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Social Media Big Data Analytics for Demand Forecasting: Development and Case Implementation of an Innovative Framework

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Social media big data offers insights that can be used to make predictions of products' future demand and add value to the supply chain performance. The paper presents a framework for improvement of demand forecasting in a supply chain using social media data from Twitter and Facebook. The proposed framework uses sentiment, trend, and word analysis results from social media big data in an extended Bass emotion model along with predictive modelling on historical sales data to predict product demand. The forecasting framework is validated through a case study in a retail supply chain. It is concluded that the proposed framework for forecasting has a positive effect on improving accuracy of demand forecasting in a supply chain.
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DOI: 10.4018/JGIM.2020010106
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Rehan Iftikhar, Maynooth University, Maynooth, Ireland
Mohammad Saud Khan, Victoria University of Wellington, New Zealand

Social media big data offers insights that can be used to make predictions of products’ future demand
and add value to the supply chain performance. The paper presents a framework for improvement
of demand forecasting in a supply chain using social media data from Twitter and Facebook. The
proposed framework uses sentiment, trend, and word analysis results from social media big data in
an extended Bass emotion model along with predictive modelling on historical sales data to predict
product demand. The forecasting framework is validated through a case study in a retail supply
chain. It is concluded that the proposed framework for forecasting has a positive effect on improving
accuracy of demand forecasting in a supply chain.

Apparel Supply Chain, Bass Emotion Model, Big Data, Demand Forecasting, Emotion Enhanced Model,
Sentiment Analysis, Social Media, Supply Chain Management
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Big data represents a tremendous opportunity for companies, as it can help to make better decisions
in an operational, tactical and strategic level (Schroeck, Shockley, Smart, Romero-Morales, & Tufano,
2012), with direct impact on business profitability (Waller & Fawcett, 2013). The ability to draw
insights from different types of data creates huge value for a firm (Dijcks, 2013; Kiron & Shockley,
2015). Big data presents a far greater opportunity than what is being utilized. Only 0.5% of big data
is being utilized and analysed while there is potential for so much more (Guess, 2015). Bearing in
mind this huge potential, literature providing empirical evidence of the business value added by big
data analytics in a supply chain remains little and even poor (Wamba, 2017).
All supply chain operations and activities are set in motion by the final customers’ demand
(Syntetos et al., 2016). Demand forecasting is used as a basis to make supply chain strategy (Marshall,
Dockendorff, & Ibáñez, 2013) and forecasting weaknesses is one of the main reasons for supply chain
failures (Zadeh, Sepehri, & Farvaresh, 2014). Demand Forecasting can be improved significantly by
using big data (Chao, 2015), especially the big data from social media (Arias, Arratia, & Xuriguera,
2014). With an increase in social media activity, there has been an emergence of academic and
industrial research that taps into these social media data sources. However, the utilization of these
data sources remain at an early stage and outcomes are often mixed (Yu, Duan, & Cao, 2013).
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Companies face a challenge in forecasting with regards to analysing their historical data in the
same breath as big data from social media (Papanagnou & Matthews-Amune, 2017). There has been an
increased focus from supply chain practitioners to leverage effects from unstructured big data such as
social media data, but there is very little support in terms of empirical evidence (Syntetos et al., 2016).
Integration of social media analytics and supply chain management is needed to comprehensively
establish ‘what can be actually done’ in the field of forecasting with the help of analytics. There
is a paucity of predictive frameworks for forecasting using social media big data. This paper aims
to bridge the gap between traditional forecasting techniques and big data analytics utilization and
contributes towards a forecasting platform using social media big data as well as historical sales data.
This work presents a framework to utilize social media big data in Bass-Emotion Model introduced
by Fan, Che, & Chen (2017). The proposed framework uses the results of sentiment analysis on
Facebook and Twitter for demand forecasting. This work provides empirical evidence on the usage
of social media big data for demand forecasting in supply chain management (Choi, 2018; Schaer,
Kourentzes, & Fildes, 2018). It is one of the first studies that incorporates word analysis, topic
modelling and sentiment analysis to provide social media data parameters to the Bass- Emotion model.
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Diverse, massive and complex data on different domains of business and technology which cannot
be efficiently addressed by the traditional technologies, skills, and infrastructure is referred to as
big data. Most big data researchers and practitioners in general agree on three dimensions that
characterize big data: volume, velocity and variety (Zikopoulos & Eaton, 2011). Big data analytics in
supply chain management can be described as applying analytical techniques on big data to facilitate
optimization and decision making in a supply chain (Souza, 2014). The use of big data analytics can
help us understand ‘what has happened, what is happening at the moment, what will happen and why
things happen’ (Feki & Wamba, 2016 p.1127). Three distinct analytics approaches for answering
these questions have been classified as descriptive, predictive, and prescriptive analytics (Hahn &
Packowski, 2015). The most valued use of big data analytics in a supply chain is the ability it provides
to analysts in predicting a reaction or an event by detecting changes based on current or historical
data (Sanders, 2014). The utilization of current data, is very effective in improving a supply chain
which is seeing a start in its use now in industry. Amazon has patented Anticipatory Shipping’ which
predicts based on an analysis of previous orders and other factors such as customersshopping trend to
anticipate that when and by whom a certain product will be bought and ship it in advance and deliver
it instantly after the order has been placed (Kopalle, 2014). Another example is that of DHL. DHL is
implementing big data analytics to re-route their vehicles and re-define the delivery/picking sequence
to save significant time; additionally, DHL has also developed ‘MyWays’: a crowd-based platform
that assigns the parcels to daily commuters, students and taxi drivers by their geo-location and usual
routes which in turn improves the efficiency of the last-mile delivery (Jeske, Grüner, & WeiB, 2013).
