Big data (BD) is making advances in promoting sustainable consumption behaviour and has attracted the attention of researchers worldwide. Despite the increased focus, the findings of studies on this topic are fragmented, and future researchers need a systematic understanding of the existing literature for identification of the research scope. This study offers a systematic review of the role of BD in promoting sustainable-consumption behaviour with the help of a bibliometric analysis, followed by a thematic analysis. The findings suggest that businesses deploy BD to create sustainable consumer experiences, predict consumer buying patterns, design and alter business models and create nudges for sustainable consumption, while consumers are forcing businesses to develop green operations and supply chains to reduce the latter’s carbon footprint. The major research gaps for future researchers are in the following areas: the impact of big data analytics (BDA) on consumerism, the role of BD in the formation of sustainable habits and consumer knowledge creation for sustainable consumption and prediction of green consumer behaviour.
AbbasiA., SarkerS., & ChiangR. H. (2016). Big data research in information systems: Toward an inclusive research agenda. Journal of the Association for Information Systems, 17(2), 3.
2.
AhmedA. K., SenthilkumarC. B., & NallusamyS. (2018). Study on environmental impact through analysis of big data for sustainable and green supply chain management. International Journal of Mechanical and Production Engineering Research and Development, 8, 1245–1254.
3.
AlfianG., IjazM. F., SyafrudinM., SyaekhoniM. A., FitriyaniN. L., & RheeJ. (2019). Customer behaviour analysis using real-time data processing. Asia Pacific Journal of Marketing and Logistics, 31(1), 265–290.
4.
Al-RahmiW. M., YahayaN., AldraiweeshA. A., AlturkiU., AlamriM. M., SaudM. S. B., & AlhamedO. A. (2019). Big data adoption and knowledge management sharing: An empirical investigation on their adoption and sustainability as a purpose of education. IEEE Access, 7, 47245–47258.
5.
AriaM., & CuccurulloC. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975.
6.
AntonidesG. (2017). Sustainable consumer behaviour: A collection of empirical studies. Sustainability, 9(10), 1686.
7.
BanH. J., & KimH. S. (2019). Understanding customer experience and satisfaction through airline passengers’ online review. Sustainability, 11(15), 4066.
8.
BedingfieldS., AlahakoonD., GenegederaH., & ChilamkurtiN. (2018). Multi-granular electricity consumer load profiling for smart homes using a scalable big data algorithm. Sustainable Cities and Society, 40, 611–624.
9.
BentleyR. A., & O’BrienM. J. (2015). Collective behaviour, uncertainty and environmental change. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 373(2055), 20140461.
10.
BressanelliG., AdrodegariF., PeronaM., & SaccaniN. (2018). Exploring how usage-focused business models enable circular economy through digital technologies. Sustainability, 10(3), 639.
11.
CambraU. C., GonzálezJ. I. N., MartínezL. M., & BielsaC. D. (2019). Nuevas herramientas de información en campañas de concienciación social. El “Big Data” aplicado a la Sequía y Salud Pública. Estudios sobre el Mensaje Periodistico, 25(2), 767.
12.
CanU., & AlatasB. (2017). Big social network data and sustainable economic development. Sustainability, 9(11), 2027.
13.
CornelissenG., PandelaereM., WarlopL., & DewitteS. (2008). Positive cueing: Promoting sustainable consumer behavior by cueing common environmental behaviors as environmental. International Journal of Research in Marketing, 25(1), 46–55.
14.
ElhoushyS., & LanziniP. (2020). Factors affecting sustainable consumer behavior in the MENA region: A systematic review. Journal of International Consumer Marketing, 33(5), 1–24.
15.
EncaladaL., Boavida-PortugalI., Cardoso FerreiraC., & RochaJ. (2017). Identifying tourist places of interest based on digital imprints: Towards a sustainable smart city. Sustainability, 9(12), 2317.
16.
EtzionD., & Aragon-CorreaJ. A. (2016). Big data, management, and sustainability: Strategic opportunities ahead. Organization & Environment, 29(2), 147–155.
