IDEAS home Printed from https://ideas.repec.org/a/eee/jbrese/v132y2021icp102-114.html
   My bibliography  Save this article

Propagation of online consumer perceived negativity: Quantifying the effect of supply chain underperformance on passenger car sales

Author

Listed:
  • Singh, Amit
  • Jenamani, Mamata
  • Thakkar, Jitesh J.
  • Rana, Nripendra P.

Abstract

The paper presents a text analytics framework that analyses online reviews to explore how consumer-perceived negativity corresponding to the supply chain propagates over time and how it affects car sales. In particular, the framework integrates aspect-level sentiment analysis using SentiWordNet, time-series decomposition, and bias-corrected least square dummy variable (LSDVc) – a panel data estimator. The framework facilitates the business community by providing a list of consumers’ contemporary interests in the form of frequently discussed product attributes; quantifying consumer-perceived performance of supply chain (SC) partners and comparing the competitors; and a model assessing various firms’ sales performance. The proposed framework demonstrated to the automobile supply chain using a review dataset received from a renowned car-portal in India. Our findings suggestthat consumer-voiced negativity is maximum for dealers and minimum for manufacturing and assembly related features. Firm age, GDP, and review volume significantly influence car sales whereas the sentiments corresponding to SC partners do not. The proposed research framework can help the manufacturers in inspecting their SC partners; realising consumer-cited critical car sales influencers; and accurately predicting the sales, which in turn can help them in better production planning, supply chain management, marketing, and consumer relationships.