Most important aspect which hinders maximum utilization of big data is the lack of analytical
techniques and applications which could be used to convert the unstructured data from various sources
to business intelligence for the user (Sanders, 2014). This calls for more practical applications and
techniques to be introduced which use big data analytics for improving decision making in supply
chain management. To cater for this call, this paper introduces a framework which utilizes social
media big data to update the demand forecast while also using information from the related product’s
sale. The proposed framework will generate direct implications to supply chain practitioners who are
keen to utilize customers’ opinions for improving their demand forecasting.
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Social Media is defined as “ a conversational, distributed mode of content generation, dissemination,
and communication among communities” (Zeng et al., 2010 p. 13). Social Media is an effective
sensor when it comes to receiving signals from potential customers. Social media data contains
emotions, opinions, and preferences which makes it potentially useful as a market sensing platform
but with social media data being qualitative, unstructured and subjective form of big data, it calls
for a different analytics approach from traditional approach used in big data (Wong, Chan, & Lacka,
2017). Descriptive analytics, network analytics and content analytics have been identified as three
major type of analytics which can be used to create value from social media data (Chae, 2015). As
the concern of this study is analysis of the text on Twitter and Facebook, content analytics will be
used. Three main dimensions have been identified in the content analytics domain through which
social media data can be used to create value for a supply chain forecasting in the proposed framework
which are sentiment analysis, word analysis and topic modelling.
Sentiment Analysis
Analysing people’s opinion, sentiment, evaluation, attitude, judgment and emotions towards tangible
or intangible objects, issues or attributes, such as, product, service, organizations, individuals, events,
topics is known as Sentiment Analysis (Liu, 2012). Twitter and Facebook are a very tempting source
for sentiment analysis due to the variety, velocity and volume (3vs of big data) of the available content.
But informal style of posts and tweets, length of tweets, the resulting use of special symbols in posts
makes it challenging to extract high performance result from analysis on these sources. Appraisal
theory (Scherer, 2005) describes a way to extract sentiment from text. Arnold and Plutchik (1964)
introduced the basic concept of the theory. The theory lays basis for structured sentiment extraction
that is based on appraisal expression, a basic grammatical unit by which an opinion is expressed .
Korenek and Šimko (2014) utilized appraisal theory to analyse microblogs using sentiment analysis
and categorize sentiments as positive, negative and neutral. The sentiments have been categorized
in the proposed framework utilizing concepts from appraisal theory. Various organizations from
different sectors have used sentiment analysis for gathering information, predicting market response,
election results, product innovation, improving customer service, stock forecasting and supply
chain management as shown in Table 1. Machine learning, lexicon based, statistical and rule based
approaches are the most widely used methods for sentiment analysis (Medhat et al., 2014) but n-gram
analysis and artificial neural networks methods have also been used (Ghiassi, Skinner, & Zimbra,
2013). Fan et al. (2017) used Naïve Bayes (NB) algorithm for sentiment analysis on online reviews
for use in product forecasting. NB algorithm is better suited to classifications where text is treated
independently. Cui et al. (2017) used Support Vector Machine (SVM) for classifying text from social
media for event detection. In the proposed framework, both NB and SVM algorithm are used but
different from all it is being applied on social media data from Twitter and Facebook and is used in
conjunction with trend and word analysis results.
Topic Modelling
Social media sources provide huge amount of information every day and with proper tools an
understanding of the trends of that information for actionable insights can be developed. Topic
Modelling is typically used to uncover industry data across a certain topic or domain (Kwak, Lee,
Park, & Moon, 2010), such as product demands, consumer insights, and service quality of an industry.
It can help business managers or decision makers to predict the future behaviours or trends of a
community based on a relevant set of data. Lansley and Longley (2016) demonstrates a way to use
Twitter information to analyse and present geographical trends using Latent Dirichlet Allocation
(LDA). Blei, Ng and Jordan (2003) describes LDA as an unsupervised model which is used to find
possible topics from collections of text.
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Word Analysis
Word analysis of social media data encompasses term frequency analysis, word cloud formation and
clustering (Chae, 2015). Term frequency is used to identify key words and phrases from the dataset
by use of algorithms such as n-gram. N-gram combines adjacent words of length ‘n’ from the given
dataset to capture the language structure from statistical point of view. Word cloud is a visually
appealing method to get an overview of the text (Heimerl et al., 2014). Word analysis have been used
frequently in literature for text summarization (Kuo, Hentrich, Good, & Wilkinson, 2007), opinion
mining (Wu et al., 2010) and text visualization (Stasko, Görg, Liu, & Singhal, 2007), patent analysis
(Koch et al., 2011) and investigative analysis (Stasko et al., 2007). In the proposed framework, word
analysis is used to get an overview of the text being used for the selected keywords and to identify
related words to add to the search.
