Joint collaborative planning as a governance mechanism to strengthen the chain of IT value co-creation

Drawing on the relational view of the firm, this paper examines the chain of IT-based co-creation of value and how joint collaborative planning between partners can strengthen this chain. Using data from 51 suppliers in the telecommunication equipment industry, our analysis demonstrates that the greater the partner-specific IT investments made by the firm, the greater its use of supply chain collaborative systems (SCCSs) with those partners and the greater the firm uses SCCSs with partners, the greater its benefits, through the generation of relational rents. Further investigation also shows that partner-specific IT investments is not a predictor of firm benefits and hence confirms the hypothesized chain of IT-based co-creation where IT investments encourage IT use, which in turn creates business value. Finally, our findings also show that joint collaborative planning between partners is an important governance mechanism, anchored on control and trust, that can strengthen the relationship between SCCSs use and firm benefits by impacting the other sources of relational rents tied to the use of SCCSs. This contribution should give academics and practitioners alike a better understanding of how both SCCSs and joint collaborative planning can foster the co-creation of value.     

Using the balanced scorecard in assessing the performance of e-SCM diffusion: A multi-stage perspective

Electronic supply chain management (e-SCM), a specific form of inter-organizational systems, has generally regarded as one of the major strategies to create competitive advantage. The diffusion of e-SCM among trading partners is critical for its final successful use and accordingly, performance impact. However, the diffusion process is complex and dynamic in nature and involves an evolutionary property across time. Innovation diffusion theory (IDT) is defined for effectively exploring diffusion process with multiple stages. Moreover, prior studies have found inconclusive results of IT-enabled performance due to inadequate measures. The balanced scorecard (BSC) with the extension to SCM, incorporating four performance perspectives, is appropriate for overcoming this problem. Grounding on the IDT and BSC, this study proposes a novel framework for exploring the relationships between a stage-based structure and the BSC. Data are collected from a questionnaire survey. The results indicate that there are significant differences between external diffusion and the two earlier stages, adoption and internal diffusion, on the four BSC perspectives. Furthermore, all of the four perspectives are well realized at external diffusion stage. Implications for managers and scholars are discussed.     

SMEs' Preference for Innovation Networks: A Choice Experimental Approach

The objective of this paper is to evaluate the importance of selected characteristics of innovation networks in affecting the decision of food SMEs when joining such networks. The paper develops and tests a series of hypotheses through a choice experiment exercise ran on a sample of 231 firms in six EU countries. The results showed that SMEs prefer networks that are composed of manufacturers and supply chain members, where information is shared confidentially among network partners, and when the network provides support to help the firm build its own networks of partners for innovation. In addition, SMEs' choice of the network is also affected by the interaction between specific network attributes and two firm characteristics, i.e. the firm's collaborative experience and innovation objectives. Based on the results, we conclude that the success of inter‐organizational networks depends on the fit between the network's design and the innovation and networking behaviour of the firms.     

Machine learning approach for finding business partners and building reciprocal relationships

Business development is vital for any firms. However, globalization and the rapid development of technologies have made it difficult to find appropriate business partners such as suppliers and customers, and build reciprocal relationships among them, while it simultaneously offers many opportunities. In this contribution, we propose AI-based approach to find plausible candidates of business partners using firm profiles and transactional relationships among them. We employ machine learning techniques to build a prediction model of customer–supplier relationships. We applied our approach to the large amount of actual business data. The results showed that our approach successfully found potential business partners with F-values of about 84% and reciprocity among them with F-values of about 77%. Using our method, we also developed the Web-based system that helps people in actual businesses to find their new business partners. These contribute to developing one’s own business in the complicated, specialized and rapidly changing business environments of recent years.     

Agents that react to changing market situations

Market-driven agents are negotiation agents that react to changing market situations by making adjustable rates of concession. This paper presents 1) the foundations for designing market-driven strategies of agents, 2) a testbed of market-driven agents, 3) experimental results in simulating the market-driven approach, and 4) theoretical analyses of agents' performance in extremely large markets. In determining the amount of concession for each trading cycle, market-driven agents in this research are guided by four mathematical functions of eagerness, remaining trading time, trading opportunity , and competition. At different stages of trading, agents may adopt different trading strategies, and make different rates of concession. Four classes of strategies with respect to remaining trading time are discussed. Trading opportunity is determined by considering: 1) number of trading partners, 2) spreads-differences in utilities between an agent and its trading partners, and 3) probability of completing a deal. While eagerness represents an agent's desire to trade, trading competition is determined by the probability that it is not considered as the most preferred trader by its trading partners. Experimental results and theoretical analyses showed that agents guided by market-driven strategies 1) react to changing market situations by making prudent and appropriate rates of concession, and 2) achieve trading outcomes that correspond to intuitions in real-life trading.     