Suggested Citation

  • Singh, Amit & Jenamani, Mamata & Thakkar, Jitesh J. & Rana, Nripendra P., 2021. "Propagation of online consumer perceived negativity: Quantifying the effect of supply chain underperformance on passenger car sales," Journal of Business Research, Elsevier, vol. 132(C), pages 102-114.
  • Handle: RePEc:eee:jbrese:v:132:y:2021:i:c:p:102-114
    DOI: 10.1016/j.jbusres.2021.04.027
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0148296321002654
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.jbusres.2021.04.027?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. King, Robert Allen & Racherla, Pradeep & Bush, Victoria D., 2014. "What We Know and Don't Know About Online Word-of-Mouth: A Review and Synthesis of the Literature," Journal of Interactive Marketing, Elsevier, vol. 28(3), pages 167-183.
    2. Suzanna El-Massah & Shereen Mostafa Bacheer & Ola Al Sayed, 2019. "Liquidity Risk in the Mena Region Banking Sector: Does Bank Type Make a Difference?," Journal of Developing Areas, Tennessee State University, College of Business, vol. 53(1), pages 147-163, January-M.
    3. Burdekin, Richard C. K & Tao, Ran, 2021. "From Shanghai to Sydney: Chinese stock market influences on Australia," Finance Research Letters, Elsevier, vol. 38(C).
    4. Kiviet, Jan F., 1995. "On bias, inconsistency, and efficiency of various estimators in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 68(1), pages 53-78, July.
    5. Dong Zhang & Chong Wu & Jiaming Liu, 2020. "Ranking products with online reviews: A novel method based on hesitant fuzzy set and sentiment word framework," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 71(3), pages 528-542, March.
    6. Erik Brynjolfsson & Yu (Jeffrey) Hu & Michael D. Smith, 2003. "Consumer Surplus in the Digital Economy: Estimating the Value of Increased Product Variety at Online Booksellers," Management Science, INFORMS, vol. 49(11), pages 1580-1596, November.
    7. Garrett P. Sonnier & Leigh McAlister & Oliver J. Rutz, 2011. "A Dynamic Model of the Effect of Online Communications on Firm Sales," Marketing Science, INFORMS, vol. 30(4), pages 702-716, July.
    8. Chunhua Wu & Hai Che & Tat Y. Chan & Xianghua Lu, 2015. "The Economic Value of Online Reviews," Marketing Science, INFORMS, vol. 34(5), pages 739-754, September.
    9. Ajaya Kumar Swain & Ray Qing Cao, 2019. "Using sentiment analysis to improve supply chain intelligence," Information Systems Frontiers, Springer, vol. 21(2), pages 469-484, April.
    10. Bruno, Giovanni S.F., 2005. "Approximating the bias of the LSDV estimator for dynamic unbalanced panel data models," Economics Letters, Elsevier, vol. 87(3), pages 361-366, June.
    11. Mike Thelwall & Kevan Buckley & Georgios Paltoglou, 2012. "Sentiment strength detection for the social web," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(1), pages 163-173, January.
    12. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    13. Giovanni S. F. Bruno, 2005. "Estimation and inference in dynamic unbalanced panel-data models with a small number of individuals," Stata Journal, StataCorp LP, vol. 5(4), pages 473-500, December.
    14. Singh, Akshit & Shukla, Nagesh & Mishra, Nishikant, 2018. "Social media data analytics to improve supply chain management in food industries," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 398-415.
    15. Li, Yiming & Li, Gang & Tayi, Giri Kumar & Cheng, T.C.E., 2019. "Omni-channel retailing: Do offline retailers benefit from online reviews?," International Journal of Production Economics, Elsevier, vol. 218(C), pages 43-61.
    16. Xu, Xun & Lee, Chieh, 2020. "Utilizing the platform economy effect through EWOM: Does the platform matter?," International Journal of Production Economics, Elsevier, vol. 227(C).
    17. Raymond Yiu Keung Lau & Wenping Zhang & Wei Xu, 2018. "Parallel Aspect‐Oriented Sentiment Analysis for Sales Forecasting with Big Data," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1775-1794, October.
    18. Fan, Zhi-Ping & Che, Yu-Jie & Chen, Zhen-Yu, 2017. "Product sales forecasting using online reviews and historical sales data: A method combining the Bass model and sentiment analysis," Journal of Business Research, Elsevier, vol. 74(C), pages 90-100.
    19. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    20. Mike Thelwall & Kevan Buckley & Georgios Paltoglou, 2012. "Sentiment strength detection for the social web," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(1), pages 163-173, January.
    21. Pradeep K. Chintagunta & Shyam Gopinath & Sriram Venkataraman, 2010. "The Effects of Online User Reviews on Movie Box Office Performance: Accounting for Sequential Rollout and Aggregation Across Local Markets," Marketing Science, INFORMS, vol. 29(5), pages 944-957, 09-10.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Singh, Amit & Jenamani, Mamata & Thakkar, Jitesh J. & Rana, Nripendra P., 2022. "Quantifying the effect of eWOM embedded consumer perceptions on sales: An integrated aspect-level sentiment analysis and panel data modeling approach," Journal of Business Research, Elsevier, vol. 138(C), pages 52-64.