Table 1. Studies based on sentiment analysis
Research Topic Previous work with description
Stock Forecasting Arias et al. (2013) and Bollen et al. (2011) have used
social media analytics for stock forecasting using twitter
information.
Srivastava et al. (2016) and (Zhang, Xu, & Xue, 2017)
used sentiment analysis and transaction data to predict
market trends for stock market customers.
Ren, Wu and Liu (2018) used SVM with sentiment
analysis to predict market movements.
Brand management Ghiassi et al. (2013) have used sentiment analysis from
twitter data for brand management employing techniques
such as n-gram analysis and artificial neural networks.
Election results Oliveira, Bermejo and dos Santos (2017) compared results
from sentiment analysis on social media data to traditional
opinion surveys and found it 1 to 8% more accurate for
predicting election results.
Giglietto (2012) used likes on Facebook pages to the
study the predictive power of Facebook to forecast Italian
elections in 2011.
Product Innovation KIA motors and The Royal Bank of Canada, have used
sentiment analysis to innovate new products (Kite, 2011).
Supply Chain Management Singh et al. (2017) presented a framework for improving
supply chain management in food industry using sentiment
analysis.
Swain and Cao (2017) explored the sharing of information
by supply chain members on social media and by using
sentiment analysis gauged its association with supply
chain performance.
Box Office Forecasting Asur and Huberman (2010) presented a study to use data
from Twitter for Box Office forecasting using sentiment
analysis.
Customer Service Bank of America used sentiment analysis to recognize key
issues facing their customers by collecting and analysing
texts from different social media sources (Purcell, 2011).
Malhotra et al. (2012) used sentiment analysis to
implement improved marketing methods using Twitter.
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Social Media Analytics in Supply Chain
Getting accurate information from extremely noisy data such as social media data, is a big challenge
and as is unifying all social media data and making sense of it, which hinders wide use of social
media analytics. Table 2 lists the major studies which have used social media big data in supply
chain management. In the last few years, there has been a growing interest in utilizing value from
social media data in supply chain management as evident from Table 2. But there is still a lack of
accurate models for supply chain management which utilize social media data. One of the reason is
that with extremely noisy sources such a social media getting the external casual factors right is a
big challenge. Making sense of all the casual data (particularly social media) poses a big question
for supply chain practitioners and software developers and requires further research (Syntetos et
al.,2016). The framework proposed in this paper tries to address this issue.

The authors have developed a framework for extracting maximum benefits out of social media in terms
of product forecasting. Three main dimensions were identified from the literature and experimentation
through which social media data can be used to create value in demand forecasting which are sentiment
analysis, word analysis and topic modelling. The framework utilizes these dimension for using social
media analytics to improve demand forecasting. The framework consists of data collection and
preprocessing, sentiment extraction and building of forecasting model as shown in Figure 1.
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Data is collected and preprocessed using following methods in the given order.
Table 2. Use of social media analytics in supply chain
Research Topic Previous work with description Used Feature
Supply Chain Forecasting (Chong, Li, Ngai, Ch’ng, & Lee, 2016) conducted a study
using neural network and sentiment analysis to see effect of
online user generated contents on product sales.
Three-layered neural
network
Sentiment Analysis
Choi (2016) analytically explored the impact of positive
sentiment on social media on market demand of fashion
retailers.
Word Analysis
Beheshti-Kashi (2015) explored whether microblogging
websites such as Twitter can be used for predicting fashion
trends.
Trend Analysis
Boldt et al., (2016) tested utilization of Facebook data for
predicting sales of Nike Products and the effects of events on
activity on Nike’s Facebook pages.
Event Study
Supply Chain
Management
Chae (2015) developed a framework to study usefulness of
twitter information in supply chain management.
Descriptive Analytics
Content Analytics
Network Analytics
Sianipar and Yudoko (2014) concluded in their work that
social media integration with a supply chain can be helpful
to improve collaboration among supply chains and to
increase the agile response of a supply chain.
Content Analysis
Singh et al. (2017) presented a framework for improving
supply chain management in food industry using sentiment
analysis
Sentiment Analysis
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Keywords Identification
The first step is to identify the initial keywords to be provided by the user. Keywords are used to
harvest public data from Facebook and Twitter which are selected after input from the user. N-gram
is then applied.
Figure 1. Overview of the demand forecasting framework using social media big data
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API Streaming
The process of getting data from Twitter and Facebook is the next step and it starts authentication
from Twitter and Facebook APIs and establishing a connection. After the authentication, data can
be captured using different platforms such as R and Python.
Data Cleaning
The Twitter and Facebook data extracted contains a lot of details (tweets, posts, number of comments,
coordinates, embedded URLs, hashtags, retweet count, number of follower, username, location). This
data is then transformed using data parsing, data cleansing and noise cancellation to get only relevant
data for analysis. All those SMDs (Social Media datasets) collected from Facebook and Twitter are to
be neglected which contained less than three words as they didn’t represent the customer comments
in focus. SMDs from users with 2000 plus posts or tweets are also discarded. If a user is tweeting
or posting on the same subject with high frequency those will also be discarded to prevent bias as
the results which include these are skewed by the company’s marketing campaign. Beheshti-Kashi,
Karimi, Thoben, Lütjen, & Teucke (2015) had similar results in their study when they found URLs
linked of such tweets and posts to eBay shops. In the final step of data cleansing, the pre-processing
of the collected data is done which is mainly cleaning the data. This includes removing URL links,
symbols, punctuation and spaces to transform cases.