A Fuzzy Collaborative Assessment Methodology for Partner Trust Evaluation

Trust level assessment within collaborative networks is an interesting issue in the partner evaluation and partner selection literature. This paper proposes a fuzzy collaborative assessment methodology for partner trust evaluation within horizontal collaborative networks. The proposed approach concerns a group evaluation context where a decision‐making comity associated with a manufacturer needs to evaluate its company's partners for their ranking purposes. Different expertise levels are attributed to the comity members. In this paper, trust level is evaluated based on information‐sharing attributes considered in the literature as critical influencing factors. Different weights are associated with these attributes with respect to their corresponding influence on trust. The semantic fuzzy partitioning method is considered for the collaborative trust assessment based on unbalanced linguistic term sets representing information‐sharing attributes. The developed approach is applied to a real case showing its effectiveness and its objectivity.     

A New Vision of Classical Multi-regional Input–Output Models

We present classical multi-regional multi-sectoral input–output trade structures from the perspective of networks. Unlike existing approaches by which dynamic changes in structures are studied, here, emphasis is given on recovering the stable structure architecture of interrelations in the input–output model. By self-explanatory visual outputs we display the connectivity patterns, interactions of the trading partners, the number of trade linkages per trader and the density of interrelations of the network. Our network analysis traces causal relationships, feedback loops and examines certain global structural properties. The whole computational approach is implemented in Mathematica. Our programming style allows for the creation of iterative schemes explaining the the linkage development among sectors, the influence of regions and sectors system-wise, aspects of the nature of trade and, the evolution of spatial trading/production cycles in growing trading networks.     

A fuzzy-based negotiation approach for collaborative planning in manufacturing supply chains

The coordination of the planning operations across the manufacturing supply chains (MSC) is considered as a major component of supply chain management. As centralized coordination requires relevant information sharing, alternative approaches are needed to synchronize production plans between partners of MSC characterized by decentralized decision making systems with limited information sharing. In this paper, a bi-level fuzzy-based negotiation approach is proposed in order to model collaborative planning between MSC partners. During negotiation, each manufacturer is optimizing a bi-objective planning model. In order to generate optimal production plans, a genetic algorithm is used. To evaluate the exchanged proposals and the satisfaction degree of each partner, the fuzzy logic approach is adopted in the both negotiation levels. The main result of the developed approach consists in a collaborative decision making mechanism allowing the MSC partners to define their optimal production plans while considering the whole negotiating process with the pre-negotiation and post-negotiation stages. Computational tests done for different MSC structures show the effectiveness of the proposed mechanism, which ensures the satisfaction of the manufacturers and the optimality of the final solution. By comparing the results with the ones obtained considering a centralized planning approach, it is shown that the developed negotiation mechanism yields to near optimal solutions with insignificant gaps from the global optimal solutions.     

Factors affecting trading partners’ knowledge sharing: Using the lens of transaction cost economics and socio-political theories

Knowledge sharing allows trading partners to orchestrate the operation of supply chain and capture positions of advantage. Yet, lack of knowledge sharing has been consistently found to be the most critical failure factor in supply chain management. This paper intends to study the factors affecting trading partners’ entering knowledge sharing ties. Drawing upon transaction cost economics and socio-political theories, we developed our research framework. The hypotheses derived were tested by data collected with six medium-sized companies. Data analysis showed that socio-political factors were more robust in affecting the focal firm’s decision on whether to share knowledge with a particular partner. In particular, trust towards the partner and the partner’s power were the primary factors leading the firm to enter the knowledge sharing ties. In contrast, asset specificity did not play an important role in affecting the firm’s knowledge sharing decision. Theoretical contribution and practical implications are discussed.     