    2. Xiaoguang Wang & Tao Lv & Lei Fan, 2022. "New Energy Vehicle Consumer Demand Mining Research Based on Fusion Topic Model: A Case in China," Sustainability, MDPI, vol. 14(6), pages 1-13, March.
    3. Lu, Lin & Xu, Pei & Wang, Yen-Yao & Wang, Yu, 2023. "Measuring service quality with text analytics: Considering both importance and performance of consumer opinions on social and non-social online platforms," Journal of Business Research, Elsevier, vol. 169(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Singh, Amit & Jenamani, Mamata & Thakkar, Jitesh J. & Rana, Nripendra P., 2022. "Quantifying the effect of eWOM embedded consumer perceptions on sales: An integrated aspect-level sentiment analysis and panel data modeling approach," Journal of Business Research, Elsevier, vol. 138(C), pages 52-64.
    2. Eicher, Theo S. & Schreiber, Till, 2010. "Structural policies and growth: Time series evidence from a natural experiment," Journal of Development Economics, Elsevier, vol. 91(1), pages 169-179, January.
    3. Rashid Sbia & Helmi Hamdi, 2020. "Remittances and inflation in OPEC countries:Evidence from bias-corrected least-squares dummy variable (CLSDV) estimator," Economics Bulletin, AccessEcon, vol. 40(3), pages 2471-2483.
    4. Mahir Binici & Yin-Wong Cheung & Kon S. Lai, 2011. "Trade Openness, Market Competition, and Inflation: Some Sectoral Evidence from OECD Countries," CESifo Working Paper Series 3690, CESifo.
    5. Akhilesh K. Verma & Rajeswari Sengupta, 2021. "Interlinkages between external debt financing, credit cycles and output fluctuations in emerging market economies," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 157(4), pages 965-1001, November.
    6. Gehringer, Agnieszka & Prettner, Klaus, 2019. "Longevity And Technological Change," Macroeconomic Dynamics, Cambridge University Press, vol. 23(4), pages 1471-1503, June.
    7. Dosi, G. & Piva, M. & Virgillito, M.E. & Vivarelli, M., 2021. "Embodied and disembodied technological change: The sectoral patterns of job-creation and job-destruction," Research Policy, Elsevier, vol. 50(4).
    8. Armey, Laura E. & McNab, Robert M., 2018. "Expenditure decentralization and natural resources," The Quarterly Review of Economics and Finance, Elsevier, vol. 70(C), pages 52-61.
    9. Mariacristina Piva & Marco Vivarelli, 2017. "The employment impact of R&D expenditures and capital formation," DISCE - Quaderni del Dipartimento di Politica Economica ispe0078, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    10. Mariacristina Piva & Marco Vivarelli, 2018. "Is Innovation Destroying Jobs? Firm-Level Evidence from the EU," Sustainability, MDPI, vol. 10(4), pages 1-16, April.
    11. Christian Almer & Timo Goeschl, 2011. "The political economy of the environmental criminal justice system: a production function approach," Public Choice, Springer, vol. 148(3), pages 611-630, September.
    12. Francesco Bogliacino & Mariacristina Piva & Marco Vivarelli, 2011. "The impact of R&D on employment in Europe: A firm-level analysis," DISCE - Quaderni del Dipartimento di Scienze Economiche e Sociali dises1176, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    13. Mariacristina Piva & Marco Vivarelli, 2018. "Technological change and employment: is Europe ready for the challenge?," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 8(1), pages 13-32, March.
    14. Opolska, Iweta, 2017. "The efficacy of liberalization and privatization in introducing competition into European natural gas markets," Utilities Policy, Elsevier, vol. 48(C), pages 12-21.
    15. Roberto Dell'Anno & Adalgiso Amendola, 2015. "Social Exclusion and Economic Growth: An Empirical Investigation in European Economies," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 61(2), pages 274-301, June.
    16. Daude, Christian & Levy Yeyati, Eduardo & Nagengast, Arne J., 2016. "On the effectiveness of exchange rate interventions in emerging markets," Journal of International Money and Finance, Elsevier, vol. 64(C), pages 239-261.
    17. Giovanni Dosi & Mariacristina Piva & Maria Enrica Virgillito & Marco Vivarelli, 2019. "Technology and employment in a vertically connected economy: a model and an empirical test," DISCE - Quaderni del Dipartimento di Politica Economica dipe0005, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    18. Jochimsen, Beate & Thomasius, Sebastian, 2014. "The perfect finance minister: Whom to appoint as finance minister to balance the budget," European Journal of Political Economy, Elsevier, vol. 34(C), pages 390-408.
    19. Bogliacino, Francesco & Piva, Mariacristina & Vivarelli, Marco, 2012. "R&D and employment: An application of the LSDVC estimator using European microdata," Economics Letters, Elsevier, vol. 116(1), pages 56-59.
    20. Capelle-Blancard, Gunther & Crifo, Patricia & Diaye, Marc-Arthur & Oueghlissi, Rim & Scholtens, Bert, 2019. "Sovereign bond yield spreads and sustainability: An empirical analysis of OECD countries," Journal of Banking & Finance, Elsevier, vol. 98(C), pages 156-169.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jbrese:v:132:y:2021:i:c:p:102-114. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/jbusres .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.