Word Analysis
Word analysis of social media data encompasses term frequency analysis, word cloud formation and
clustering (Chae, 2015). Term frequency is used to identify key words and phrases from the dataset
by use of algorithms such as n-gram. In the proposed framework, n-grams that occur with frequency
above the selected threshold are selected. This step involves identifying keywords for the products
using word analysis. It is then later compared to quantitative result from the sentimental analysis
obtained by rating positive and negative words being used. Bounding Boxes and restricting region
approach is used which helps in extracting more useful data from the API (Singh et al., 2017). Specific
keywords and exact regions are used to make sure of the accuracy of the data.
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In the second major part of the framwork topic modelling is performed to form different groups of
text extraced from Facebook and Twitter in terms of product type, colour and brand.
Topic Modelling
LDA is used in the proposed framework to identify topics related to a product and then perform
sentiment analysis on the groups. It is described as an unsupervised model which is used to find possible
topics from text collections (Blei et al., 2003). LDA is applied using R and the library ‘topicmodels’.
Sentiment Analysis
Liu (2012) provides an English Lexicon of about 6800 words which has been amended and used for
the purpose of Sentiment Analysis . NB method (Yu et al., 2013) is used for polarity classification
with the aim of obtaining a sentiment index for each SMD. Three categories of sentiment are positive,
negative and neutral. The value of Wtk is calculated using the NB and SVM method. ‘R’ is the
software used in this study. NB is applied using ‘E1071’ library in R and SVM using ‘caret’ package
in R. ‘Caret’ package has in built algorithms for different machine learning algorithms including
decision tree, K-Nearest Neighbours(KNN) and SVM. In this instance, the authors are using only
SVM from caret package.
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The sentiment index in time period t, Wt, is calculated by W W c
t
h
tk
 
( ) where value of
‘c’ is from 1 to -1 depending on the category of W
tk
i.e. sentiment value of the SMD(positive,
negative, neutral) and h is the number of SMDs.

In this framework, the Bass Emotion Model (Fan et al., 2017) is extended to include sentiment analysis
results from SMDs collected in the first step. In the Bass model (Bass, 2004), potential buyers are
classified as innovators and imitators, and then the general form of the Bass model is as follows.
S t m e
q
pe
p q t
p q t
 
 
 
 
 
 
1
1
where S(t) is the cumulative sales by the end of time period t. p refers to the coefficient of
innovation, q refers to the coefficient of imitation, and m refers to the total number of potential
adopters. m and p are calculated using historical sales data. q is related to the sentiment and can be
perceived as a function of the social media sentiment q f Wt
 
. From the SMDs, if positive
sentiment is obtained it means that social media users are talking positively about the product and it
gives a potential increase in adopters q and vice versa. The function is described as
qq q
q q q e
m
mWt
 
 
0
0 0
where q denotes the effect of word of mouth via social media. q0 refers to the minimum of q, qm
refers to the maximum of q. ϒ is a constant that represents the slope of the sales curve. ϒ is calculated
using historical product data.

The study was conducted at an apparel retail company. Focal company’s business model is buying
and selling apparel products. The suppliers are from different countries encapsulating Far East,
South Asia and Europe. Clothes are imported from these countries as well as bought from the local
market and then sold to more than 60 countries throughout the world. The complete supply chain is
huge spanning four continents. The focal apparel retail company was chosen because of importance
of customer-oriented content in apparel industry and because of the focal company’s significant
presence on social media.
It is difficult to coordinate longer apparel supply chains, so it becomes really important to have
very accurate demand forecasting (Syntetos et al., 2016). Traditional forecasting methods like time
series data don’t work particularly will in an apparel industry as designs and items of one season are
typically replaced next season by new collections and trends, and therefore, companies often face a
lack of historical sales data (Thomassey, 2010). Moreover, demand in the industry is significantly
influenced by additional factors such as the economic situation, events or changing weather conditions
(Thomassey, 2014). Many practitioners have been using univariate method (Au et al., 2008) for supply
chain forecasting in apparel industry which utilizes historical sales data and it is assumed that the
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underlying variation of data is constant. For instance, Wong and Guo (2010) utilized one-step-ahead
sales data to predict the sales of medium-priced fashion products in Mainland China. Au et al. (2008)
used previous time series data to predict the sales of T-shirt and jeans from several shops with the
use of neural networks. The sales of products in apparel industry are volatile, often influenced by
changing trends and weather conditions and events. So, for the forecasting purposes, it is not right
to hypothesize that the trend of time series sales data is unchanged. To cope with this, researchers
integrate other influencing factors as the inputs of forecasting models besides the historical time
series data, which is known as multivariate forecasting. Beheshti-Kashi (2015) has presented current
fashion forecasting approaches in the industry and academia. Most successful techniques surveyed
were Extreme machine learning(Sun, Choi, Au, & Yu, 2008), evolutionary neural network (ENN) (Au
et al., 2008; Wong & Guo, 2010), Thomassey and Happiette fuzzy inference systems (Thomassey,
Happiette, & Castelain, 2005) and hybrid intelligent sales forecasting model (Aburto & Weber, 2007).