Attribute Generation Based on Association Rules

A decision tree is considered to be appropriate (1) if the tree can classify the unseen data accurately, and (2) if the size of the tree is small. One of the approaches to induce such a good decision tree is to add new attributes and their values to enhance the expressiveness of the training data at the data pre-processing stage. There are many existing methods for attribute extraction and construction, but constructing new attributes is still an art. These methods are very time consuming, and some of them need a priori knowledge of the data domain. They are not suitable for data mining dealing with large volumes of data. We propose a novel approach that the knowledge on attributes relevant to the class is extracted as association rules from the training data. The new attributes and the values are generated from the association rules among the originally given attributes. We elaborate on the method and investigate its feature. The effectiveness of our approach is demonstrated through some experiments. Received 6 December 1999 / Revised 28 October 2000 / Accepted in revised form 9 March 2001     

Collaborative integration in e-business through private trading exchanges (PTXs)

Collaborative integration is e-business process integration with supply chain partners such that there is a tightly-coupled collaborative relationship among all partners, deep visibility across all tiers of the supply chain, and near-real-time information exchange and knowledge sharing around supply chain processes. Enterprises seek to implement collaborative integration in order to realize the value-added benefits and cost savings of superior supply chain management, while enabling intelligent action and faster response to environmental variability. Increasingly, supply chain leaders are building private trading exchanges (PTXs) in order to more effectively enable and manage such collaborative integration. This paper examines the value proposition and the spectrum of solutions and services around collaborative integration through PTXs. The paper first identifies the challenges of collaborative integration in near-real-time supply chains. It then describes the building blocks of a PTX solution. It develops a comparative value proposition for collaborative integration. It then provides an illustrative case example from Viacore Inc. The paper ends with an examination of the future of collaborative integration in lean e-business environments.     

Which types of firm use collaborative innovative spaces?

Collaborative innovation spaces (CIS) can bring together multiple actors to enhance creativity, collaboration and knowledge exchange, sometimes leading to innovation. In this paper, we suggest that CIS can be categorized into three broad types (internal to the firm, external and virtual) and that each type is related to innovation processes, knowledge-sourcing and geographic context in specific ways. Our results, based on an original firm-level survey, reveal that there is heterogeneity with respect to firm attributes and innovation activities associated with different types of CIS. In particular, whilst innovation is associated with the use of CIS in general, radical and technological innovations are more strongly associated with internal CIS, whereas smaller firms tend to use virtual CIS. External CIS, whilst not associated with technological innovation, are associated with high-tech firms. CIS use does not vary across geographic context. These results emphasize the importance of in-person, internal, CIS for radical and technological innovation and the need to distinguish between different types of CIS in order to understand how each is associated with different types of innovation, knowledge-sourcing and firm.     

How to orchestrate R&D networks? The role of orchestration subprocesses and collaborative practices over time

Although R&D networks have been increasingly used by companies to foster innovation through collaboration, there are still challenges regarding how to successfully orchestrate such projects. The goal of this study is to analyse how a hub firm orchestrates an R&D network through collaborative practices. We based our study on the three orchestration subprocesses proposed by Dhanaraj and Parkhe, namely, to foster knowledge mobility, guarantee innovation appropriability, and maintain network stability. To achieve our goal, we analysed the collaborative practices in an R&D network in the semiconductor industry in France. The case consists of an R&D network among 26 companies to create a new transistor. The data include 65 interviews and 192 documents. The study contributes to the literature on network orchestration by showing that specific collaborative practices performed by a hub firm enable a fourth orchestration subprocess—network reconfiguration—which is necessary in long‐lasting and complex R&D networks and also affects the other three subprocesses. Network reconfiguration consists in disassociating and attracting new members to the R&D network to cover knowledge demands, and is performed by the hub‐firm through specific collaborative practices. The results can benefit practitioners by showing which collaborative practices may be useful for a hub firm throughout the development of an R&D network.     

Dynamic and Personalized Web Services Composition in E-Business

Building composite Web services can save significant time and cost for developing new applications and enhancing the interoperability and collaboration among E-business partners. This article proposes a framework for dynamic and personalized composition of Web services using an approach that integrates not only functional attributes of Web services, but also nonfunctional attributes such as service requirements, quality of service, and the preferences and constraints of individual service consumers. A prototype system based on the proposed framework and some implementation details are also presented.     

Innovation Culture,Collaboration with External Partners and NPD Performance

Researchers and managers have found that the use of external knowledge in the process of new product development (NPD) helps to sustain a firm's competitiveness by strengthening its innovative performance. However, little is known about why some firms use external knowledge sources for NPD in an extensive manner while others hardly ever use them. In addition, there is disagreement about which external partners significantly contribute to the innovative performance of a firm as valuable knowledge sources. Based on the resource‐based view (RBV) of the firm and Kitchell's innovation adoption model, this paper expects a firm's innovation culture to have a significant impact on its openness to external knowledge – measured in terms of its collaborative behaviour with five different external partners – and for that behaviour to influence the firm's NPD performance. A sample of 254 technology‐based firms across several industries is used to empirically test the research model with covariance‐based structural equation modeling (SEM). The findings deepen our understanding of the discrepancies between successful pioneering firms active in technology and knowledge sourcing and others being less successful.     