17.
FahimniaB., SarkisJ., & DavarzaniH. (2015). Green supply chain management: A review and bibliometric analysis. International Journal of Production Economics, 162, 101–114.
18.
FengL., SunB., WangK., & TsaiS. B. (2018). An empirical study on the design of digital content products from a big data perspective. Sustainability, 10(9), 3092.
19.
GanM., YangS., LiD., WangM., ChenS., XieR., & LiuJ. (2018). A novel intensive distribution logistics network design and profit allocation problem considering sharing economy. Complexity, 2018, 4678358.
20.
GandomiA., & HaiderM. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144.
21.
GhazalehM. A., & ZabadiA. M. (2020). Promoting a revamped CRM through Internet of Things and Big Data: an AHP-based evaluation. International Journal of Organizational Analysis, 28(1), 66–91.
22.
GibbsG. R. (2007). Thematic coding and categorizing. Analyzing Qualitative Data, 703, 38–56.
23.
GlavičP., & LukmanR. (2007). Review of sustainability terms and their definitions. Journal of Cleaner Production, 15(18), 1875–1885.
24.
Goličnik MarušićB., & Praper GuličS. (2018). Development of a user-centered module: A contribution to flood-sustainable spatial planning. Urbani izziv, 29(2), 111–125.
25.
GuoZ., ZhouK., ZhangC., LuX., ChenW., & YangS. (2018). Residential electricity consumption behaviour: Influencing factors, related theories and intervention strategies. Renewable and Sustainable Energy Reviews, 81, 399–412.
26.
HolmlundM., Van VaerenberghY., CiuchitaR., RavaldA., SarantopoulosP., OrdenesF. V., & ZakiM. (2020). Customer experience management in the age of big data analytics: A strategic framework. Journal of Business Research, 116, 356–365.
27.
HostaM., & ZabkarV. (2020). Antecedents of environmentally and socially responsible sustainable consumer behavior. Journal of Business Ethics, 1–21. https://link.springer.com/article/10.1007/s10551-019-04416-0
28.
HeiskanenE., & PantzarM. (1997). Toward sustainable consumption: Two new perspectives. Journal of Consumer Policy, 20(4), 409–442.
29.
HungJ. L., HeW., & ShenJ. (2019). Big data analytics for supply chain relationship in banking. Industrial Marketing Management, 86, 144–153.
30.
JainG., RakeshS., NabiM. K., & ChaturvediK. R. (2018). Hyper-personalization–fashion sustainability through digital clienteling. Research Journal of Textile and Apparel, 22(4), 320–334.
31.
JiaQ., WeiL., & LiX. (2019). Visualizing sustainability research in business and management (1990–2019) and emerging topics: A large-scale bibliometric analysis. Sustainability, 11(20), 5596.
32.
JohnstonP., EverardM., SantilloD., & RobèrtK. H. (2007). Reclaiming the definition of sustainability. Environmental Science and Pollution Research International, 14(1), 60–66.
33.
KambatlaK., KolliasG., KumarV., & GramaA. (2014). Trends in big data analytics. Journal of Parallel and Distributed Computing, 74(7), 2561–2573.
34.
KauffmannE., PeralJ., GilD., FerrándezA., SellersR., & MoraH. (2019). Managing marketing decision-making with sentiment analysis: An evaluation of the main product features using text data mining. Sustainability, 11(15), 4235.
35.
KitchensB., DobolyiD., LiJ., & AbbasiA. (2018). Advanced customer analytics: Strategic value through integration of relationship-oriented big data. Journal of Management Information Systems, 35(2), 540–574.
KrämerA., FriesenM., & SheltonT. (2018). Are airline passengers ready for personalized dynamic pricing? A study of German consumers. Journal of Revenue and Pricing Management, 17(2), 115–120.
38.
KunzW., AksoyL., BartY., HeinonenK., KabadayiS., OrdenesF. V., SigalaM., DiazD., & TheodoulidisB. (2017). Customer engagement in a big data world. Journal of Services Marketing, 31(2), 161–171.