Most of the forecasting models discussed above give reliable results for middle and long-term
forecasting. But due to a very competitive market and short selling span accurate and customer centric
and short-term forecasting is necessary. With the advent of information technology and affordable
information systems, most companies (big and small) have developed or implemented information
systems from which they get sales reports, graphs and even forecasts. With the advent of social
media data, this is not enough to be competitive. Data gathered by the companies needs to add the
information circulating on social media, which could deliver another type of insight for forecasting
and result in the increased competitiveness especially for creative industry such as apparel industry
with the involvement of potential customers in style design, colour preference and judging trends,
and scope for new products (Banica & Hagiu, 2016).
Short term forecasting methods have not been explored as much (N. Liu, Ren, Choi, Hui, & Ng,
2013). Short term forecasting is very important in the apparel industry because of the ever-changing
trends and short selling times. For this purpose, Beheshti (2015) suggested adding social media to
the discussion of fashion forecasting and Syntetos et al. (2016) predicted that future of supply chain
forecasting will include predictive analytics based on social media data. For an apparel supply chain,
there can be multiple topics of interest which are being discussed in social media. The authors try to
utilize these topics to make this data viable using the proposed framework for supply chain forecasting
in apparel industry.
For the implementation of the framework, company sales and social media data i.e. Twitter and
Facebook data was collected. This data was collected for a period of six weeks. Data collection for
this study began in July 2016 and data was collected till August 15, 2016. Beheshti-Kashi (2015) did a
study for exploration of trends using twitter and found out it hard to present the finding in quantitative
form. To cater for this issue, the authors expanded the study by analysing specificities and increased
the amount of data collection by including both Facebook and Twitter so results could be presented
in quantitative form. The period of six weeks was chosen with the insights from the user, which in
this case is the supply chain manager of the focal company. ‘Shorts’ were selected as the product to
be used for the study. For collection of data from social media i.e. Twitter and Facebook, APIs were
used and the related SMDs was analysed. Only those SMDs were selected which were either brand
related, product type related, or a fashion trend related. Data was collected every 7 days as twitter
allowed tweets to be collected which were 7-8 days old. SMDs were extracted for brand and products.
Hashtags and texts for the brands sold by the focal company were analysed. The total number of
tweets analysed were 1,208,650. For the category product type shorts were chosen as they were the
most selling item as the data was collected in summers. SMDs were collected against different type
of shorts as shown in Table 3 and for different brands as shown in Table 4. As this data of brands was
analysed there were a lot of data which wasn’t related to the brand or products of the focal company.
One such example was #next being used for election campaign in United States. After extraction of
text, it was used to form word clouds which can be helpful in manual inspection of the data gathered
as the viewer can get a general idea about the kind of words being used and this can later be used for
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cross checking the results obtained by sentimental analysis to make sure no anomaly has occurred
during the process. Word Clouds were formed before and after processing and cleaning of data to
investigate manually the dataset being used for sentiment extraction. Figure 2 displays a word cloud
for keyword ‘nike’ before data cleaning process. The noise in this dataset is evident as there are words
from different languages and some completely unrelated words. Figure 3 displays the word cloud
after data cleaning which removes all the unrelated SMDs.
For a period of 6 weeks, the SMDs were analysed and then compared to the sales period for
that period as well as next 6 weeks. Table 5 shows the sentiment analysis score for different product
categories after application of SVM and then calculation of parameter q. Analysis of sentiment score
show that the amount of sales had a co relation with the sentiment around that particular brand or
colour. There was no co relation found when sentiment analysis was done for the product type which
could be attributed to the noise in the data as single word or single product search was susceptible to
much more noise than a search using words for multiple characteristics. Multiple character searches
with positive sentiment lead to an increase in sale and the negative sentiment lead to a decrease.
Analysing the tweets and Facebook comments for running shorts and running a sentiment analysis on
it using SVM and NB methods. Comparison of the results of these models have been shown in Table 7.
Figure 2. Word cloud for brand ‘Nike’
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The results from sentiment analysis were then used in Bass Emotion model to predict the sales.
The parameters m,p and γ for Bass- Emotion model were calculated using historical sales data and q
was calculated using sentiment analysis from SMDs. Parameters calculated are represented in Table 8.
All these parameters were calculated using R. Table 6 shows the forecasting accuracy of the proposed
emotion enhanced model which is a significant improvement on the forecasting accuracy of original
Bass Model. Figure 4 displays the forecasted values using proposed model compared to actual values.

This paper introduced a framework that provides a way of utilizing social media big data in Bass-
Emotion Model for demand forecasting using results from sentiment analysis on Facebook and
Twitter data. As social media data is very noisy, it is difficult to make accurate predictions from social
media data about products in general but if the products are broken down and multiple characteristics
search is applied then the information which is collected can be converted as a demand forecasting
and market or trend sensing tool. The major factor in extracting value from the social media is to
apply multiple data cleaning techniques in conjunction with one another, so the data subjected to
Figure 3. Word cloud after data cleaning
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later analysis gives reliable results as described in the framework presented in the paper. More than
1200,000 tweets, posts and comments from Facebook and Twitter were analysed in the case study.
