Measuring intellectual capital in a firm belonging to a strategic alliance

Veltri, Stefania; Venturelli, Andrea; Mastroleo, Giovanni

doi:10.1108/jic-06-2014-0069

Cited by 17 publications

(17 citation statements)

References 85 publications

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“…Then, the mean of the three measurement criteria was calculated. To calculate the correctness of each question, the following formula was used according to Haladyna ():

Correctness = ((), correct responses ⁄ all correct options) - ((), wrong responses ⁄ all options) .

To calculate the completeness of knowledge, the following formula was used according to Bose (), Liebowitz and Suen (), Naumann and Rolker (), and Veltri et al ():

Completeness = correct responses ⁄ all correct options .

To calculate the understandability of knowledge, the following formula was used based on the mentioned definition in Section 2.3 (Naumann & Rolker, ):

Understandability = ((), Correctness + Completeness) ⁄ 2 .

…”

Section: Methodsmentioning

confidence: 99%

“…Completeness for requirements specifications is similarly characterized as including all real‐life situations (Jani, ). On this basis, completeness of requirements knowledge in this study is defined as the ratio of correct responses to all correct options (Bose, ; Liebowitz & Suen, ; Naumann & Rolker, ; Veltri, Venturelli, & Mastroleo, ). Correctness is characterized as being accurate and precise (Jani, ).…”

Section: Existing Studiesmentioning

confidence: 99%

“…To calculate the completeness of knowledge, the following formula was used according to Bose (2004), Liebowitz and Suen (2000), Naumann and Rolker (2000), and Veltri et al (2015):…”

Section: Knowledge Audit Model For Requirements Elicitation Processmentioning

confidence: 99%

See 2 more Smart Citations

A knowledge audit model for requirement elicitation: A case study to assess knowledge in requirement elicitation

Taheri

Shafazand

et al. 2017

Knowl Process Manag

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This paper aims to develop a knowledge audit (KA) model with the focus on knowledge assessment in the requirements elicitation process (REP) to allay the problems of REP regarding knowledge communication. The principal problems with REP are knowledge conflict and the failure to mention a variety of knowledge and requirements changes. Despite of many existing studies relating to KA, inadequate effort has been directed towards investigating the full part played by the KA process in REP. The purpose of this paper is to bridge this gap using a software prototype that uses the KA model in the REP. This study proposes a KA model using an iterative triangulation method. The proposed model is validated through a case study by using a software prototype developed based on the proposed KA model to see if this KA model is effective for software developers in REP by improving the completeness, correctness, and understandability of the elicited requirements knowledge. Research findings are based on responses of 40 respondents from software development organizations. The results of case study confirmed the effectiveness of KA model for REP with respect to completeness, correctness, and understandability. This research answers the call to assess knowledge in REP by developing a KA model and prototype to fill the existing gap in this area. Overall, a KA model for REP is introduced and validated to identify and assess knowledge that supports knowledge communication in REP.

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“…Then, the mean of the three measurement criteria was calculated. To calculate the correctness of each question, the following formula was used according to Haladyna ():

Correctness = ((), correct responses ⁄ all correct options) - ((), wrong responses ⁄ all options) .

To calculate the completeness of knowledge, the following formula was used according to Bose (), Liebowitz and Suen (), Naumann and Rolker (), and Veltri et al ():

Completeness = correct responses ⁄ all correct options .

To calculate the understandability of knowledge, the following formula was used based on the mentioned definition in Section 2.3 (Naumann & Rolker, ):

Understandability = ((), Correctness + Completeness) ⁄ 2 .

…”

Section: Methodsmentioning

confidence: 99%

Section: Existing Studiesmentioning

confidence: 99%

See 1 more Smart Citation

A knowledge audit model for requirement elicitation: A case study to assess knowledge in requirement elicitation

Taheri

Shafazand

et al. 2017

Knowl Process Manag

View full text Add to dashboard Cite

show abstract

“…Learning can explain both a firm's competitive advantage and its scope. Mody (1993) and Veltri et al, (2015) argue that learning is a strong motive for forming and sustaining alliances. Other authors, including Powell and Brantley (1992), Khanna (1996), and Wuyts and Dutta (2014) also suggest that one of the strongest motives for collaboration is the acquisition of new technical skills or technological capabilities from partner firms.…”

Section: The Learning With Flexibility Explanationmentioning

confidence: 99%

Strategic Alliances in Knowledge-Intensive Industries: An Integrated View

Han

Liu

2018

jabmi

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In this study, we examine strategic alliances between large pharmaceutical companies (LPCs) and new biotechnology firms (NBFs) in the knowledge-intensive biopharmaceutical industry in the light of two explanations for the motives behind R&D alliances, namely, the transaction cost explanation and the learning with flexibility explanation. We develop a series of propositions based on an integrated view of the motives behind strategic alliances in knowledge-based industries.

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“…Bontis (1998), mencoba untuk memberikan penjelasan mengenai konsep IC secara teoritis, Pulic (1998) ;Veltri, Venturelli & Mastroleo (2015), mencoba memberikan sebuah metode untuk mengukur IC secara lebih mudah dan dapat dipahami oleh berbagai pihak. El-Bannany (2008); Saleh, Rahman, & Hassan (2009) ;Swartz & Firer (2005); Schiavone et al (2014) mencoba untuk mendeskripsikan faktor-faktor apa saja yang mempengaruhi kinerja IC.…”

Section: Pendahuluanunclassified