Valuation of mergers and acquisitions in the telecommunications industry: a study on diversification and firm size

Since the 1996 Telecommunications Act, numerous mergers and alliances (M&A) have been consummated within the telecommunications industry. These M&A involve both large and small firms in a variety of different and similar industry segments. In this industry, replete with technological uncertainty, it is useful to evaluate the impact of these activities on the market valuation of the firms involved. This study uses event analysis to examine 44 M&A events involving 89 partners in the telecommunications industry. Drawing on prior literature on diversification and firm size, the study formulates and tests hypotheses relating the impact of near and far diversification, and the size of the firm, on market valuation. The results are mostly consistent with prior work and suggest that while overall these events weight positively on market value, M&A involving near-diversification and larger firms tend to experience greater valuation effects.     

Predicting shareholder litigation on insider trading from financial text: An interpretable deep learning approach

The detrimental effects of insider trading on the financial markets and the economy are well documented. However, resource-constrained regulators face a great challenge in detecting insider trading and enforcing insider trading laws. We develop a text analytics framework that uses machine learning to predict ex-ante potentially opportunistic insider trading (using actual insider trading allegation by shareholders as the proxy) from corporate textual disclosures. Distinct from typical black-box neural network models, which have difficulty tracing a prediction back to key features, our approach combines the predictive power of deep learning with attention mechanisms to provide interpretability to the model. Further, our model utilizes representations from a business proximity network and incorporates the temporal variations of a firm’s financial disclosures. The empirical results offer new insights into insider trading and provide practical implications. Overall, we contribute to the literature by reconciling performance and interpretability in predictive analytics. Our study also informs the practice by proposing a new method for regulators to examine a large amount of text in order to monitor and predict financial misconduct.     

Open to a Select Few? Matching Partners and Knowledge Content for Open Innovation Performance

The purpose of the paper is to illuminate the costs and benefits of crossing firm boundaries in inbound open innovation (OI) by determining the relationships among partner types, knowledge content and performance. The empirical part of the study is based on a survey of OI collaborations answered by R&D managers in 415 Italian, Finnish and Swedish firms. The results show that the depth of collaboration with different partners (academic/consultants, value chain partners, competitors and firms in other industries) is positively related to innovation performance, whereas the number of different partners and size have negative effects. The main result is that the knowledge content of the collaboration moderates the performance outcomes and the negative impact of having too many different kinds of partners. This illustrates how successful firms use selective collaboration strategies characterized by linking explorative and exploitative knowledge content to specific partners, to leverage the benefits and limit the costs of knowledge boundary crossing processes.     

Exploration,exploitation and adaptive rationality: the neo-Schumpeterian perspective

Resource allocation between exploration of emerging technological possibilities and exploitation of known technological possibilities involves a delicate trade-off. We develop a model to represent this trade-off under the time-pressing situation where the firm’s existing basis of survival is constantly challenged by competitors’ innovation and imitation. We examine how the employment of an adaptive rule improves a balance between the exploration and the exploitation. Simulation experiments show that an adaptively rational decision rule, or a step-by-step exploration of unknown opportunities based on feedback on returns, is more likely to increase firm survival under diverse conditions than an all-or-nothing approach regarding the unknown opportunities. Furthermore, our study suggests that the adaptively rational rule is self-protected from too much loss, while its potential pay-off can be unbounded above.     

A Market–Driven Model for Designing Negotiation Agents

Although there are many extant agent–based systems for negotiation in e–commerce, the negotiation strategies of agents in these systems are mostly static. This article presents a model for designing negotiation agents that make adjustable rates of concession by reacting to changing market situations. To determine the amount of concession for each trading cycle, these market–driven agents are guided by four mathematical functions of eagerness, trading time, trading opportunity , and competition . Trading opportunity is determined by considering: (i) number of trading partners, (ii) spreads —differences in utilities between an agent and its trading partners, and (iii) probability of completing a deal. Competition is determined by the probability that an agent is not considered the most preferred trader by other negotiating parties. Motivated by factors such as corporate policies and resource needs, eagerness represents an agent's desire to complete a deal. Agents with different time sensitivity to deadlines employ different trading strategies by making different rates of concession at different stages of negotiation. In this article, three classes of strategies with respect to remaining trading time are discussed. Theoretical analyses show that market–driven agents are designed to make prudent and appropriate amounts of concession for a given market situation.