The study showed that social media big data is extremely useful for apparel industry and can be very
effective if used to support demand forecasting. With proper modelling and implementation of right
techniques, social media big data has the potential to help forecast with accuracy. Results from this
study shows a co relation between customers opinion on Facebook and Twitter to actual sales. The
framework presented in this study can be further verified and improved with the help of case studies
to make it a reliable mechanism for using social media big data in demand forecasting.
As this a relatively new research area, there is a considerable need for enhancing our understanding
social media data in supply chain contexts. One area which needs urgent work, is developing detailed,
Table 3. Keywords used for SMDs extraction for ‘shorts’
Shorts#nike Shorts#green Shorts#swimming zara#swimmingshorts
Shorts#adidas Shorts#navy Shorts#running zara#runningshorts
Shorts#reebok Shorts #jersey nike#jerseyshorts zarablack#jerseyshort
Shorts#next Shorts #cargo nike #cargoshorts zarablack#cargoshorts
Shorts#blue Shorts#jorts nike #jorts zarablack#jorts
Shorts#black Shorts#fleece nike #fleeceshorts zarablack#fleeceshort
Shorts#grey Shorts#gym nike #gymshorts zarablack#gymshorts
Shorts#swimming nike#swimmingshort Shorts#swimming adidas#swimmingshor
Shorts#running nike#runningshorts Shorts#running puma#runningshorts
nike#jerseyshorts nikeblack#jerseyshor adidas#jerseyshorts nikeblack#jerseyshort
nike#cargoshorts nextblack#cargoshor adidas#cargoshorts pumablack#cargoshts
nike #jorts nike black#jorts adidas #jorts nike black#jorts
nike #fleeceshorts nikeblack#fleeceshor adidas#fleeceshorts nikeblack#fleeceshort
adidasShorts#ru nike#runningshorts adidasShorts#runni puma#runningshorts
next#jerseyshorts nikeblack#jerseyshorts adidas#jerseyshorts pumablack#jerseyshorts
next #cargoshorts nextblack#cargoshors adidas#cargoshorts pumblack#cargoshorts
next #jorts nike black#jorts adidas #jorts puma black#jorts
next #fleeceshorts nikeblack#fleeceshorts adidas#fleeceshorts pumablack#fleeceshorts
next #gymshorts nikeblack#gymshorts adidas #gymshorts pumablack#gymshorts
Table 4. Number of Brands and Product Related SMDs for week 1
Brand # of SMDs Product Type # of SMDs
Zara 12,456 #jerseyshorts 651
Nike 29,435 #cargoshorts 543
Adidas 36,792 #jorts 189
NEXT 71,234 #gymshorts 984
BHS 61,281 #swimmingshorts 429
Puma 23,124 #runningshorts 183
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Table 5. Product type with sentiment analysis score
Product Type Sales Number of
SMDs
Sentiment
Analysis Score
Product Type Sales Number of
SMDs
Sentiment
Analysis Score
Nike Jersey
Shorts
1120 651 0.23 Adidas Jersey
Shorts
983 156 0.64
Nike Cargo
Shorts
2832 543 0.12 Adidas Cargo
Shorts
811 531 0.12
Nike Denim
Shorts
563 189 0.70 Adidas Denim
Shorts
641 145 0.53
Nike Fleece
Shorts
212 84 0.34 Adidas Fleece
Shorts
1212 821 0.31
Nike Gym
Shorts
984 984 0.05 Adidas Gym
Shorts
1944 547 0.43
Nike
Swimming
Shorts
1367 429 0.76 Adidas
Swimming
Shorts
937 122 0.53
Table 6. Comparison of forecasted and actual values for Bass Model and proposed Emotion Enhanced Model
Forecasting week 1 2 3 4 5 6
Actual value 712.3409 817.6867 921.2260 843.5641 926.7657 923.9208
Forecasted value (Bass Model) 704.5435 810.4631 927.0904 841.5382 922.7238 918.6123
Forecasted value (Proposed Model) 708.6674 816.5294 923.1996 844.2350 926.8046 922.7927
Table 7. Comparison of SVM and NB Methods
Product Brand Algorithm Accuracy
Nike NB 67.21
SVM 69.24
Adidas NB 67.46
SVM 75.12
Puma NB 65.24
SVM 71.81
BHS NB 69.42
SVM 78.10
Next NB 63.41
SVM 63.51
Zara NB 75.87
SVM 75.11
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practical guidelines, which can help companies in designing industry applications, using Facebook,
Twitter and other social media platforms, for diverse supply chain activities, including new product
development, stake holder engagement, supply chain risk management, and market sensing. Further
research is needed in the implementation of this framework on other industries and using cloud-
based systems. Moreover, sentiment extraction could be improved by including other social media
platforms including YouTube, google trends and Instagram. Sentiment analysis can be implemented
on videos and pictures posted instead of limiting it only to the text. This could further improve the
results as it will take into consideration users from other platforms as well, painting a more accurate
picture of customers sentiment.