39.
LaValleS., LesserE., ShockleyR., HopkinsM. S., & KruschwitzN. (2011). Big data, analytics and the path from insights to value. MIT Sloan Management Review, 52(2), 21–32.
40.
LeT. M., & LiawS. Y. (2017). Effects of pros and cons of applying big data analytics to consumers’ responses in an e-commerce context. Sustainability, 9(5), 798.
41.
Leonard-BartonD. (1981). Voluntary simplicity lifestyles and energy conservation. Journal of Consumer Research, 8(3), 243–252.
42.
LiL., LiuY., & SongY. (2019). Factors affecting bike-sharing behaviour in Beijing: price, traffic congestion, and supply chain. Annals of Operations Research, 1–16. https://link.springer.com/article/10.1007/s10479-019-03293-0
43.
LiuW. K., & YenC. C. (2016). Optimizing bus passenger complaint service through big data analysis: Systematized analysis for improved public sector management. Sustainability, 8(12), 1319.
44.
MaD., & HuJ. (2020). Research on collaborative management strategies of closed-loop supply chain under the influence of Big-Data Marketing and reference price effect. Sustainability, 12(4), 1685.
45.
MilfontT. L., & MarkowitzE. (2016). Sustainable consumer behavior: A multilevel perspective. Current Opinion in Psychology, 10, 112–117.
46.
MarrB. (2015). Big Data: 20 mind-boggling facts everyone must read. Forbes Magazine. https://www.forbes.com/sites/bernardmarr/2015/09/30/big-data-20-mind-boggling-facts-everyone-must-read/?sh=162909c117b1
47.
MartiP., MegensC., & HummelsC. (2016). Data-enabled design for social change: Two case studies. Future Internet, 8(4), 46.
48.
MishraN., SinghA., RanaN. P., & DwivediY. K. (2017). Interpretive structural modelling and fuzzy MICMAC approaches for customer centric beef supply chain: Application of a big data technique. Production Planning & Control, 28(11–12), 945–963.
49.
MooreJ. E., MascarenhasA., BainJ., & StrausS. E. (2017). Developing a comprehensive definition of sustainability. Implementation Science, 12(1), 110.
50.
MorG., VilaplanaJ., DanovS., CiprianoJ., SolsonaF., & ChemisanaD. (2018). EMPOWERING, a smart Big Data framework for sustainable electricity suppliers. IEEE Access, 6, 71132–71142.
51.
NilashiM., AhaniA., EsfahaniM. D., YadegaridehkordiE., SamadS., IbrahimO., SharefN. M., & AkbariE. (2019). Preference learning for eco-friendly hotels recommendation: A multi-criteria collaborative filtering approach. Journal of Cleaner Production, 215, 767–783.
52.
NiederbergerA. A., & ChampnissG. (2018). Flip sides of the same coin? A simple efficiency score versus energy bill savings information to drive consumers to choose more energy-efficient products. Energy Efficiency, 11(7), 1657–1671.
53.
NolanS., & DaneA. (2018). A sharper conversation: book publishers’ use of social media marketing in the age of the algorithm. Media International Australia, 168(1), 153–166.
54.
OhJ., ConnertonT. P., & KimH. J. (2019). The rediscovery of brand experience dimensions with Big Data analysis: Building for a sustainable brand. Sustainability, 11(19), 5438.
55.
OpreaS. V., & BâraA. (2020). Setting the time-of-use tariff rates with NoSQL and machine learning to a sustainable environment. IEEE Access, 8, 25521–25530.
56.
Periañez-CristobalR., LuengoP., & Calvo-MoraA. (2020). The heritage character and service quality in urban defensive structures renovated as hotels. Journal of Heritage Tourism, 15(1), 44–59.
57.
PosladS., MaA., WangZ., & MeiH. (2015). Using a smart city IoT to incentivise and target shifts in mobility behaviour—Is it a piece of pie?Sensors, 15(6), 13069–13096.
