an interrelationship-driven hierarchical model of service innovation in sustainable product service systems. Hence, this study provides a set of attributes and a hybrid method to assess the model as well as linguistic preferences to weight the importance and performance measures. The results present four features that are included in the model: sustainable consumption, collaborative advantage, innovation activities and service innovation capabilities. Therefore, when building sustainable product service systems, firms should maintain operations and aim for business synergy in self-generated innovative products/services along with high-quality products/services, collaboration innovation and product and service innovations. Managerial and theoretical implications are discussed. © 2018 Elsevier B.V