Table 8. Parameter for bass model
Parameter Results
m 887.0306
p 0.023777
q00.090407
qm0.093113
γ 0.170784
Figure 4. Results of Forecasting Model of Emotion Enhanced Model
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Rehan Iftikhar is a Marie-Curie Research Fellow and a 2nd year PhD student at School of Business, Maynooth
University. He holds a Master’s degree in Engineering Management from University of Exeter. His current research
interests include digital retail, information systems and big data. His work has appeared in various journals and
conference proceedings including Journal of Global Information Management, British Food Journal, Academy of
Management Global Proceedings and International Conference on Information Systems Development. Rehan is
the corresponding author and can be contacted at: rehan.iftikhar@mu.ie
Mohammad Saud Khan, PhD, is a Senior Lecturer in the area of Strategic Innovation and Entrepreneurship at
Victoria University of Wellington, New Zealand. Before taking up this role, he was positioned as a Postdoctoral
Researcher at the University of Southern Denmark. Having a background in Mechatronics (Robotics & Automation)
Engineering, he has worked as a field engineer in the oil and gas industry with Schlumberger Oilfield Services in
Bahrain, Saudi Arabia, and the United Kingdom. His current research interests include innovation management
(especially the implications of big data and 3D printing), technology, and social media entrepreneurship.
Wong, W. K., & Guo, Z. X. (2010). A hybrid intelligent model for medium-term sales forecasting in fashion
retail supply chains using extreme learning machine and harmony search algorithm. International Journal of
Production Economics. doi:10.1016/j.ijpe.2010.07.008
Wu, Y., Wei, F., Liu, S., Au, N., Cui, W., Zhou, H., & Qu, H. (2010). OpinionSeer: Interactive visualization of hotel
customer feedback. IEEE Transactions on Visualization and Computer Graphics. doi:10.1109/TVCG.2010.183
Yu, Y., Duan, W., & Cao, Q. (2013). The impact of social and conventional media on firm equity value: A
sentiment analysis approach. Decision Support Systems. doi:10.1016/j.dss.2012.12.028
Zeng, D., Chen, H. C. H., Lusch, R., & Li, S.-H. (2010). Social Media Analytics and Intelligence. IEEE
Intelligent Systems.
Zhang, G., Xu, L., & Xue, Y. (2017). Model and forecast stock market behavior integrating investor sentiment
analysis and transaction data. Cluster Computing. doi:10.1007/s10586-017-0803-x
Zikopoulos, P., & Eaton, C. (2011). Understanding big data: Analytics for enterprise class hadoop and streaming
data. McGraw-Hill Osborne Media.
... Iftikhar and Khan [82] Improve demand forecasting in a supply chain using social media data from Twitter and Facebook. ...
... Other studies combined descriptive analytics and predictive analytics. Iftikhar and Khan [82] developed descriptive and predictive analytics for sentiment analysis and demand forecasting and demonstrated that the forecasting accuracy of their proposed emotionenhanced model was significantly improved on the forecasting accuracy of the original Bass Model. Singh et al. [83] used social media data analytics, support vector machine (SVM), and hierarchical clustering with multiscale bootstrap resampling to identify root causes affecting consumer satisfaction in various segments in the food industry supply chains and demonstrated that the performance of the SVM is better than that of the Naive Bayes (NB) classifier in terms of sentiment classification. ...
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Big data analytics has been successfully used for various business functions, such as accounting, marketing, supply chain, and operations. Currently, along with the recent development in machine learning and computing infrastructure, big data analytics in the supply chain are surging in importance. In light of the great interest and evolving nature of big data analytics in supply chains, this study conducts a systematic review of existing studies in big data analytics. This study presents a framework of a systematic literature review from interdisciplinary perspectives. From the organizational perspective, this study examines the theoretical foundations and research models that explain the sustainability and performances achieved through the use of big data analytics. Then, from the technical perspective, this study analyzes types of big data analytics, techniques, algorithms, and features developed for enhanced supply chain functions. Finally, this study identifies the research gap and suggests future research directions.
... Innovative activities involve a process from the identification of new business ideas to the development of new products/services for markets, which requires a firm to understand changes in business environments and customer requirements Chen et al., 2020;Ensslin et al., 2020;Ge et al., 2020;Huang et al., 2019;Iftikhar & Khan, 2020;Lee & Chen, 2019;Li et al., 2019;Nisha et al., 2019;Yin et al., 2019;Zheng, Xiong et al., 2021). The literature has identified a dual nature of innovation activities as comprising innovation generation (products are defined as new to the world) and innovation adoption (products are considered new to the market and firm) Damanpour & Wischnevsky, 2006;Pérez-Luño et al., 2011;Zheng, Fan et al., 2021). ...
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This paper investigates how big data analytics influences the innovative activities of micro, small, and medium enterprises (MSMEs) on the basis of the resource-based and dynamic capabilities views. Studying a survey sample collected from 186 entrepreneurial and MSMEs, we discovered a crucial mediating mechanism in the relationship between them: market agility. Our findings suggest that the use of big data analytics enables MSMEs to enhance their market agility, thereby strengthening their innovation adoption. In addition, an MSME’s strong dynamic capability helps boost the effects of market agility on innovation adoption. While early success traps strengthen the effects of market agility on innovation adoption, they weaken the effects on innovation generation. Our study enriches the existing literature on big data analytics and innovation and provides practical guidance for MSMEs to engage in entrepreneurial innovation through big data analytics.