58.
Ramos-RodríguezA. R., & Ruíz-NavarroJ. (2004). Changes in the intellectual structure of strategic management research: A bibliometric. Management Journal, 25(10), 981–1004.
59.
RowleyJ., & SlackF. (2004). Conducting a literature review. Management Research News, 27(6), 31–39.
60.
RussomP. (2011). TDWI best practices report, fourth quarter. Big Data Analytics, 19(4), 1–34.
61.
SantilloD. (2007). Reclaiming the definition of sustainability (7 pp). Environmental Science and Pollution Research International, 14(1), 60.
62.
SchiebelJ., OmraniH., & GerberP. (2015). Border effects on the travel mode choice of resident and cross-border workers in Luxembourg. European Journal of Transport and Infrastructure Research, 15(4), 570–596.
63.
SchmittB. (2011). Experience marketing: Concepts, frameworks and consumer insights. Now Publishers Inc.
64.
ShafiqA., AhmedM. U., & MahmoodiF. (2019). Impact of supply chain analytics and customer pressure for ethical conduct on socially responsible practices and performance: An exploratory study. International Journal of Production Economics, 107571.
65.
ShettyV., YamamotoJ., & YaleK. (2018). Re-architecting oral healthcare for the 21st century. Journal of Dentistry, 74, S10–S14.
66.
SongM., CenL., ZhengZ., FisherR., LiangX., WangY., & HuisinghD. (2017). How would big data support societal development and environmental sustainability? Insights and practices. Journal of Cleaner Production, 142, 489–500.
67.
ThalerR. H., & SunsteinC. R. (2009). Nudge: Improving decisions about health, wealth, and happiness. Penguin.
68.
TrabucchiD., BuganzaT., & PellizzoniE. (2017). Give away your digital services: Leveraging Big Data to Capture Value New models that capture the value embedded in the data generated by digital services may make it viable for companies to offer those services for free. Research-Technology Management, 60(2), 43–52.
UllahA., AiminW., & AhmedM. (2018). Smart automation, customer experience and customer engagement in electric vehicles. Sustainability, 10(5), 1350.
71.
ValatinG., MoseleyD., & DandyN. (2016). Insights from behavioural economics for forest economics and environmental policy: Potential nudges to encourage woodland creation for climate change mitigation and adaptation?Forest Policy and Economics, 72, 27–36.
72.
VermaN., & SinghJ. (2017). An intelligent approach to big data analytics for sustainable retail environment using Apriori-MapReduce framework. Industrial Management & Data Systems. https://doi.org/10.1108/IMDS-09-2016-0367
73.
VojtovičS., NavickasV., & GruzauskasV. (2016). Strategy of sustainable competitiveness: methodology of real-time customers’ segmentation for retail shops. Journal of Security & Sustainability Issues, 5(4), 489–499.
74.
WangB., WangX., GuoD., ZhangB., & WangZ. (2018). Analysis of factors influencing residents’ habitual energy-saving behaviour based on NAM and TPB models: Egoism or altruism?Energy Policy, 116, 68–77.
75.
WangZ., XueM., WangY., SongM., LiS., DazianoR. A., WangB., MaG., ChenK., LiX., & ZhangB. (2019). Big data: New tend to sustainable consumption research. Journal of Cleaner Production, 236, 117499.
76.
WhiteK., & SimpsonB. (2013). When do (and don’t) normative appeals influence sustainable consumer behaviors?Journal of Marketing, 77(2), 78–95.
77.
WhiteK., HabibR., & HardistyD. J. (2019). How to SHIFT consumer behaviors to be more sustainable: A literature review and guiding framework. Journal of Marketing, 83(3), 22–49.
78.
YeY., LuX., & LuT. (2020). Examining the spillover effect of sustainable consumption on microloan repayment: A big data-based research. Information & Management, 103288.
79.
Yong-HakJ. (2013). Web of science. Thomson Reuters.