... Islam et al. (2019) analyzed the effects of online recommendations from anonymous customers. The contemporary omnichannel assortment methods have been adopted by Bijmolt et al. (2018) that captured the consumers' movement between touchpoints and incorporated the change in consumers' behavior due to the advent of digital and social media communication channels (Iftikhar & Khan, 2020). In a multichannel strategy (Choi & Kim, 2020;Klaus & Nguyen, 2013;Ozuem et al., 2017), the firm interacts with different consumers through multiple channels visiting different touchpoints. ...
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Global retail industry players have witnessed a grave scenario due to the impact of the COVID-19 pandemic. The pandemic has changed the way shoppers think, manifested in the decelerating footfall and increasing threat for traditional brick and mortar stores. The strategy of switching over to omnichannel seemed to have provided the needed relief to traditional retailers and manufacturers in the consumer goods industry. However, a robust omnichannel product assortment model requires integrating channels and remodeling managers’ roles to provide consumer experience and satisfaction and maximize profitability across all touchpoints with minor disruptions. The paper formulates and simulates an omnichannel data-driven fulfillment analytical model to analyze customers’ product mix and manage assortment accordingly. Further, an optimization model that maximizes revenue and profitability is formulated as a suggestive framework with strategies for the current scenario. The paper is helpful for marketing researchers and retail planners for omnichannel assortment management.
... The use of information and data systems enhances the coordination among the employees of the organization, and social media data are useful for demand forecasting as well (Iftikhar & Khan, 2020;Zulkefly et al., 2021). The use of technologies increases the efficiency of the workers within the organization (Kim & Miranda, 2018). ...
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The study draws upon the ethical theory of organizing to elucidate the links between ethical climate antecedents, organizational practices, and consequences. We also integrated organizing vision theory to examine the influence of diffusion of big data analytics innovation on sustainable business practices. The results indicate that organizational trust has a significant positive impact on ethics training and ethics audit, which is critical to South Africa's mining industry. Furthermore, the results indicate that ethical leadership is positively related to ethics training and ethics audits. Findings show that ethics training and ethics audit is positively related to sustainable business practices. Findings indicate that the vision constructed by community members is positively related to the diffusion of big data analytics innovation. We also found that the diffusion of big data analytics innovation is positively related to sustainable business practices. Lastly, findings show that sustainable business practices are positively related to firm performance.
... Combined with bibliometric techniques, including citation and co-citation analyses, we can further explore the information exchanged between documents to effectively reveal knowledge spreading patterns and establish the intellectual structure of a particular discipline. Compared with existing studies (Iftikhar and Khan 2020;Nam and Kabutey 2021), the concept of the social network is further extended in the review methodology. Similar to identifying key social actors in the network, the SNA can be extended to examine the key research studies in a domain-specific publication network. ...
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Cognition and emotion play important roles in information systems (IS) research, yet existing studies have not provided a comprehensive picture of these issues in the IS field. In this study, a citation network including 2,061 related academic articles published between 1996 and 2019 is established. Two novel indicators are proposed, through which 57 influential articles are identified, namely annual average degree centrality (AADC) and annual average betweenness centrality (AABC). A backward search process is performed preceding the co-citation analysis to exhaustively collect co-citation data. Finally, integrating multidimensional scaling analysis with clustering analysis, six core knowledge groups are revealed.
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Purpose The aim of this paper is to study and describe the Industry 4.0 (I4.0) concept in the retail industry by identifying the technology enablers, the benefits and the challenges involved in adopting these technologies. Design/methodology/approach The work is built on a systematic literature review (SLR) about the I4.0 concept within retail studies through the enablers codified by the Industry 4.0 literature. Findings The concept of Industry 4.0 within the retail context is presented through three relevant technology enablers I4.0 emerges as a latent concept represented by technologies adopted to study customer behaviour and manage customer relationships and, partially, to optimise retail management. Furthermore, the paper highlights the benefits and challenges stemming from the adoption of I4.0 technology enablers in the retail industry. Originality/value The use of a SLR to describe the I4.0 concept in the retail industry identifying the relevant technology enablers and their relations. The article lays the foundations for future studies on the integrated effects of the contemporary actions of multiple technologies on retail management.
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The present research aimed to provide a mechanism for enhancing sustainable supply chain survivability (SSCS) during and after the COVID-19 outbreak. Logistical and manufacturing operations have been restricted due to the severe lockdown, which significantly impacts the demand and supply of various items. COVID-19 has a massive effect on a multitude of units of various sectors. This research emphasized the important elements that must be considered to adapt the sustainable supply chain (SSC) practices. As a result, businesses are now attempting to improve the system for SSC. The stepwise weight assessment ratio analysis (SWARA) approach is used in this research to classify dynamic aspects for improving SSC survival in an epidemic condition. The results reveal that the viability of the supply chain network is the essential criteria for managing the relationships of suppliers and buyers and maximizing SSCS during and after COVID-19. This research will help businesses, manufacturers, and other stakeholders to concentrate on the described factors to achieve a brighter environment.
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